Σάββατο 23 Μαρτίου 2019

Roentgenology

Editorial

AJR Reviewers: Heartfelt Thanks From the Editors and Staff

Thomas H. Berquist1
Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 715-716. 10.2214/AJR.19.21180

References

1. Berquist TH. AJR reviewers: thank you from the editors and staff. AJR 2018; 210:237–238 [Google Scholar]
2. Berquist TH. New AJR initiatives in 2017. AJR 2017; 208:231–232 [Google Scholar]
Full Text | PDF (501 KB) | PDF Plus (504 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 717-726
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.20517) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Special Articles

Original Research

Detection of Hyperacute Reactions of Desacetylvinblastine Monohydrazide in a Xenograft Model Using Intravoxel Incoherent Motion DWI and R2* Mapping

Jianye Liang1, Rong Ma2, Heru Chen3 ... Show all
Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 717-726. 10.2214/AJR.18.20517

ABSTRACT :

OBJECTIVE. This study aimed to investigate the feasibility of intravoxel incoherent motion (IVIM) DWI and R2* (transverse relaxation rate) mapping to monitor the hyperacute therapeutic efficacy of desacetylvinblastine monohydrazide (DAVLBH) on an experimental hepatocellular carcinoma mouse model within 24 hours.

MATERIALS AND METHODS. Forty-four mice were implanted with hepatocellular carcinoma and divided into three random groups. A treatment group and a control group underwent IVIM-DWI and R2* mapping examinations before and after a single injection of DAVLBH or saline at 1, 2, 4, and 24 hours. The pathology group was set for pathologic analysis, including H and E staining and CD31 and hypoxia-inducible factor (HIF)–1α immunohistochemical staining.

RESULTS. DAVLBH caused hyperacute disruptions on the tumor capillaries in the treatment group. Water molecule diffusion (D), microcirculation perfusion (D*), and perfusion fraction (f) decreased initially but then gradually recovered to the baseline level by 24 hours after the first injection of DAVLBH. In contrast, R2* increased dramatically at 1 hour and then gradually decreased from 1 hour to 24 hours after treatment. D*, f, and D showed similar trends and were positively correlated with CD31 expression (r = 0.868, 0.721, and 0.730, respectively), but were negatively correlated with HIF-1α expression (r = −0.784, −0.737, and −0.673, respectively). R2* showed a negative correlation with CD31 expression (r = −0.823) and a positive correlation with HIF-1α expression (r = 0.791).

CONCLUSION. Both IVIM-DWI and R2* mapping can adequately detect the vascular-disrupting effect of DAVLBH as early as 1 hour after injection in a mouse xenograft model. Moreover, D* and R2* are the two most sensitive hemodynamic parameters and can monitor the hyperacute changes associated with DAVLBH treatment in vivo.

Keywords: desacetylvinblastine monohydrazide, intravoxel incoherent motion DWI, perfusion, R2* mapping, vascular-disrupting agent

Supported by grant 2017A020215065 from the Science and Technology Planning Project of Guangdong Province and grant 21317241 from the National Natural Science Foundation of China.

References
Previous section
1. De Palma M, Biziato D, Petrova TV. Microenvironmental regulation of tumour angiogenesis. Nat Rev Cancer 2017; 17:457–474 [Google Scholar]
2. Siemann DW. The unique characteristics of tumor vasculature and preclinical evidence for its selective disruption by tumor-vascular disrupting agents. Cancer Treat Rev 2011; 37:63–74 [Google Scholar]
3. Siemann DW, Bibby MC, Dark GG, et al. Differentiation and definition of vascular-targeted therapies. Clin Cancer Res 2005; 11:416–420 [Google Scholar]
4. Salmon BA, Salmon HW, Siemann DW. Monitoring the treatment efficacy of the vascular disrupting agent CA4P. Eur J Cancer 2007; 43:1622–1629 [Google Scholar]
5. Baguley BC. Preclinical efficacy of vascular disrupting agents in non-small-cell lung cancer. Clin Lung Cancer 2011; 12:81–86 [Google Scholar]
6. Zweifel M, Jayson GC, Reed NS, et al. Phase II trial of combretastatin A4 phosphate, carboplatin, and paclitaxel in patients with platinum-resistant ovarian cancer. Ann Oncol 2011; 22:2036–2041 [Google Scholar]
7. Chen M, Lei X, Shi C, et al. Pericyte-targeting pro-drug overcomes tumor resistance to vascular disrupting agents. J Clin Invest 2017; 127:3689–3701 [Google Scholar]
8. Lei X, Chen M, Huang M, et al. Desacetylvinblastine monohydrazide disrupts tumor vessels by promoting VE-cadherin internalization. Theranostics 2018; 8:384–398 [Google Scholar]
9. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168:497–505 [Google Scholar]
10. Shi C, Liu D, Xiao Z, et al. Monitoring tumor response to antivascular therapy using non-contrast intravoxel incoherent motion diffusion-weighted MRI. Cancer Res 2017; 77:3491–3501 [Google Scholar]
11. Pruijm M, Milani B, Burnier M. Blood oxygenation level-dependent MRI to assess renal oxygenation in renal diseases: progresses and challenges. Front Physiol 2017; 7:667 [Google Scholar]
12. Cho GY, Moy L, Kim SG, et al. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol 2016; 26:2547–2558 [Google Scholar]
13. Robinson SP, Rodrigues LM, Howe FA, Stubbs M, Griffiths JR. Effects of different levels of hypercapnic hyperoxia on tumour R2* and arterial blood gases. Magn Reson Imaging 2001; 19:161–166 [Google Scholar]
14. Li F, Lee KE, Simon MC. Detection of hypoxia and HIF in paraffin-embedded tumor tissues. Methods Mol Biol 2018; 1742:277–282 [Google Scholar]
15. McKeage MJ, Fong P, Jeffery M, et al. 5,6-Dimethylxanthenone-4-acetic acid in the treatment of refractory tumors: a phase I safety study of a vascular disrupting agent. Clin Cancer Res 2006; 12:1776–1784 [Google Scholar]
16. Perini R, Choe R, Yodh AG, Sehgal C, Divgi CR, Rosen MA. Non-invasive assessment of tumor neovasculature: techniques and clinical applications. Cancer Metastasis Rev 2008; 27:615–630 [Google Scholar]
17. Li JL, Ye WT, Liu ZY, et al. Comparison of micro-vascular perfusion evaluation among IVIM-DWI, CT perfusion imaging and histological microvessel density in rabbit liver VX2 tumors. Magn Reson Imaging 2018; 46:64–69 [Google Scholar]
18. Joo I, Lee JM, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging for monitoring the therapeutic efficacy of the vascular disrupting agent CKD-516 in rabbit VX2 liver tumors. Radiology 2014; 272:417–426 [Google Scholar]
19. Hoekstra LT, van Lienden KP, Verheij J, van der Loos CM, Heger M, van Gulik TM. Enhanced tumor growth after portal vein embolization in a rabbit tumor model. J Surg Res 2013; 180:89–96 [Google Scholar]
20. Nakabayashi H, Taketa K, Miyano K, Yamane T, Sato J. Growth of human hepatoma cells lines with differentiated functions in chemically defined medium. Cancer Res 1982; 42:3858–3863 [Google Scholar]
21. Cui Y, Zhang C, Li X, et al. Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for monitoring the early response to ZD6474 from nasopharyngeal carcinoma in nude mouse. Sci Rep 2015; 5:16389 [Google Scholar]
22. Wu G, Liu G, Kong W, et al. Assessment of response to anti-angiogenic targeted therapy in pulmonary metastatic renal cell carcinoma: R2* value as a predictive biomarker. Eur Radiol 2017; 27:3574–3582 [Google Scholar]
23. Robinson SP, Kalber TL, Howe FA, et al. Acute tumor response to ZD6126 assessed by intrinsic susceptibility magnetic resonance imaging. Neoplasia 2005; 7:466–474 [Google Scholar]
24. McPhail LD, Griffiths JR, Robinson SP. Assessment of tumor response to the vascular disrupting agents 5,6-dimethylxanthenone-4-acetic acid or combretastatin-A4-phosphate by intrinsic susceptibility magnetic resonance imaging. Int J Radiat Oncol Biol Phys 2007; 69:1238–1245 [Google Scholar]
25. Cyran CC, Sennino B, Chaopathomkul B, et al. Magnetic resonance imaging assays for dimethyl sulfoxide effect on cancer vasculature. Invest Radiol 2008; 43:298–305 [Google Scholar]
26. Kim KW, Lee JM, Jeon YS, et al. Vascular disrupting effect of CKD-516: preclinical study using DCE-MRI. Invest New Drugs 2013; 31:1097–1106 [Google Scholar]
Address correspondence to L. Luo ().

J. Liang and R. Ma contributed equally to this work.

Full Text | PDF (1674 KB) | PDF Plus (1283 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 727-733
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20195) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

FOCUS ON: Medical Physics and Informatics

Original Research

Evaluation of a Ferromagnetic Marker Technology for Intraoperative Localization of Nonpalpable Breast Lesions

Share Share Claim CREDIT
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 727-733. 10.2214/AJR.18.20195

ABSTRACT :

OBJECTIVE. The purpose of this study was to evaluate the magnetic occult lesion localization instrument (MOLLI) system that involves implantation of a small, ferromagnetic marker to guide surgical excision of nonpalpable breast lesions. Characterization of the system was undertaken as part of what is, to our knowledge, the first study to assess the MOLLI system.

MATERIALS AND METHODS. The MOLLI system consists of a handheld probe that can detect the position and distance of an implanted magnetic marker. The system presents the surgeon with an accurate assessment of lesion location and depth measurement for precise 3D localization. The marker is implanted under ultrasound or mammographic guidance at any time before the surgical procedure and requires no special precautions. Experimental analysis focused on characterization of the following aspects of the MOLLI system: visualization of the marker under imaging, 3D detection of the magnetic marker, spatial resolution of the probe to detect markers placed in close proximity, and the effect of signal interference on system performance.

RESULTS. The MOLLI system can reliably detect mean (± SD) marker depths up to 53 ± 8.56 mm from the probe. Bracketing large lesions or localizing multiple lesions can be accomplished by placing markers as close as 10 mm apart, at depths of up to 42 mm. The biologically inert MOLLI marker is readily visible under ultrasound and mammographic guidance, and it is differentiable from radiologic clips. The effect of surgical instruments on MOLLI functioning is minimal and does not impact system accuracy or reliability.

CONCLUSION. The MOLLI system offers an accurate and efficient alternative lesion localization method for nonpalpable breast lesions.

Keywords: breast-conserving surgery, breast cancer, localization, lumpectomy, nonpalpable lesion

Supported by an internal Association of Fundraising Professionals grant from Sunnybrook Health Sciences Centre and an Innovation Grant from the Canadian Cancer Society Research Institute.

References
Previous section
1. McLaughlin SA, Ochoa-Frongia LM, Patil SM, Cody HS 3rd, Sclafani LM. Influence of frozen-section analysis of sentinal lymph node and lumpectomy margin status on reoperation rates in patients undergoing breast-conserving surgery. J Am Coll Surg 2008; 206:76–82 [Google Scholar]
2. O'Sullivan M, Li T, Freedman G, Morrow M. The effect of multiple reexcisions on the risk of local recurrence after breast conserving surgery. Ann Surg Oncol 2007; 14:3133–3140 [Google Scholar]
3. Cox CE, Furman B, Stowell N, et al. Radioactive seed localization breast biopsy and lumpectomy: can specimen radiographs be eliminated? Ann Surg Oncol 2003; 10:1039–1047 [Google Scholar]
4. Hughes JH, Mason MC, Gray RJ, et al. A multi-site validation trial of radioactive seed localization as an alternative to wire localization. Breast J2008; 14:153–157 [Google Scholar]
5. Atkins J, Mushawah F, Appleton C, et al. Positive margin rates following breast-conserving surgery for stage I-III breast cancer: palpable versus non-palpable tumors. J Surg Res 2012; 177:109–115 [Google Scholar]
6. Sajid MS, Parampalli U, Haider Z, Bonomi R. Comparison of radioguided occult lesion localization (ROLL) and wire localization for non-palpable breast cancers: a meta-analysis. J Surg Oncol 2012; 105:852–858 [Google Scholar]
7. Wilke LG, Czechura T, Wang C. Repeat surgery after breast conservation for the treatment of stage 0 to II breast carcinoma: a report of the National Cancer Data Base. JAMA Surg 2014; 149:1296–1305 [Google Scholar]
8. Bronstein AD, Kilcoyne RF, Moe RE. Complications of needle localization of foreign bodies and nonpalpable breast lesions. Arch Surg 1988; 123:775–779 [Google Scholar]
9. McGhan LJ, McKeever SC, Pockaj BA, et al. Radioactive seed localization for nonpalpable breast lesions: review of 1,000 consecutive procedures at a single institution. Ann Surg Oncol 2011; 18:3096–3101 [Google Scholar]
10. Sharek D, Zuley ML, Zhang JY, Soran A, Ahrendt GM, Ganott MA. Radioactive seed localization verus wire localization for lumpectomies: a comparison of outcomes. AJR 2015; 204:872–877 [Google Scholar]
11. Lovrics PJ, Goldsmith CH, Hodgson N, et al. A multicentered, randomized, controlled trial comparing radioguided seed localization to standard wire localization for nonpalpable, invasive and in situ breast carcinomas. Ann Surg Oncol 2011; 18:3407–3414 [Google Scholar]
12. Lovrics PJ, Cornacchi SD, Vora R, Goldsmith CH, Kahnamoui K. Systematic review of radioguided surgery for non-palpable breast cancer. Eur J Surg Oncol 2011; 37:388–397 [Google Scholar]
13. Klein RL, Mook J, Euhus D, et al. Evaluation of a hydrogel based breast biopsy marker (Hydro-MARK) as an alternative to wire and radioactive seed localization for non-palpable breast lesions. J Surg Oncol 2012; 105:591–594 [Google Scholar]
14. Dauphine C, Reicher JJ, Reicher MA, Gondusky C, Khalkhali I, Kim M. A prospective clinical study to evaluate the safety and performance of wireless localization of nonpalpable breast lesions using radiofrequency identification technology. AJR 2015; 204:[web]W720–W723 [Google Scholar]
15. Ganz RA. A modern magnetic implant for gastroesophageal reflux disease. Clin Gastroenterol Hepatol 2017; 15:1326–1337 [Google Scholar]
16. Nachev P, Rose GE, Verity DH, et al. Magnetic oculomotor prosthetics for acquired nystagmus. Ophthalmology 2017; 124:1556–1564 [Google Scholar]
17. Hu C, Meng M, Mandal M. A linear algorithm for tracing magnet position and orientation by using three-axis magnetic sensors. IEEE Trans Magn 2017; 43:4096–4101 [Google Scholar]
18. Cherry S, Sorenson J, Phelps M. Physics in nuclear medicine. Pennsylvania: Elsevier Science, 2003 [Google Scholar]
19. Cox CE, Garcia-Henriquez N, Glancy MJ, et al. Pilot study of a new nonradioactive surgical guidance technology for locating nonpalpable breast lesions. Ann Surg Oncol 2016; 23:1824–1830 [Google Scholar]
20. Mango V, Ha R, Gomberawalla A, et al. Evaluation of the SAVI SCOUT surgical guidance system for localization and excision of nonpalpable breast lesions: a feasibility study. AJR 2016; 207:[web]W1–W4 [Google Scholar]
21. Hayes MK. Update on preoperative breast localization. Radiol Clin North Am 2017; 55:591–603 [Google Scholar]
22. Jadeja PH, Mango V, Patel S, et al. Utilization of multiple SAVI SCOUT surgical guidance system reflectors in the same breast: a single-institution feasibility study. Breast J 2018; 24:531–534 [Google Scholar]
23. Cox CE, Russell S, Prowler V, et al. A prospective, single arm, multi-site, clinical evaluation of a non-radioactive surgical guidance technology for the location of nonpalpable breast lesions during excision. Ann Surg Oncol 2016; 23:3168–3174 [Google Scholar]
24. Jeffries DO, Dossett LA, Joms JM. Localization of breast surgery: the next generation. Arch Pathol Lab Med 2017; 141:1324–1329 [Google Scholar]
Address correspondence to A. Ravi ().

A. Ravi and J. Dillon are listed as inventors on a provisional patent for several components of the magnetic occult lesion localization instrument (MOLLI) system.

Full Text | PDF (948 KB) | PDF Plus (773 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 734-740
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.19869) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

FOCUS ON: Medical Physics and Informatics

Original Research

Automatic Disease Annotation From Radiology Reports Using Artificial Intelligence Implemented by a Recurrent Neural Network

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 734-740. 10.2214/AJR.18.19869

ABSTRACT :

OBJECTIVE. Radiology reports are rich resources for biomedical researchers. Before utilization of radiology reports, experts must manually review these reports to identify the categories. In fact, automatically categorizing electronic medical record (EMR) text with key annotation is difficult because it has a free-text format. To address these problems, we developed an automated system for disease annotation.

MATERIALS AND METHODS. Reports of musculoskeletal radiography examinations performed from January 1, 2016, through December 31, 2016, were exported from the database of Hanyang University Medical Center. After sentences not written in English and sentences containing typos were excluded, 3032 sentences were included. We built a system that uses a recurrent neural network (RNN) to automatically identify fracture and nonfracture cases as a preliminary study. We trained and tested the system using orthopedic surgeon–classified reports. We evaluated the system for the number of layers in the following two ways: the word error rate of the output sentences and performance as a binary classifier using standard evaluation metrics including accuracy, precision, recall, and F1 score.

RESULTS. The word error rate using Levenshtein distance showed the best performance in the three-layer model at 1.03%. The three-layer model also showed the highest overall performance with the highest precision (0.967), recall (0.967), accuracy (0.982), and F1 score (0.967).

CONCLUSION. Our results indicate that the RNN-based system has the ability to classify important findings in radiology reports with a high F1 score. We expect that our system can be used in cohort construction such as for retrospective studies because it is efficient for analyzing a large amount of data.

Keywords: automatic annotation, deep learning, natural language processing, radiology reports, recurrent neural network

This study was supported by a grant from the National Research Foundation (NRF) of Korea that was funded by the Ministry of Science and ICT (grant no. 2011-0030075) and a grant through the NRF's Basic Science Research Program that was funded by the Ministry of Education (grant no. 2018R1D1A1B07048957).

References
Previous section
1. Köpcke F, Trinczek B, Majeed RW, et al. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence. BMC Med Inform Decis Mak 2013; 13:37 [Google Scholar]
2. Hersh WR, Weiner MG, Embi PJ, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care 2013; 51(suppl 3):S30–S37 [Google Scholar]
3. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 2013; 20:e147–e154 [Google Scholar]
4. Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009; 16:328–337 [Google Scholar]
5. Lependu P, Iyer SV, Fairon C, Shah NH. Annotation analysis for testing drug safety signals using unstructured clinical notes. J Biomed Semantics 2012; 3(suppl 1):S5 [Google Scholar]
6. Yadav K, Sarioglu E, Smith M, Choi HA. Automated outcome classification of emergency department computed tomography imaging reports. Acad Emerg Med 2013; 20:848–854 [Google Scholar]
7. Johnson DB, Taira RK, Cardenas AF, Aberle DR. Extracting information from free text radiology reports. Int J Digit Libr 1997; 1:297–308 [Google Scholar]
8. Friedman C, Hripcsak G, DuMouchel W, Johnson SB, Clayton PD. Natural language processing in an operational clinical information system. Nat Lang Eng 1995; 1:83–108 [Google Scholar]
9. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010; 17:507–513 [Google Scholar]
10. Dreyer KJ, Kalra MK, Maher MM, et al. Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study. Radiology 2005; 234:323–329 [Google Scholar]
11. Hassanpour S, Langlotz CP, Amrhein TJ, Befera NT, Lungren MP. Performance of a machine learning classifier of knee MRI reports in two large academic radiology practices: a tool to estimate diagnostic yield. AJR 2017; 208:750–753 [Google Scholar]
12. Wu Y, Schuster M, Chen Z, et al. Google's neural machine translation system: bridging the gap between human and machine translation. arXiv website. arxiv.org/abs/1609.08144. Published October 8, 2016. Accessed December 10, 2017 [Google Scholar]
13. Namin ST, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO. Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods 2018; 14:66 [Google Scholar]
14. Cocos A, Fiks AG, Masino AJ. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Inform Assoc 2017; 24:813–821 [Google Scholar]
15. Masino AJ, Grundmeier RW, Pennington JW, Germiller JA, Crenshaw EB 3rd. Temporal bone radiology report classification using open source machine learning and natural langue processing libraries. BMC Med Inform Decis Mak 2016; 16:65 [Google Scholar]
16. Levenshtein VI. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 1966; 10:707–710 [Google Scholar]
17. Soukoreff RW, MacKenzie IS. Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic. In: Extended Abstracts of the ACM Conference on Human Factors in Computing Systems: CHI 2001. New York, NY: Association for Computing Machinery, 2001:319–320 [Google Scholar]
18. Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Tech 2011; 2:37–63 [Google Scholar]
19. Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: NIPS 2014: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2. Cambridge, MA: MIT Press, 2014:3104–3112 [Google Scholar]
20. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997; 9:1735–1780 [Google Scholar]
21. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Brookline, MA: Microtome, 2010:249–256 [Google Scholar]
22. Kingma D, Ba J. Adam: a method for stochastic optimization. arXiv website. arxiv.org/abs/1412.6980. Published December 22, 2014. Accessed December 10, 2017 [Google Scholar]
23. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15:1929–1958 [Google Scholar]
24. Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv website. arxiv.org/abs/1603.04467. Published March 14, 2016. Accessed December 10, 2017 [Google Scholar]
25. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv website. arxiv.org/abs/1301.3781. Published January 16, 2013. Accessed December 10, 2017 [Google Scholar]
26. Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994; 1:161–174 [Google Scholar]
27. Jain NL, Knirsch CA, Friedman C, Hripcsak G. Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports. In: Proceedings of the AMIA Annual Fall Symposium. Bethesda, MD: American Medical Informatics Association, 1996:542 [Google Scholar]
28. Jain NL, Friedman C. Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports. In: Proceedings of the AMIA Annual Fall Symposium. Bethesda, MD: American Medical Informatics Association, 1997:829 [Google Scholar]
29. Hripcsak G, Austin JH, Alderson PO, Friedman C. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology 2002; 224:157–163 [Google Scholar]
30. Hersh W, Mailhot M, Arnott-Smith C, Lowe H. Selective automated indexing of findings and diagnoses in radiology reports. J Biomed Inform2001; 34:262–273 [Google Scholar]
31. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61:85–117 [Google Scholar]
32. Leeper NJ, Bauer-Mehren A, Iyer SV, LePendu P, Olson C, Shah NH. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes. PLoS One 2013; 8:e63499 [Google Scholar]
33. Gallego B, Dunn AG, Coiera E. Role of electronic health records in comparative effectiveness research. J Comp Eff Res 2013; 2:529–532 [Google Scholar]
34. Ni Y, Kennebeck S, Dexheimer JW, et al. Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department. J Am Med Inform Assoc 2014; 22:166–178 [Google Scholar]
Address correspondence to J. Jang ().

Full Text | PDF (1086 KB) | PDF Plus (1110 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 741-747
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.20065) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

FOCUS ON: Medical Physics and Informatics

Original Research

Comparison of Radiation Dose and Image Quality of Contrast-Enhanced Dual-Source CT of the Chest: Single-Versus Dual-Energy and Second-Versus Third-Generation Technology

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 741-747. 10.2214/AJR.18.20065

ABSTRACT :

OBJECTIVE. The purpose of this study was to compare radiation dose and image quality of single- and dual-energy CT (SECT, DECT) examinations of the chest in matched cohorts for second and third-generation dual-source CT (DSCT) systems.

MATERIALS AND METHODS. We analyzed 200 patients (100 men; mean age, 61.7 ± 14.8 years old; 100 women, mean age, 59.4 ± 15.1 years old), matched by sex and body mass index, who had undergone clinically indicated contrast-enhanced chest CT. Four study groups, each consisting of 50 patients, were evaluated. Contrast-enhanced chest CT was performed using vendor-preset second-generation DSCT (group A, 120-kV SECT; group C, 80/Sn140-kV DECT) or third-generation DSCT (group B, 90-kV SECT; group D, 90/Sn150-kV DECT) protocols. Radiation dose assessment was normalized to a scan range of 27.5 cm. Image quality was objectively analyzed using dose-independent figure-of-merit (FOM) contrast-to-noise ratio (CNR) calculations and subjectively evaluated by three independent radiologists.

RESULTS. Direct comparison of effective radiation dose for second-generation DSCT groups A and C showed statistically significant lower radiation dose values for DECT compared with SECT acquisition (3.2 ± 1.2 mSv vs 2.3 ± 0.6 mSv, p ≤ 0.004), but differences between third-generation SECT and DECT were not significant (1.2 ± 0.9 mSv vs 1.3 ± 0.6 mSv, p = 0.412). FOM CNR analysis revealed highest values for third-generation DECT (p ≤ 0.043). Differences in subjective image quality between the four groups were not statistically significant (p ≥ 0.179).

CONCLUSION. Contrast-enhanced DECT examinations of the chest can be performed routinely with second- and third-generation DSCT systems without either increased radiation exposure or decreased image quality compared with SECT acquisition.

Keywords: diagnostic imaging, lung, MDCT, radiation dose, thorax

M.H. Albrecht has received speakers' fees from Siemens Healthcare and Bracco. J. L. Wichmann has received speakers' fees from GE Healthcare and Siemens Healthcare. Data were controlled by authors with no potential conflict of interest.

References
Previous section
1. Weiss J, Notohamiprodjo M, Bongers M, et al. Effect of noise-optimized monoenergetic postprocessing on diagnostic accuracy for detecting incidental pulmonary embolism in portal-venous phase dual-energy computed tomography. Invest Radiol 2017; 52:142–147 [Google Scholar]
2. Apfaltrer P, Sudarski S, Schneider D, et al. Value of monoenergetic low-kV dual energy CT datasets for improved image quality of CT pulmonary angiography. Eur J Radiol 2014; 83:322–328 [Google Scholar]
3. Choe J, Lee SM, Chae EJ, et al. Evaluation of postoperative lung volume and perfusion changes by dual-energy computed tomography in patients with lung cancer. Eur J Radiol 2017; 90:166–173 [Google Scholar]
4. Takeuchi H, Suzuki S, Machida H, Ishikawa T, Ueno E. Preliminary results: can dual-energy computed tomography help distinguish cardiogenic pulmonary edema and acute interstitial lung disease? J Comput Assist Tomogr 2018; 42:39–44 [Google Scholar]
5. Leithner D, Wichmann JL, Vogl TJ, et al. Virtual monoenergetic imaging and iodine perfusion maps improve diagnostic accuracy of dual-energy computed tomography pulmonary angiography with suboptimal contrast attenuation. Invest Radiol 2017; 52:659–665 [Google Scholar]
6. Chen X, Xu Y, Duan J, Li C, Sun H, Wang W. Correlation of iodine uptake and perfusion parameters between dual-energy CT imaging and first-pass dual-input perfusion CT in lung cancer. Medicine (Baltimore) 2017; 96:e7479 [Google Scholar]
7. Moon JW, Bae JP, Lee HY, et al. Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis. Eur Radiol 2016; 26:1368–1377 [Google Scholar]
8. Lee SM, Seo JB, Hwang HJ, et al. Assessment of regional emphysema, air-trapping and xenon-ventilation using dual-energy computed tomography in chronic obstructive pulmonary disease patients. Eur Radiol 2017; 27:2818–2827 [Google Scholar]
9. Lapointe A, Bahig H, Blais D, et al. Assessing lung function using contrast-enhanced dual energy computed tomography for potential applications in radiation therapy. Med Phys 2017; 44:5260–5269 [Google Scholar]
10. Meier A, Wurnig M, Desbiolles L, Leschka S, Frauenfelder T, Alkadhi H. Advanced virtual monoenergetic images: improving the contrast of dual-energy CT pulmonary angiography. Clin Radiol 2015; 70:1244–1251 [Google Scholar]
11. Schabel C, Bongers M, Sedlmair M, et al. Assessment of the hepatic veins in poor contrast conditions using dual energy CT: evaluation of a novel monoenergetic extrapolation software algorithm. RoFo Fortschr Geb Rontgenstr Nuklearmed 2014; 186:591–597 [Google Scholar]
12. Yuan R, Shuman WP, Earls JP, et al. Reduced iodine load at CT pulmonary angiography with dual-energy monochromatic imaging: comparison with standard CT pulmonary angiography—a prospective randomized trial. Radiology 2012; 262:290–297 [Google Scholar]
13. Krauss B, Grant KL, Schmidt BT, Flohr TG. The importance of spectral separation: an assessment of dual-energy spectral separation for quantitative ability and dose efficiency. Invest Radiol 2015; 50:114–118 [Google Scholar]
14. Gordic S, Morsbach F, Schmidt B, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection: first performance evaluation of single energy scanning with spectral shaping. Invest Radiol 2014; 49:465–473 [Google Scholar]
15. Wichmann JL, Hardie AD, Schoepf UJ, et al. Single- and dual-energy CT of the abdomen: comparison of radiation dose and image quality of 2nd and 3rd generation dual-source CT. Eur Radiol 2017; 27:642–650 [Google Scholar]
16. Schenzle JC, Sommer WH, Neumaier K, et al. Dual energy CT of the chest: how about the dose? Invest Radiol 2010; 45:347–353 [Google Scholar]
17. Shrimpton PC, Hillier MC, Lewis MA, Dunn M. National survey of doses from CT in the UK: 2003. Br J Radiol 2006; 79:968–980 [Erratum in Br J Radiol 2007; 80:685 (dosage error)] [Google Scholar]
18. [No authors listed]. The 2007 Recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP2007; 37:1–332 [Google Scholar]
19. Christner JA, Kofler JM, McCollough CH. Estimating effective dose for CT using dose-length product compared with using organ doses: consequences of adopting International Commission on Radiological Protection publication 103 or dual-energy scanning. AJR 2010; 194:881–889 [Google Scholar]
20. Schindera ST, Nelson RC, Mukundan S Jr, et al. Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection–phantom study. Radiology 2008; 246:125–132 [Google Scholar]
21. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 1994; 6:284–290 [Google Scholar]
22. Purysko AS, Primak AN, Baker ME, et al. Comparison of radiation dose and image quality from single-energy and dual-energy CT examinations in the same patients screened for hepatocellular carcinoma. Clin Radiol 2014; 69:e538–e544 [Google Scholar]
23. Saba L, di Martino M, Siotto P, et al. Radiation dose and image quality of computed tomography of the supra-aortic arteries: a comparison between single-source and dual-source CT scanners. J Neuroradiol 2018; 45:136–141 [Google Scholar]
24. De Cecco CN, Darnell A, Macias N, et al. Second-generation dual-energy computed tomography of the abdomen: radiation dose comparison with 64-and 128-row single-energy acquisition. J Comput Assist Tomogr 2013; 37:543–546 [Google Scholar]
25. Primak AN, Giraldo JC, Eusemann CD, et al. Dual-source dual-energy CT with additional tin filtration: dose and image quality evaluation in phantoms and in vivo. AJR 2010; 195:1164–1174 [Google Scholar]
26. Nam SB, Jeong DW, Choo KS, et al. Image quality of CT angiography in young children with congenital heart disease: a comparison between the sinogramaffirmed iterative reconstruction (SAFIRE) and advanced modelled iterative reconstruction (ADMIRE) algorithms. Clin Radiol 2017; 72:1060–1065 [Google Scholar]
27. Schmid AI, Uder M, Lell MM. Reaching for better image quality and lower radiation dose in head and neck CT: advanced modeled and sinogramaffirmed iterative reconstruction in combination with tube voltage adaptation. Dentomaxillofac Radiol 2017; 46:20160131 [Google Scholar]
28. Mangold S, De Cecco CN, Wichmann JL, et al. Effect of automated tube voltage selection, integrated circuit detector and advanced iterative reconstruction on radiation dose and image quality of 3rd generation dual-source aortic CT angiography: an intra-individual comparison. Eur J Radiol 2016; 85:972–978 [Google Scholar]
29. Graser A, Johnson TR, Hecht EM, et al. Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology 2009; 252:433–440 [Google Scholar]
30. Einstein AJ, Henzlova MJ, Rajagopalan S. Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography. JAMA 2007; 298:317–323 [Google Scholar]
31. Lenga L, Albrecht MH, Othman AE, et al. Monoenergetic dual-energy computed tomographic imaging: cardiothoracic applications. J Thorac Imaging 2017; 32:151–158 [Google Scholar]
Address correspondence to J. L. Wichmann ().

S. S. Martin and J. L. Wichmann contributed equally to this work.

Full Text | PDF (778 KB) | PDF Plus (809 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 748-754
Posted online on December 17, 2018.
(https://doi.org/10.2214/AJR.18.20334) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

FOCUS ON: Medical Physics and Informatics

Original Research

Ultra-Low-Dose Neck CT With Low-Dose Contrast Material for Preoperative Staging of Thyroid Cancer: Image Quality and Diagnostic Performance

Share Share Journal Club
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 748-754. 10.2214/AJR.18.20334

ABSTRACT :

OBJECTIVE. Although CT has been used as a complementary diagnostic method for the preoperative diagnosis of thyroid cancer, it has the shortcomings of substantial radiation exposure and the use of contrast material (CM). The purpose of this article is to evaluate the image quality and diagnostic performance of 70-kVp thyroid CT with low volumes of CM versus conventional 120-kVp thyroid CT protocol.

MATERIALS AND METHODS. Eighty patients referred for preoperative thyroid CT were randomly divided into two groups (group A: 40 patients, 70 kVp, 60 mL of CM; group B: 40 patients, 120 kVp, 100 mL of CM). Quantitative and qualitative image quality and radiation doses for the two groups were compared using the Mann-Whitney U and chi-square tests. Degrees of agreement between preoperative CT staging and pathologic results were evaluated and compared using the Wald statistic.

RESULTS. Calculated signal-to-noise ratios of different anatomic structures, calculated contrast-to-noise ratios, overall image quality, subjective noise, and streak artifacts were not significantly different between the two groups (all p > 0.05), and neither were the accuracies of preoperative CT staging (all p > 0.05). The estimated effective doses were significantly lower in group A (mean [± SD], 0.52 ± 0.14 mSv in group A and 2.28 ± 0.29 mSv in group B; p < 0.001).

CONCLUSION. Ultra-low-dose 70-kVp CT with a low volume of CM provides sufficient image quality for preoperative staging of thyroid cancer and substantially reduces the radiation dose compared with standard 120-kVp CT.

Keywords: neck, thyroid cancer, ultra-low-dose CT

Based on a presentation at the European Congress of Radiology 2018 annual meeting, Vienna, Austria.

References
Previous section
1. Suh CH, Baek JH, Choi YJ, Lee JH. Performance of CT in the preoperative diagnosis of cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis. AJNR 2017; 38:154–161 [Google Scholar]
2. Lee YH, Seo HS, Suh SI, et al. Feasibility study of a contrast-enhanced multi-detector CT (64 channels) protocol for papillary thyroid carcinoma: the influence of different scan delays on tumor conspicuity. Thyroid 2016; 26:726–733 [Google Scholar]
3. Liu X, Ouyang D, Li H, et al. Papillary thyroid cancer: dual-energy spectral CT quantitative parameters for preoperative diagnosis of metastasis to the cervical lymph nodes. Radiology 2015; 275:167–176 [Google Scholar]
4. Ippolito D, Talei Franzesi C, Fior D, Bonaffini PA, Minutolo O, Sironi S. Low kV settings CT angiography (CTA) with low dose contrast medium volume protocol in the assessment of thoracic and abdominal aorta disease: a feasibility study. Br J Radiol 2015; 88:20140140 [Google Scholar]
5. Beeres M, Trommer J, Frellesen C, et al. Evaluation of different keV-settings in dual-energy CT angiography of the aorta using advanced image-based virtual monoenergetic imaging. Int J Cardiovasc Imaging 2016; 32:137–144 [Google Scholar]
6. Mourits MM, Nijhof WH, van Leuken MH, Jager GJ, Rutten MJ. Reducing contrast medium volume and tube voltage in CT angiography of the pulmonary artery. Clin Radiol 2016; 71:615.e7–615.e13 [Google Scholar]
7. Gnannt R, Winklehner A, Goetti R, Schmidt B, Kollias S, Alkadhi H. Low kilovoltage CT of the neck with 70 kVp: comparison with a standard protocol. AJNR 2012; 33:1014–1019 [Google Scholar]
8. Hsieh MS, Chiu CS, Chen WC, et al. Iodinated contrast medium exposure during computed tomography increase the risk of subsequent development of thyroid disorders in patients without known thyroid disease: a nationwide population-based, propensity score-matched, longitudinal follow-up study. Medicine (Baltimore) 2015; 94:e2279 [Google Scholar]
9. Nakayama Y, Awai K, Funama Y, et al. Lower tube voltage reduces contrast material and radiation doses on 16-MDCT aortography. AJR 2006; 187:[web]W490–W497 [Google Scholar]
10. Wichmann JL, Hu X, Kerl JM, et al. 70 kVp computed tomography pulmonary angiography: potential for reduction of iodine load and radiation dose. J Thorac Imaging 2015; 30:69–76 [Google Scholar]
11. Yu L, Li H, Fletcher JG, McCollough CH. Automatic selection of tube potential for radiation dose reduction in CT: a general strategy. Med Phys2010; 37:234–243 [Google Scholar]
12. Tawfik AM, Kerl JM, Bauer RW, et al. Dual-energy CT of head and neck cancer: average weighting of low- and high-voltage acquisitions to improve lesion delineation and image quality-initial clinical experience. Invest Radiol 2012; 47:306–311 [Google Scholar]
13. Russell MT, Fink JR, Rebeles F, Kanal K, Ramos M, Anzai Y. Balancing radiation dose and image quality: clinical applications of neck volume CT. AJNR 2008; 29:727–731 [Google Scholar]
14. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 2010; 17:1471–1474 [Google Scholar]
15. Choi JS, Kim J, Kwak JY, Kim MJ, Chang HS, Kim EK. Preoperative staging of papillary thyroid carcinoma: comparison of ultrasound imaging and CT. AJR 2009; 193:871–878 [Google Scholar]
16. Hoang JK, Vanka J, Ludwig BJ, Glastonbury CM. Evaluation of cervical lymph nodes in head and neck cancer with CT and MRI: tips, traps, and a systematic approach. AJR 2013; 200:[web]W17–W25 [Google Scholar]
17. Bongartz G, Golding S, Jurik A, et al. European guidelines for multislice computed tomography. Brussels, Belgium: European Commission, 2004 [Google Scholar]
18. American Association of Physicists in Medicine (AAPM). AAPM Report no. 204: size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. AAPM website. www.aapm.org/pubs/reports/RPT_204.pdf. Published 2011. Accessed October 30, 2018 [Google Scholar]
19. Donner A, Shoukri MM, Klar N, Bartfay E. Testing the equality of two dependent kappa statistics. Stat Med 2000; 19:373–387 [Google Scholar]
20. Scholtz JE, Kaup M, Kraft J, et al. Objective and subjective image quality of primary and recurrent squamous cell carcinoma on head and neck low-tube-voltage 80-kVp computed tomography. Neuroradiology 2015; 57:645–651 [Google Scholar]
21. Scholtz JE, Wichmann JL, Husers K, et al. Third-generation dual-source CT of the neck using automated tube voltage adaptation in combination with advanced modeled iterative reconstruction: evaluation of image quality and radiation dose. Eur Radiol 2016; 26:2623–2631 [Google Scholar]
22. Scholtz JE, Husers K, Kaup M, et al. Evaluation of image quality and dose reduction of 80 kVp neck computed tomography in patients with suspected peritonsillar abscess. Clin Radiol 2015; 70:e67–e73 [Google Scholar]
23. Wichmann JL, Kraft J, Noske EM, et al. Low-tube-voltage 80-kVp neck CT: evaluation of diagnostic accuracy and interobserver agreement. AJNR 2014; 35:2376–2381 [Google Scholar]
24. Zhang J, Kang S, Han D, Xie X, Deng Y. Application of intelligent optimal kV scanning technology (CARE kV) in dual-source computed tomography (DSCT) coronary angiography. Int J Clin Exp Med 2015; 8:17644–17653 [Google Scholar]
25. Kalra MK, Maher MM, Toth TL, et al. Strategies for CT radiation dose optimization. Radiology 2004; 230:619–628 [Google Scholar]
26. Wintersperger B, Jakobs T, Herzog P, et al. Aortoiliac multidetector-row CT angiography with low kV settings: improved vessel enhancement and simultaneous reduction of radiation dose. Eur Radiol 2005; 15:334–341 [Google Scholar]
27. Sigal-Cinqualbre AB, Hennequin R, Abada HT, Chen X, Paul JF. Low-kilovoltage multi-detector row chest CT in adults: feasibility and effect on image quality and iodine dose. Radiology 2004; 231:169–174 [Google Scholar]
28. Brooks RA. A quantitative theory of the Hounsfield unit and its application to dual energy scanning. J Comput Assist Tomogr 1977; 1:487–493 [Google Scholar]
29. Gordic S, Desbiolles L, Stolzmann P, et al. Advanced modelled iterative reconstruction for abdominal CT: qualitative and quantitative evaluation. Clin Radiol 2014; 69:e497–e504 [Google Scholar]
30. Rhee CM, Bhan I, Alexander EK, Brunelli SM. Association between iodinated contrast media exposure and incident hyperthyroidism and hypothyroidism. Arch Intern Med 2012; 172:153–159 [Google Scholar]
31. Padovani RP, Kasamatsu TS, Nakabashi CCD, et al. One month is sufficient for urinary iodine to return to its baseline value after the use of water-soluble iodinated contrast agents in post-thyroidectomy patients requiring radioiodine therapy. Thyroid 2012; 22:926–930 [Google Scholar]
32. Sohn SY, Choi JH, Kim NK, et al. The impact of iodinated contrast agent administered during pre-operative computed tomography scan on body iodine pool in patients with differentiated thyroid cancer preparing for radioactive iodine treatment. Thyroid 2014; 24:872–877 [Google Scholar]
33. Schindera ST, Graca P, Patak MA, et al. Thoracoabdominal-aortoiliac multidetector-row CT angiography at 80 and 100 kVp: assessment of image quality and radiation dose. Invest Radiol 2009; 44:650–655 [Google Scholar]
Address correspondence to S. K. Baik ().

Full Text | PDF (844 KB) | PDF Plus (792 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 755-757
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20508) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

FOCUS ON: Medical Physics and Informatics

Clinical Perspective

Patient Shielding in Diagnostic Imaging: Discontinuing a Legacy Practice

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 755-757. 10.2214/AJR.18.20508

ABSTRACT :

OBJECTIVE. Patient shielding is standard practice in diagnostic imaging, despite growing evidence that it provides negligible or no benefit and carries a substantial risk of increasing patient dose and compromising the diagnostic efficacy of an image. The historical rationale for patient shielding is described, and the folly of its continued use is discussed.

CONCLUSION. Although change is difficult, it is incumbent on radiologic technologists, medical physicists, and radiologists to abandon the practice of patient shielding in radiology.

Keywords: gonadal shielding, patient safety, patient shielding, radiation, shielding

References
Previous section
1. Advanced Health Education Center. What would you do? Stop shielding your patients? Advanced Health Education Center Online website.ahecblog.com/2018/01/15/what-would-you-do-stop-shielding-your-patients. Published January 15, 2018. Accessed July 18, 2018 [Google Scholar]
2. Gomes M, Matias A, Macedo F. Risks to the fetus from diagnostic imaging during pregnancy: review and proposal of a clinical protocol. Pediatr Radiol 2015; 45:1916–1929 [Google Scholar]
3. Pahade JK, Litmanovich D, Pedrosa I, et al. Quality initiatives: imaging pregnant patients with suspected pulmonary embolism: what the radiologist needs to know. RadioGraphics 2009; 29:639–654 [Google Scholar]
4. Ratnapalan S, Bentur Y, Koren G. Doctor, will that x-ray harm my unborn child? CMAJ 2008; 179:1293–1296 [Erratum in CMAJ 2009; 280:952] [Google Scholar]
5. Sikand M, Stinchcombe S, Livesley PJ. Study on the use of gonadal protection shields during paediatric pelvic x-rays. Ann R Coll Surg Engl2003; 85:422–425 [Google Scholar]
6. Ursprung WM, Howe JW, Yochum TR, Kettner NW. Plain film radiography, pregnancy, and therapeutic abortion revisited. J Manipulative Physiol Ther 2006; 29:83–87 [Google Scholar]
7. 21 C.F.R. §1000.50 (1976) [Google Scholar]
8. International Commission on Radiological Protection (ICRP). Recommendations of the International Commission on Radiological Protection: ICRP publication 26. Ann ICRP 1977; 1(3) [Google Scholar]
9. 21 C.F.R. §1000.50 (2018) [Google Scholar]
10. National Research Council. Health risks from exposure to low levels of ionizing radiation: BEIR VII phase 2. Washington: DC: The National Academies Press, 2006 [Google Scholar]
11. Bishop HA, Webber M, O'Loughlin BJ. Reducing gonad irradiation in pediatric diagnosis. Calif Med 1959; 90:20–25 [Google Scholar]
12. Frantzen MJ, Robben S, Postma AA, et al. Gonad shielding in paediatric pelvic radiography: disadvantages prevail over benefit. Insights Imaging 2012; 3:23–32 [Google Scholar]
13. National Council on Radiation Protection and Measurements (NCRP). Preconception and prenatal radiation exposure: health effects and protective guidance. Report 174. Bethesda, MD: NCRP Publications, 2013 [Google Scholar]
14. Gilet AG, Dunkin JM, Fernandez TJ, et al. Fetal radiation dose during gestation estimated on an anthropomorphic phantom for three generations of CT scanners. AJR 2011; 196:1133–1137 [Google Scholar]
15. Colletti PM, Lee KH, Elkayam U. Cardiovascular imaging of the pregnant patient. AJR 2013; 200:515–521 [Google Scholar]
16. Wieseler KM, Bhargava P, Kanal KM, et al. Imaging in pregnant patients: examination appropriateness. RadioGraphics 2010; 30:1215–1229 [Google Scholar]
17. Arnold KM, Flynn NJ, Raben A, et al. The impact of radiation on the tumor microenvironment: effect of dose and fractionation schedules. Cancer Growth Metastasis 2018; 11:1179064418761639 [Google Scholar]
18. Tubiana M. Dose-effect relationship and estimation of the carcinogenic effects of low doses of ionizing radiation: the joint report of the Académie des Sciences (Paris) and of The Académie Nationale de Médicine. Int J Radiat Oncol Biol Phys 2005; 63:317–319 [Google Scholar]
19. Brooks AL, Hoel DG, Preston RJ. The role of dose rate in radiation cancer risk: evaluating the effect of dose rate at the molecular, cellular and tissue levels using key events in critical pathways following exposure to low LET radiation. Int J Radiat Biol 2016; 92:405–426 [Google Scholar]
20. Moghissi AA, Calderone R, Azam F, et al. Regulating ionizing radiation based on metrics for evaluation of regulatory science claims. Dose Response 2018; 16:1559325817749413 [Google Scholar]
21. American College of Radiology (ACR). ACR–AAPM–SIIM–SPR practice parameter for digital radiography. ACR website.www.acr.org/-/media/ACR/Files/Practice-Parameters/Rad-Digital.pdf. Accessed July 23, 2018 [Google Scholar]
22. Patel SJ, Reede DL, Katz DS, et al. Imaging the pregnant patient for nonobstetric conditions: algorithms and radiation dose considerations. RadioGraphics 2007; 27:1705–1722 [Google Scholar]
23. Ryckx N, Sans-Merce M, Schmidt S, et al. The use of out-of-plane high Z patient shielding for fetal dose reduction in computed tomography: literature review and comparison with Monte-Carlo calculations of an alternative optimization technique. Phys Med 2018; 48:156–161 [Google Scholar]
24. Sadro C, Bernstein MP, Kanal KM. Imaging of trauma. Part 2. Abdominal trauma and pregnancy: a radiologist's guide to doing what is best for the mother and baby. AJR 2012; 199:1207–1219 [Google Scholar]
25. Sidhu M, Strauss K, Connolly B, et al. Radiation safety in pediatric interventional radiology. Tech Vasc Interv Radiol 2010; 13:158–166 [Google Scholar]
26. 21 C.F.R. § 020.30 (2017) [Google Scholar]
27. National Council on Radiation Protection and Measurements (NCRP). Radiation protection in pediatric radiology. Report 68. Bethesda, MD: NCRP Publications, 1978 [Google Scholar]
28. Fawcett SL, Barter SJ. The use of gonad shielding in paediatric hip and pelvis radiographs. Br J Radiol 2009; 82:363–370 [Google Scholar]
29. Winfeld M, Strubel N, Pinkney L, et al. Relative distribution of pertinent findings on portable neonatal abdominal radiographs: can we shield the gonads? Pediatr Radiol 2013; 43:1295–1302 [Google Scholar]
30. Lee MC, Lloyd J, Solomito MJ. Poor utility of gonadal shielding for pediatric pelvic radiographs. Orthopedics 2017; 40:e623–e627 [Google Scholar]
31. Hendee WR. Personal and public perceptions of radiation risks. RadioGraphics 1991; 11:1109–1119 [Google Scholar]
32. Freudenberg LS, Beyer T. Subjective perception of radiation risk. J Nucl Med 2011; 52(suppl 2):29S–35S [Google Scholar]
Address correspondence to R. M. Marsh ().

Full Text | PDF (586 KB) | PDF Plus (609 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 758-765
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20036) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Cardiopulmonary Imaging

Original Research

Multireader Determination of Clinically Significant Obstruction Using Hyperpolarized 129Xe–Ventilation MRI

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 758-765. 10.2214/AJR.18.20036

ABSTRACT :

OBJECTIVE. The objective of our study was to identify the magnitude and distribution of ventilation defect scores (VDSs) derived from hyperpolarized (HP) 129Xe-MRI associated with clinically relevant airway obstruction.

MATERIALS AND METHODS. From 2012 to 2015, 76 subjects underwent HP 129Xe-MRI (48 healthy volunteers [mean age ± SD, 54 ± 17 years]; 20 patients with asthma [mean age, 44 ± 20 years]; eight patients with chronic obstructive pulmonary disease [mean age, 67 ± 5 years]). All subjects underwent spirometry 1 day before MRI to establish the presence of airway obstruction (forced expiratory volume in 1 second–to–forced vital capacity ratio [FEV1/FVC] < 70%). Five blinded readers assessed the degree of ventilation impairment and assigned a VDS (range, 0–100%). Interreader agreement was assessed using the Fleiss kappa statistic. Using FEV1/FVC as the reference standard, the optimum VDS threshold for the detection of airway obstruction was estimated using ROC curve analysis with 10-fold cross-validation.

RESULTS. Compared with the VDSs in healthy subjects, VDSs in patients with airway obstruction were significantly higher (p < 0.0001) and significantly correlated with disease severity (r = 0.66, p < 0.0001). Ventilation defects in subjects with airway obstruction did not show a location-specific pattern (p = 0.158); however, defects in healthy control subjects were more prevalent in the upper lungs (p = 0.014). ROC curve analysis yielded an optimal threshold of 12.4% ± 6.1% (mean ± SD) for clinically significant VDS. Interreader agreement for 129Xe-MRI was substantial (κ = 0.71).

CONCLUSION. This multireader study of a diverse cohort of patients and control subjects suggests a 129Xe–ventilation MRI VDS of 12.4% or greater represents clinically significant obstruction.

Keywords: airway obstruction, asthma, chronic obstructive pulmonary disease (COPD), MRI, xenon

References
Previous section
1. Ebner L, Kammerman J, Driehuys B, Schiebler ML, Cadman RV, Fain SB. The role of hyperpolarized 129Xenon in MR imaging of pulmonary function. Eur J Radiol 2017; 86:343–352 [Google Scholar]
2. Virgincar RS, Cleveland ZI, Kaushik SS, et al. Quantitative analysis of hyperpolarized 129Xe ventilation imaging in healthy volunteers and subjects with chronic obstructive pulmonary disease. NMR Biomed 2013; 24:424–435 [Google Scholar]
3. He M, Driehuys B, Que LG, Huang YT. Using hyperpolarized 129Xe MRI to quantify the pulmonary ventilation distribution. Acad Radiol 2016; 23:1521–1531 [Google Scholar]
4. Ebner L, He M, Virgincar RS. Hyperpolarized 129Xenon magnetic resonance imaging to quantify regional ventilation differences in mild to moderate asthma: a prospective comparison between semiautomated ventilation defect percentage calculation and pulmonary function tests. Invest Radiol 2017; 52:120–127 [Google Scholar]
5. Criner GJ, Sue R, Wright S, et al.; LIBERATE Study Group. A multicenter RCT of Zephyr endo-bronchial valve treatment in heterogeneous emphysema (LIBERATE). Am J Respir Crit Care Med 2018; 198:1151–1164 [Google Scholar]
6. Kirby M, Svenningsen S, Owrangi A, Wheatley A. Hyperpolarized 3He and 129Xe MR imaging in healthy volunteers and patients with chronic obstructive pulmonary disease. Radiology 2012; 265:600–610 [Google Scholar]
7. Salzman SH. Which pulmonary function tests best differentiate between COPD phenotypes? Respir Care 2012; 57:50–60 [Google Scholar]
8. Bossuyt PM, Reitsma JB, Bruns DE, et al.; Standards for Reporting of Diagnostic Accuracy Group. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. The Standards for Reporting of Diagnostic Accuracy Group. Croat Med J 2003; 44:639–650 [Google Scholar]
9. National Heart, Lung, and Blood Institute. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: NHLBI, 2007:1–440 [Google Scholar]
10. Global Initiative for Chronic Obstructive Lung Disease website. Global Initiative for Chronic Obstructive Lung Disease: pocket guide to COPD diagnosis, management, and prevention. A guide for health care professionals 2017 report. goldcopd.org/wp-content/uploads/dlm_uploads/2016/12/wms-GOLD-2017-Pocket-Guide-Final.pdf. Accessed January 28, 2019 [Google Scholar]
11. He M, Driehuys B, Que LG, Huang YT. Using hyperpolarized 129Xe MRI to quantify the pulmonary ventilation distribution. Acad Radiol 2016; 23:1521–1531 [Google Scholar]
12. Roos JE, McAdams HP, Kaushik SS, Driehuys B. Hyperpolarized gas MR imaging. Magn Reson Imaging Clin N Am 2015; 23:217–229 [Google Scholar]
13. He M, Robertson SH, Kaushik SS, et al. Dose and pulse sequence considerations for hyperpolarized 129Xe ventilation MRI. Magn Reson Imaging 2015; 33:877–885 [Google Scholar]
14. Suga K. Technical and analytical advances in pulmonary ventilation SPECT with xenon-133 gas and Tc-99m-Technegas. Ann Nucl Med 2002; 16:303–310 [Google Scholar]
15. Rabe KF, Hurd S, Anzueto A, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2007; 176:532–555 [Google Scholar]
16. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012; 22:276–282 [Google Scholar]
17. Virgincar RS, Cleveland ZI, Sivaram Kaushik S, et al. Quantitative analysis of hyperpolarized 129Xe ventilation imaging in healthy volunteers and subjects with chronic obstructive pulmonary disease. NMR Biomed 2012; 26:424–435 [Google Scholar]
18. He M, Kaushik SS, Robertson SH, et al. Extending semiautomatic ventilation defect analysis for hyperpolarized 129Xe ventilation MRI. Acad Radiol 2014; 21:1530–1541 [Google Scholar]
19. Marshall H, Deppe MH, Parra-Robles J, et al. Direct visualisation of collateral ventilation in COPD with hyperpolarised gas MRI. Thorax 2012; 67:613–617 [Google Scholar]
20. Janssens JP, Pache JC, Nicod LP. Physiological changes in respiratory function associated with ageing. Eur Respir J 1999; 13:197–205 [Google Scholar]
21. Win T, Tasker AD, Groves AM, et al. Ventilation-perfusion scintigraphy to predict postoperative pulmonary function in lung cancer patients undergoing pneumonectomy. AJR 2006; 187:1260–1265 [Google Scholar]
22. Chae EJ, Seo JB, Goo HW, et al. Xenon ventilation CT with a dual-energy technique of dual-source CT: initial experience. Radiology 2008; 248:615–624 [Google Scholar]
23. Bannier E, Cieslar K, Mosbah K, et al. Hyperpolarized 3He MR for sensitive imaging of ventilation function and treatment efficiency in young cystic fibrosis patients with normal lung function. Radiology 2010; 255:225–232 [Google Scholar]
24. Mahmood K, Ebner L, He M, et al. Novel magnetic resonance imaging for assessment of bronchial stenosis in lung transplant recipients. Am J Transplant 2017; 17:1895–1904 [Google Scholar]
25. Svenningsen S, Kirby M, Starr D, et al. Hyperpolarized (3) He and (129) Xe MRI: differences in asthma before bronchodilation. J Magn Reson Imaging 2013; 38:1521–1530 [Google Scholar]
26. Capaldi D, Sheikh K, Eddy RL, et al. Free-breathing functional pulmonary MRI: response to bronchodilator and bronchoprovocation in severe asthma. Acad Radiol 2017; 24:1268–1276 [Google Scholar]
27. Zha N, Pike D, Svenningsen S, et al. Second-order texture measurements of (3)He ventilation MRI: proof-of-concept evaluation of asthma bronchodilator response. Acad Radiol 2016; 23:176–185 [Google Scholar]
Address correspondence to L. Ebner ().

Funding for data acquisition was provided by the National Heart, Lung, and Blood Institute (grants NHLBI R01HL105643 and R01HL126771). L. Ebner received financial funding from the Swiss National Science Foundation (grant SNSF P2SKP3_158645/1). B. Driehuys is founder of and shareholder in Polarean Imaging.

Full Text | PDF (1134 KB) | PDF Plus (774 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 766-772
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20232) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Cardiopulmonary Imaging

Original Research

Differentiation Between Lymphangioleiomyomatosis and Birt-Hogg-Dubé Syndrome: Analysis of Pulmonary Cysts on CT Images

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 766-772. 10.2214/AJR.18.20232

ABSTRACT :

OBJECTIVE. The purposes of this study were to identify diagnostic imaging markers for differentiating pulmonary cysts in lymphangioleiomyomatosis and Birt-Hogg-Dubé syndrome and to identify potential risk factors for spontaneous pneumothorax in the two diseases.

MATERIALS AND METHODS. This retrospective study included 44 patients with lymphangioleiomyomatosis (44 women; mean age, 35 ± 10.9 years) and 13 patients with Birt-Hogg-Dubé syndrome (nine men, four women; mean age, 45.1 ± 10.9 years). CT findings were analyzed to determine the shape; presence of septation, wall visibility, and subpleural cysts; size; number; distribution; location of the largest cyst; and presence of cysts encircling the bronchovascular bundle ("air-cuff" sign) and of mediastinal fat indentation. Multiple logistic regression was performed to identify risk factors for spontaneous pneumothorax.

RESULTS. Compared with patients with lymphangioleiomyomatosis, patients with Birt-Hogg-Dubé syndrome were significantly older, and more of them were men. The cysts in these patients had a more irregular shape, more septation, lower and more peripheral distribution, larger maximum size, and more attachment to the pleura, air-cuff sign, indentation on mediastinal fat, and subpleural cysts larger than 2 cm. The maximum diameter of cysts was the sole independent risk factor for spontaneous pneumothorax (p = 0.027; 95% CI, 1.043–1.992) in both diseases. ROC analysis showed an AUC of 0.745 (95% CI, 0.612–0.851), and the optimal cutoff value was 22 mm (sensitivity, 72.5%; specificity, 76.5%).

CONCLUSION. Several CT imaging markers may help in differentiating pulmonary cysts in patients with lymphangioleiomyomatosis and those with Birt-Hogg-Dubé syndrome and in predicting spontaneous pneumothorax.

Keywords: Birt-Hogg-Dubé syndrome, CT, lymphangioleiomyomatosis, pulmonary cyst

References
Previous section
1. Johnson SR. Lymphangioleiomyomatosis. Eur Respir J 2006; 27:1056–1065 [Google Scholar]
2. Johnson SR, Cordier JF, Lazor R, et al. European Respiratory Society guidelines for the diagnosis and management of lymphangioleiomyomatosis. Eur Respir J 2010; 35:14–26 [Google Scholar]
3. Nickerson ML, Warren MB, Toro JR, et al. Mutations in a novel gene lead to kidney tumors, lung wall defects, and benign tumors of the hair follicle in patients with the Birt-Hogg-Dube syndrome. Cancer Cell 2002; 2:157–164 [Google Scholar]
4. Crino PB, Nathanson KL, Henske EP. The tuberous sclerosis complex. N Engl J Med 2006; 355:1345–1356 [Google Scholar]
5. Menko FH, van Steensel MA, Giraud S, et al. Birt-Hogg-Dube syndrome: diagnosis and management. Lancet Oncol 2009; 10:1199–1206 [Google Scholar]
6. Northrup H, Krueger DA. Tuberous sclerosis complex diagnostic criteria update: recommendations of the 2012 International Tuberous Sclerosis Complex Consensus Conference. Pediatr Neurol 2013; 49:243–254 [Google Scholar]
7. Boehler A, Speich R, Russi EW, Weder W. Lung transplantation for lymphangioleiomyomatosis. N Engl J Med 1996; 335:1275–1280 [Google Scholar]
8. Toro JR, Pautler SE, Stewart L, et al. Lung cysts, spontaneous pneumothorax, and genetic associations in 89 families with Birt-Hogg-Dube syndrome. Am J Respir Crit Care Med 2007; 175:1044–1053 [Google Scholar]
9. Furuya M, Yao M, Tanaka R, et al. Genetic, epidemiologic and clinicopathologic studies of Japanese Asian patients with Birt-Hogg-Dube syndrome. Clin Genet 2016; 90:403–412 [Google Scholar]
10. Abbott GF, Rosado-de-Christenson ML, Frazier AA, Franks TJ, Pugatch RD, Galvin JR. Lymphangioleiomyomatosis: radiologic-pathologic correlation. RadioGraphics 2005; 25:803–828 [Google Scholar]
11. Tobino K, Gunji Y, Kurihara M, et al. Characteristics of pulmonary cysts in Birt-Hogg-Dube syndrome: thin-section CT findings of the chest in 12 patients. Eur J Radiol 2011; 77:403–409 [Google Scholar]
12. Park HJ, Park CH, Lee SE, et al. Birt-Hogg-Dube syndrome prospectively detected by review of chest computed tomography scans. PLoS One2017; 12:e0170713 [Google Scholar]
13. Agarwal PP, Gross BH, Holloway BJ, Seely J, Stark P, Kazerooni EA. Thoracic CT findings in Birt-Hogg-Dube syndrome. AJR 2011; 196:349–352 [Google Scholar]
14. Tobino K, Hirai T, Johkoh T, et al. Differentiation between Birt-Hogg-Dube syndrome and lymphangioleiomyomatosis: quantitative analysis of pulmonary cysts on computed tomography of the chest in 66 females. Eur J Radiol 2012; 81:1340–1346 [Google Scholar]
15. Taveira-DaSilva AM, Burstein D, Hathaway OM, et al. Pneumothorax after air travel in lymphangioleiomyomatosis, idiopathic pulmonary fibrosis, and sarcoidosis. Chest 2009; 136:665–670 [Google Scholar]
16. Hoshika Y, Kataoka H, Kurihara M, et al. Features of pneumothorax and risk of air travel in Birt-Hogg-Dube syndrome. Am J Respir Crit Care Med 2012; 815:A4438 [Google Scholar]
17. Johannesma PC, Waesberghe JV, Reinhard R, et al. Birt-Hogg-Dube syndrome patients with and without pneumothorax: findings on chest CT. Am J Respir Crit Care Med 2014; 189:A6416 [Google Scholar]
18. Johannesma PC, van de Beek I, van der Wel JW, et al. Risk of spontaneous pneumothorax due to air travel and diving in patients with Birt-Hogg-Dube syndrome. Springerplus 2016; 5:1506 [Google Scholar]
19. Zbar B, Alvord WG, Glenn G, et al. Risk of renal and colonic neoplasms and spontaneous pneumothorax in the Birt-Hogg-Dube syndrome. Cancer Epidemiol Biomarkers Prev 2002; 11:393–400 [Google Scholar]
20. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J 2005; 26:319–338 [Google Scholar]
21. Shrout PE. Measurement reliability and agreement in psychiatry. Stat Methods Med Res 1998; 7:301–317 [Google Scholar]
22. Gunji Y, Akiyoshi T, Sato T, et al. Mutations of the Birt Hogg Dube gene in patients with multiple lung cysts and recurrent pneumothorax. J Med Genet 2007; 44:588–593 [Google Scholar]
23. Kunogi M, Kurihara M, Ikegami TS, et al. Clinical and genetic spectrum of Birt-Hogg-Dube syndrome patients in whom pneumothorax and/or multiple lung cysts are the presenting feature. J Med Genet 2010; 47:281–287 [Google Scholar]
24. Murakami Y, Wataya-Kaneda M, Tanaka M, et al. Two Japanese cases of Birt-Hogg-Dube syndrome with pulmonary cysts, fibrofolliculomas, and renal cell carcinomas. Case Rep Dermatol 2014; 6:20–28 [Google Scholar]
25. Houweling AC, Gijezen LM, Jonker MA, et al. Renal cancer and pneumothorax risk in Birt-Hogg-Dube syndrome; an analysis of 115 FLCNmutation carriers from 35 BHD families. Br J Cancer 2011; 105:1912–1919 [Google Scholar]
26. Schmidt LS, Nickerson ML, Warren MB, et al. Germline BHD-mutation spectrum and phenotype analysis of a large cohort of families with Birt-Hogg-Dube syndrome. Am J Hum Genet 2005; 76:1023–1033 [Google Scholar]
27. Butnor KJ, Guinee DG Jr. Pleuropulmonary pathology of Birt-Hogg-Dube syndrome. Am J Surg Pathol 2006; 30:395–399 [Google Scholar]
Address correspondence to E. J. Chae ().

Full Text | PDF (820 KB) | PDF Plus (827 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 773-781
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20519) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Cardiopulmonary Imaging

Original Research

Resected Pure Small Cell Lung Carcinomas and Combined Small Cell Lung Carcinomas: Histopathology Features, Imaging Features, and Prognoses

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 773-781. 10.2214/AJR.18.20519

ABSTRACT :

OBJECTIVE. The objective of our study was to investigate histopathology features, imaging features, and prognoses of surgically resected pure small cell lung carcinomas (SCLCs) and combined SCLCs.

MATERIALS AND METHODS. Forty-one patients with a pure SCLC or a combined SCLC underwent preoperative chest CT and 18F-FDG PET/CT and subsequent surgical resection. The clinicopathologic findings were noted by reviewing the electronic medical records. The imaging features of individual tumors were analyzed on chest CT and PET/CT scans. Each tumor was classified as being located centrally (at or in the segmental bronchus or proximal to the segmental bronchus) or peripherally (distal to the segmental bronchus). The maximum standardized uptake value (SUVmax) of each tumor was measured at PET. The 7th edition of the TNM staging system was adopted for staging.

RESULTS. The study group was composed of 34 men and seven women with a mean age of 62.0 ± 10.2 (SD) years. Sixteen of 41 (39%) patients had pure SCLC, and the remaining patients had combined SCLC. The most common combined SCLC histologic subgroup was combined SCLC and large cell neuroendocrine carcinoma in 17 (41%) patients. The mean SUVmax of pure SCLCs was 5.6 ± 2.2 and was significantly lower than that of combined SCLCs (p < 0.01). Thirty-one patients (76%) had a peripheral tumor, and 10 (24%) had a central tumor. The overall survival (OS) of the 10 patients with a central tumor was 44.6 months, significantly shorter than the OS of the 31 patients with a peripheral tumor (179.2 months) (p = 0.017). The OS of 21 patients with stage I disease was significantly longer than the OS of patients with higher-stage cancer (p = 0.004).

CONCLUSION. In our study group of patients with surgically resected SCLC, patients with a peripheral tumor (including a purely endobronchial tumor) or stage I disease showed a better prognosis than those with a central tumor or higher-stage disease.

Keywords: CT, PET/CT, prognosis, small cell lung cancer, small cell lung carcinoma (SCLC), surgery

References
Previous section
1. Govindan R, Page N, Morgensztern D, et al. Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the Surveillance, Epidemiologic, and End Results database. J Clin Oncol 2006; 24:4539–4544 [Google Scholar]
2. Nicholson SA, Beasley MB, Brambilla E, et al. Small cell lung carcinoma (SCLC): a clinicopathologic study of 100 cases with surgical specimens. Am J Surg Pathol 2002; 26:1184–1197 [Google Scholar]
3. Carter BW, Glisson BS, Truong MT, Erasmus JJ. Small cell lung carcinoma: staging, imaging, and treatment considerations. RadioGraphics2014; 34:1707–1721 [Google Scholar]
4. Zhao X, McCutcheon JN, Kallakury B, et al. Combined small cell carcinoma of the lung: is it a single entity? J Thorac Oncol 2018; 13:237–245 [Google Scholar]
5. Zhang C, Yang P, Zhao H, et al. Clinical outcomes of surgically resected combined small cell lung cancer: a two-institutional experience. J Thorac Dis 2017; 9:151–158 [Google Scholar]
6. Stahel RA. Diagnosis, staging, and prognostic factors of small cell lung cancer. Curr Opin Oncol 1991; 3:306–311 [Google Scholar]
7. Kurishima K, Kagohashi K, Miyazaki K, et al. Small cell lung cancer with endobronchial growth: a case report. Oncol Lett 2013; 6:553–555 [Google Scholar]
8. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 2010; 17:1471–1474 [Google Scholar]
9. Groome PA, Bolejack V, Crowley JJ, et al. The IASLC Lung Cancer Staging Project: validation of the proposals for revision of the T, N, and M descriptors and consequent stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J Thorac Oncol2007; 2:694–705 [Google Scholar]
10. Jhun BW, Lee KJ, Jeong K, et al. Clinical applicability of staging small cell lung cancer according to the seventh edition of the TNM staging system. Lung Cancer 2013; 81:65–71 [Google Scholar]
Address correspondence to K. S. Lee ().

Full Text | PDF (1222 KB) | PDF Plus (844 KB) | Supplemental Material




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 782-787
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20526) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Gastrointestinal Imaging

Original Research

Imaging Characteristics of Liver Metastases Overlooked at Contrast-Enhanced CT

Share Share Claim CREDIT
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 782-787. 10.2214/AJR.18.20526

ABSTRACT :

OBJECTIVE. The purpose of this study was to evaluate the imaging characteristics of liver metastases overlooked at contrast-enhanced CT.

MATERIALS AND METHODS. The records of 746 patients with a diagnosis of liver metastases from colorectal, breast, gastric, or lung cancer between November 2010 and September 2017 were reviewed. Images were reviewed when liver metastases were first diagnosed, and images from prior contrast-enhanced CT examinations were checked if available. These lesions were classified into two groups: missed lesions (those missed on the prior images) and detected lesions (those correctly identified and invisible on the prior images or there were no prior images). Tumor size, contrast-to-noise ratio, location, presence of coexisting liver cysts and hepatic steatosis, and indications for examination were compared between the groups. The t test and Fisher exact test were used to analyze the imaging characteristics of previously overlooked lesions.

RESULTS. The final analysis included 137 lesions, of which 68 were classified as missed. In univariate analysis, contrast-to-noise ratio was significantly lower in missed lesions (95% CI, 2.65 ± 0.24 vs 3.90 ± 0.23; p < 0.001). The proportion of subcapsular lesions (odds ratio, 3.44; p < 0.001), hepatic steatosis (odds ratio, 6.35; p = 0.007), and examination indication other than survey of malignant tumors (odds ratio, 9.07; p = 0.02) were significantly higher for missed lesions.

CONCLUSION. Liver metastases without sufficient contrast enhancement, those in patients with hepatic steatosis, those in subcapsular locations, and those found at examinations for indications other than to assess for tumors were significantly more likely to be overlooked.

Keywords: contrast-enhanced CT, liver metastases, overlooked, perceptual error

References
Previous section
1. Kasper HU, Drebber U, Dries V, Dienes HP. Liver metastases: incidence and histogenesis. Z Gastroenterol 2005; 43:1149–1157 [Google Scholar]
2. Seo HJ, Kim MJ, Lee JD, Chung WS, Kim YE. Gadoxetate disodium–enhanced magnetic resonance imaging versus contrast-enhanced 18F-fluorodeoxyglucose positron emission tomography/computed tomography for the detection of colorectal liver metastases. Invest Radiol 2011; 46:548–555 [Google Scholar]
3. Choi SH, Kim SY, Park SH, et al. Diagnostic performance of CT, gadoxetate disodium-enhanced MRI, and PET/CT for the diagnosis of colorectal liver metastasis: systematic review and meta-analysis. J Magn Reson Imaging 2018; 47:1237–1250 [Google Scholar]
4. Kaur H, Hindman NM, Al-Refaie WB, et al. ACR Appropriateness Criteria: suspected liver metastases. J Am Coll Radiol 2017; 14:S314–S325 [Google Scholar]
5. Kim YW, Mansfield LT. Fool me twice: delayed diagnoses in radiology with emphasis on perpetuated errors. AJR 2014; 202:465–470 [Google Scholar]
6. Brady AP. Error and discrepancy in radiology: inevitable or avoidable? Insights Imaging 2017; 8:171–182 [Google Scholar]
7. Bruno MA, Walker EA, Abujudeh HH. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. RadioGraphics 2015; 35:1668–1676 [Google Scholar]
8. Brogdon BG, Kelsey CA, Moseley RD. Factors affecting perception of pulmonary lesions. Radiol Clin North Am 1983; 21:633–654 [Google Scholar]
9. Li F, Sone S, Abe H, MacMahon H, Armato SG II, Doi K. Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology 2002; 225:673–683 [Google Scholar]
10. Birdwell RL, Ikeda DM, O'Shaughnessy KF, Sickles EA. Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology 2001; 219:192–202 [Google Scholar]
11. Gabata T, Matsui O, Terayama N, Kobayashi S, Sanada J. Imaging diagnosis of hepatic metastases of pancreatic carcinomas: significance of transient wedge-shaped contrast enhancement mimicking arterioportal shunt. Abdom Imaging 2008; 33:437–443 [Google Scholar]
12. Sica GT, Ji H, Ros PR. CT and MR imaging of hepatic metastases. AJR 2000; 174:691–698 [Google Scholar]
13. Soyer P, Poccard M, Boudiaf M, et al. Detection of hypovascular hepatic metastases at triple-phase helical CT: sensitivity of phases and comparison with surgical and histopathologic findings. Radiology 2004; 231:413–420 [Google Scholar]
14. Yoshimitsu K, Honda H, Kuroiwa T, et al. Unusual hemodynamics and pseudolesions of the noncirrhotic liver at CT. RadioGraphics 2001; 21:S81–S96 [Google Scholar]
15. Hamer OW, Aguirre DA, Casola G, et al. Fatty liver: imaging patterns and pitfalls. RadioGraphics 2006; 26:1637–1653 [Google Scholar]
16. Brink JA, Heiken JP, Forman HP, Sagel SS, Molina PL, Brown PC. Hepatic spiral CT: reduction of dose of intravenous contrast material. Radiology 1995; 197:83–88 [Google Scholar]
17. Yamashita Y, Komohara Y, Takahashi M, et al. Abdominal helical CT: evaluation of optimal doses of intravenous contrast material—a prospective randomized study. Radiology 2000; 216:718–723 [Google Scholar]
18. Awai K, Takada K, Onishi H, Hori S. Aortic and hepatic enhancement and tumor-to-liver contrast: analysis of the effect of different concentrations of contrast material at multi-detector row helical CT. Radiology 2002; 224:757–763 [Google Scholar]
19. Pooler BD, Lubner MG, Kim DH, et al. Prospective evaluation of reduced dose computed tomography for the detection of low-contrast liver lesions: direct comparison with concurrent standard dose imaging. Eur Radiol 2017; 27:2055–2066 [Google Scholar]
20. Mayo-Smith WW, Gupta H, Ridlen MS, Brody JM, Clements NC, Cronan JJ. Detecting hepatic lesions: the added utility of CT liver window settings. Radiology 1999; 210:601–604 [Google Scholar]
21. van der Gijp A, Ravesloot CJ, Jarodzka H et al. How visual search relates to visual diagnostic performance: a narrative systematic review of eye-tracking research in radiology. Adv Health Sci Educ Theory Pract 2017; 22:765–787 [Google Scholar]
Address correspondence to H. Nakai ().

Full Text | PDF (760 KB) | PDF Plus (783 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 788-795
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20204) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Gastrointestinal Imaging

Original Research

Accuracy of 3-T MRI for Preoperative T Staging of Esophageal Cancer After Neoadjuvant Chemotherapy, With Histopathologic Correlation

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 788-795. 10.2214/AJR.18.20204

ABSTRACT :

OBJECTIVE. The purpose of this study was to explore the value of 3-T MRI for evaluating the preoperative T staging of esophageal cancer (EC) treated with neoadjuvant chemotherapy (NAC), with histopathologic confirmation.

SUBJECTS AND METHODS. This prospective study enrolled patients for whom endoscopic biopsy showed EC and pretreatment CT showed stage cT1N+M0 or cT2–T4aN0–N3M0. All patients received two cycles of NAC (paclitaxel and nedaplatin protocol) followed by 3-T MRI and surgical resection. Readers assigned a T category on MRI, and postoperative pathologic confirmation was considered the reference standard. Interreader agreement, the diagnostic accuracy of T staging on T2-weighted turbo spin-echo (TSE) BLADE (Siemens Healthcare), contrast-enhanced StarVIBE (Siemens Healthcare), high-resolution delayed phase StarVIBE, and the combination of the three sequences were analyzed and compared with postoperative pathologic T staging.

RESULTS. The study included 79 patients. Mean time between NAC and MRI was 23 days. Interreader agreements of T category assignment were excellent for T2-weighted TSE BLADE (κ = 0.810, p < 0.0001), contrast-enhanced StarVIBE (κ = 0.845, p < 0.0001), high-resolution delayed phase StarVIBE (κ = 0.897, p < 0.0001), and the combination of the three sequences (κ = 0.880, p < 0.0001). The highest accuracy for T0, T1, T2, and T4a lesions was on high-resolution delayed phase StarVIBE (96.2%, 92.4%, 91.1%, and 91.1% for reader 1; 94.9%, 89.9%, 91.1%, and 94.9% for reader 2), and the highest accuracy for T3 lesions was on T2-weighted TSE BLADE (92.4% and 94.9% for reader 1 and reader 2, respectively). Diagnostic accuracy of the combination of the three sequences was not improved compared with individual sequences.

CONCLUSION. High-resolution delayed phase StarVIBE had the highest diagnostic accuracy in staging EC after NAC for all T categories except T3, for which T2-weighted TSE BLADE had the highest accuracy. Combining all three sequences did not improve diagnostic accuracy.

Keywords: esophageal cancer, MRI, neoadjuvant therapy, neoplasm staging

References
Previous section
1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136:E359–E386 [Google Scholar]
2. Lane BF, Vandermeer FQ, Oz RC, Irwin EW, McMillan AB, Wong-You-Cheong JJ. Comparison of sagittal T2-weighted BLADE and fast spin-echo MRI of the female pelvis for motion artifact and lesion detection. AJR 2011; 197:[web]W307–W313 [Google Scholar]
3. Pennathur A, Luketich JD. Resection for esophageal cancer: strategies for optimal management. Ann Thorac Surg 2008; 85:S751–S756 [Google Scholar]
4. Räsänen JV, Sihvo EI, Knuuti MJ, et al. Prospective analysis of accuracy of positron emission tomography, computed tomography, and endoscopic ultrasonography in staging of adenocarcinoma of the esophagus and the esophagogastric junction. Ann Surg Oncol 2003; 10:954–960 [Google Scholar]
5. Mortensen MB, Fristrup C, Holm FS, et al. Prospective evaluation of patient tolerability, satisfaction with patient information, and complications in endoscopic ultrasonography. Endoscopy 2005; 37:146–153 [Google Scholar]
6. Wakelin SJ, Deans C, Crofts TJ, Allan PL, Plevris JN, Paterson-Brown S. A comparison of computerised tomography, laparoscopic ultrasound and endoscopic ultrasound in the preoperative staging of oesophago-gastric carcinoma. Eur J Radiol 2002; 41:161–167 [Google Scholar]
7. Wu LF, Wang BZ, Feng JL, et al. Preoperative TN staging of esophageal cancer: comparison of miniprobe ultrasonography, spiral CT and MRI. World J Gastroenterol 2003; 9:219–224 [Google Scholar]
8. Dave UR, Williams AD, Wilson JA, et al. Esophageal cancer staging with endoscopic MR imaging: pilot study. Radiology 2004; 230:281–286 [Google Scholar]
9. Riddell AM, Allum WH, Thompson JN, Wotherspoon AC, Richardson C, Brown G. The appearances of oesophageal carcinoma demonstrated on high-resolution, T2-weighted MRI, with histopathological correlation. Eur Radiol 2007; 17:391–399 [Google Scholar]
10. Giganti F, Ambrosi A, Petrone MC, et al. Prospective comparison of MR with diffusion-weighted imaging, endoscopic ultrasound, MDCT and positron emission tomography-CT in the pre-operative staging of oesophageal cancer: results from a pilot study. Br J Radiol 2016; 89:20160087 [Google Scholar]
11. Ohgiya Y, Suyama J, Seino N, et al. MRI of the neck at 3 Tesla using the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) (BLADE) sequence compared with T2-weighted fast spin-echo sequence. J Magn Reson Imaging 2010; 32:1061–1067 [Google Scholar]
12. Rosenkrantz AB, Bennett GL, Doshi A, Deng FM, Babb JS, Taneja SS. T2-weighted imaging of the prostate: impact of the BLADE technique on image quality and tumor assessment. Abdom Imaging 2015; 40:552–559 [Google Scholar]
13. Azevedo RM, de Campos RO, Ramalho M, Heredia V, Dale BM, Semelka RC. Free-breathing 3D T1-weighted gradient-echo sequence with radial data sampling in abdominal MRI: preliminary observations. AJR 2011; 197:650–657 [Google Scholar]
14. Qu J, Zhang H, Wang Z, et al. Comparison between free-breathing radial VIBE on 3-T MRI and endoscopic ultrasound for preoperative T staging of resectable oesophageal cancer, with histopathological correlation. Eur Radiol 2018; 28:780–787 [Google Scholar]
15. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A. AJCC cancer staging manual, 7th ed. New York, NY: Springer-Verlag, 2010 [Google Scholar]
16. Chandarana H, Block TK, Rosenkrantz AB, et al. Free-breathing radial 3D fat-suppressed T1-weighted gradient echo sequence: a viable alternative for contrast-enhanced liver imaging in patients unable to suspend respiration. Invest Radiol 2011; 46:648–653 [Google Scholar]
17. Wu X, Raz E, Block TK, et al. Contrast-enhanced radial 3D fat-suppressed T1-weighted gradient-recalled echo sequence versus conventional fat-suppressed contrast-enhanced T1-weighted studies of the head and neck. AJR 2014; 203:883–889 [Google Scholar]
18. Zhang F, Qu J, Zhang H, et al. Preoperative T staging of potentially resectable esophageal cancer: a comparison between free-breathing radial VIBE and breath-hold cartesian VIBE, with histopathological correlation. Transl Oncol 2017; 10:324–331 [Google Scholar]
Address correspondence to J. Qu ().

Z. Wang, J. Guo, and J. Qin contributed equally to this study.

Full Text | PDF (1102 KB) | PDF Plus (967 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 796-801
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20293) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Dual-Source Dual-Energy CT in Detection and Characterization of Urinary Stones in Patients With Large Body Habitus: Observations in a Large Cohort

Share Share Claim CREDIT
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 796-801. 10.2214/AJR.18.20293

ABSTRACT :

OBJECTIVE. The objective of our study was to investigate the impact of large body habitus on dual-energy CT (DECT) image quality and stone characterization.

MATERIALS AND METHODS. We retrospectively included 105 consecutive patients with large body habitus (> 90 kg) who underwent stone protocol DECT between 2015 and 2017. The evaluation of DECT datasets was performed for image quality assessment based on European Guidelines on Quality Criteria for Computed Tomography and for determination of stone composition (i.e., uric acid vs non–uric acid). Correlation between DECT characterization and crystallography results was performed when available. The cohort was divided into two groups on the basis of body weight (≤ 104 kg and > 104 kg), and comparisons were made for image quality and stone characterization.

RESULTS. One hundred ninety-seven urinary tract calculi (size: mean ± SD, 5.7 ± 5.3 mm; range, 1.4–56 mm) were detected in 73% (79/108) of examinations in 105 patients (weight: mean ± SD, 104.0 ± 12.7 kg; range, 91–163 kg). The overall mean image quality score of blended images and color maps was 3.7 and 3.9, respectively, and the effective dual-energy FOV limitation did not hamper stone characterization. The diagnostic acceptability scores of blended images and color maps were slightly lower in patients weighing > 104 kg than in patients ≤ 104 kg (mean scores [highest score, 4 points]: blended images, 3.62 vs 3.82 [p = 0.0314]; color maps, 3.75 vs 3.98 [p = 0.0034]), but the scores were within acceptable range. Stone characterization as uric acid versus non–uric acid was achieved in 80% (158/197) of calculi (size: mean ± SD, 6.4 ± 5.7 mm; range, 1.6–56 mm), and DECT stone characterization was (95.6%) accurate with reference to crystallography. Twenty percent (39/197) of calculi could not be characterized on DECT, and these calculi were significantly smaller in size (size: mean ± SD, 2.8 ± 1.4 mm; range, 1.4–8.2 mm; p < 0.001) than those that could be characterized. The mean size of uncharacterized calculi was slightly larger in patients weighing > 104 kg (3.3 ± 1.6 mm) than in those weighing ≤ 104 kg (2.2 ± 0.6 mm).

CONCLUSION. In patients with large body habitus, dual-source DECT provides acceptable image quality and allows characterization of almost all clinically significant calculi.

Keywords: dual-source dual-energy CT, large body habitus, urolithiasis

Based on a presentation at the Radiological Society of North America 2016 annual meeting, Chicago, IL.

References
Previous section
1. Sturm R, Hattori A. Morbid obesity rates continue to rise rapidly in the US. Int J Obes (Lond) 2013; 37:889–891 [Google Scholar]
2. Jarolimova J, Tagoni J, Stern TA. Obesity: its epidemiology, comorbidities, and management. Prim Care Companion CNS Discord 2013; 15:PCC. 12f01475 [Google Scholar]
3. Uppot RN, Sahani DV, Hahn PF, Kalra MK, Saini SS, Mueller PR. Effect of obesity on image quality: fifteen-year longitudinal study for evaluation of dictated radiology reports. Radiology 2006; 240:435–439 [Google Scholar]
4. Scales CD, Smith AC, Hanley JM, Saigal CS, Urologic Diseases in America Project: prevalence of kidney stones in the United States. Eur Urol2012; 62:160–165 [Google Scholar]
5. Pak CY, Sakhaee K, Peterson RD, Poindexter JR, Frawley WH. Biochemical profile of idiopathic uric acid nephrolithiasis. Kidney Int 2001; 60:757–761 [Google Scholar]
6. Ekeruo WO, Tan YH, Young MD, et al. Metabolic risk factors and the impact of medical therapy on the management of nephrolithiasis in obese patients. J Urol 2004; 172:159–163 [Google Scholar]
7. Semins MJ, Shore AD, Makary MA, Magnuson T, Johns R, Matlaga BR. The association of increasing body mass index and kidney stone disease. J Urol 2010; 183:571–575 [Google Scholar]
8. Hess B. Metabolic syndrome, obesity and kidney stones. Arab J Urol 2012; 10:258–264 [Google Scholar]
9. Ahmed MH, Ahmed HT, Khalil AA. Renal stone disease and obesity: what is important for urologists and nephrologists? Ren Fail 2012; 34:1348–1354 [Google Scholar]
10. Brisbane W, Bailey MR, Sorensen MD. An overview of kidney stone imaging techniques. Nat Rev Urol 2016; 13:654–662 [Google Scholar]
11. McCarthy CJ, Baliyan V, Kordbacheh H, Sajjad Z, Sahani D, Kambadakone A. Radiology of renal stone disease. Int J Surg 2016; 36:638–646 [Google Scholar]
12. Megibow AJ, Sahani D. Best practice: implementation and use of abdominal dual-energy CT in routine patient care. AJR 2012; 199(suppl 5):S71–S77 [Google Scholar]
13. Desai GS, Uppot RN, Yu EW, Kambadakone AR, Sahani DV. Impact of iterative reconstruction on image quality and radiation dose in multidetector CT of large body size adults. Eur Radiol 2012; 22:1631–1640 [Google Scholar]
14. Qu M, Jaramillo-Alvarez G, Ramirez-Giraldo JC, et al. Urinary stone differentiation in patients with large body size using dual-energy dual-source computed tomography. Eur Radiol 2012; 23:1408–1414 [Google Scholar]
15. Primak AN, Ramirez Giraldo JC, Liu X, Yu L, McCollough CH. Improved dual-energy material discrimination for dual-source CT by means of additional spectral filtration. Med Phys 2009; 36:1359–1369 [Google Scholar]
16. Duan X, Li Z, Yu L, et al. Characterization of urinary stone composition by use of third-generation dual-source dual-energy CT with increased spectral separation. AJR 2015; 205:1203–1207 [Google Scholar]
17. Leng S, Huang A, Cardona JM, Duan X, Williams JC, McCollough CH. Dual-energy CT for quantification of urinary stone composition in mixed stones: a phantom study. AJR 2016; 207:321–329 [Google Scholar]
18. Wisenbaugh ES, Paden RG, Silva AC, Humphreys MR. Dual-energy vs conventional computed tomography in determining stone composition. Urology 2014; 83:1243–1247 [Google Scholar]
19. Akand M, Koplay M, Islamoglu N, Gul M, Kilic O, Erdogdu MB. Role of dual-source dual-energy computed tomography versus X-ray crystallography in prediction of the stone composition: a retrospective non-randomized pilot study. Int Urol Nephrol 2016; 48:1413–1420 [Google Scholar]
20. Ngo TC, Assimos DG. Uric acid nephrolithiasis: recent progress and future directions. Rev Urol 2007; 9:17–27 [Google Scholar]
21. Caramia G, Di Gregorio L, Tarantino ML, Galuffo A, Iacolino R, Caramia M. Uric acid, phosphate and oxalate stones: treatment and prophylaxis. Urol Int 2004; 72(suppl 1):24–28 [Google Scholar]
22. Jepperson MA, Cernigliaro JG, Sella D, et al. Dual-energy CT for the evaluation of urinary calculi: image interpretation, pitfalls and stone mimics. Clin Radiol 2013; 68:e707–e714 [Google Scholar]
23. Le NT, Robinson J, Lewis SJ. Obese patients and radiography literature: what do we know about a big issue? J Med Radiat Sci 2015; 62:132–141 [Google Scholar]
24. Eliahou R, Hidas G, Duvdevani M, Sosna J. Determination of renal stone composition with dual-energy computed tomography: an emerging application. Semin Ultrasound CT MR 2010; 31:315–320 [Google Scholar]
25. Boll DT, Patil NA, Paulson EK, et al. Renal stone assessment with dual-energy multidetector CT and advanced postprocessing techniques: improved characterization of renal stone composition—pilot study 1. Radiology 2009; 250:813–820 [Google Scholar]
26. Matlaga BR, Kawamoto S, Fishman E. Dual source computed tomography: a novel technique to determine stone composition. Urology 2008; 72:1164–1168 [Google Scholar]
27. Stolzmann P, Kozomara M, Chuck N, et al. In vivo identification of uric acid stones with dual-energy CT: diagnostic performance evaluation in patients. Abdom Imaging 2010; 35:629–635 [Google Scholar]
28. Primak AN, Fletcher JG, Vrtiska TJ, et al. Noninvasive differentiation of uric acid versus non–uric acid kidney stones using dual-energy CT. Acad Radiol 2007; 14:1441–1447 [Google Scholar]
29. Marin D, Pratts-Emanuelli JJ, Mileto A, et al. Interdependencies of acquisition, detection, and reconstruction techniques on the accuracy of iodine quantification in varying patient sizes employing dual-energy CT. Eur Radiol 2015; 25:679–686 [Google Scholar]
30. Shaqdan KW, Kambadakone AR, Hahn P, Sahani DV. Experience with iterative reconstruction techniques for abdominopelvic computed tomography in morbidly and super obese patients. J Comput Assist Tomogr 2018; 42:124–132 [Google Scholar]
31. Soenen O, Balliauw C, Oyen R, Zanca F. Dose and image quality in low-dose CT for urinary stone disease: added value of automatic tube current modulation and iterative reconstruction techniques. Radiat Prot Dosimetry 2017; 174:242–249 [Google Scholar]
32. Andrabi Y, Pianykh O, Agrawal M, Kambadakone A, Blake MA, Sahani DV. Radiation dose consideration in kidney stone CT examinations: integration of iterative reconstruction algorithms with routine clinical practice. AJR 2015; 204:1055–1063 [Google Scholar]
33. Megibow AJ, Kambadakone A, Ananthakrishnan L. Dual-energy computed tomography: image acquisition, processing, and workflow. Radiol Clin North Am 2018; 56:507–520 [Google Scholar]
Address correspondence to A. R. Kambadakone ().

H. Kordbacheh and V. Baliyan contributed equally to this study.

Full Text | PDF (695 KB) | PDF Plus (728 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 802-807
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20077) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Isolated Right-Sided Varicocele: Is Further Workup Necessary?

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 802-807. 10.2214/AJR.18.20077

ABSTRACT :

OBJECTIVE. Unilateral left varicoceles are common and considered benign. Unilateral right varicoceles are reportedly associated with a pathologic process, namely malignancy affecting the retroperitoneum, for which further imaging is often recommended. The purpose of this study was to test the hypothesis that this correlation between unilateral right varicocele and malignancy may be weaker than once suggested, particularly in the absence of other clinical signs of malignancy.

MATERIALS AND METHODS. Medical charts and imaging at one institution were reviewed for all patients reported to have right varicocele. Follow-up cross-sectional imaging and clinical records and surgical and medical history were reviewed for possible nonmalignant or malignant causes of varicocele.

RESULTS. Ninety-six patients with unilateral right varicocele diagnosed by means of ultrasound were identified. Twenty-nine (30.2%) patients were excluded because of confounding factors (infection, testicular mass, intrascrotal surgery). Among the other 67, 55 had available follow-up information, 39 with cross-sectional imaging. Right-sided varicocele was attributable to nonmalignant causes in 16 of the 55 subjects (29.1%) and to malignancy in two subjects: one with metastatic disease of undetermined primary and one with confluent liver masses. Both patients presented with other signs of malignancy and represented only 3.6% of the cohort who underwent follow-up.

CONCLUSION. In this cohort, patients with right-sided varicocele attributable to malignancy presented with additional signs of metastatic disease. Nonmalignant causes were more common. Therefore, confounding conditions should be considered when incidental isolated right varicocele is identified. Health care costs, patient anxiety, and unnecessary harm can be substantially reduced through modulation of follow-up recommendations based on additional findings at presentation.

Keywords: health care economics, malignancy, ultrasound, varicocele

K. Bishop has a consultation agreement with Hewlett Packard Enterprise Company. D. T. Fetzer has research and consultation agreements with Philips Ultrasound and Siemens Healthcare and is a member of the speakers' bureaus of Philips Healthcare and Siemens Healthcare.

References
Previous section
1. Beddy P, Geoghegan T, Browne RF, Torreggiani WC. Testicular varicoceles. Clin Radiol 2005; 60:1248–1255 [Google Scholar]
2. Pryor JL, Howards SS. Varicocele. Urol Clin North Am 1987; 14:499–513 [Google Scholar]
3. Canales BK, Zapzalka DM, Ercole CJ, et al. Prevalence and effect of varicoceles in an elderly population. Urology 2005; 66:627–631 [Google Scholar]
4. Cokkinos DD, Antypa E, Tserotas P, et al. Emergency ultrasound of the scrotum: a review of the commonest pathologic conditions. Curr Probl Diagn Radiol 2011; 40:1–14 [Google Scholar]
5. Nielsen ME, Zderic S, Freedland SJ, Jarow JP. Insight on pathogenesis of varicoceles: relationship of varicocele and body mass index. Urology2006; 68:392–396 [Google Scholar]
6. Mali WP, Oei HY, Arndt JW, Kremer J, Coolsaet BL, Schuur K. Hemodynamics of the varicocele. Part 2. Correlation among the results of renocaval pressure measurements, varicocele scintigraphy and phlebography. J Urol 1986; 135:489–493 [Google Scholar]
7. Mali WP, Oei HY, Arndt JW, Kremer J, Coolsaet BL, Schuur K. Hemodynamics of the varicocele. Part 1. Correlation among the clinical, phlebographic and scintigraphic findings. J Urol 1986; 135:483–488 [Google Scholar]
8. Shafik A, Moftah A, Olfat S, Mohi-el-Din M, el-Sayed A. Testicular veins: anatomy and role in varicocelogenesis and other pathologic conditions. Urology 1990; 35:175–182 [Google Scholar]
9. Tsao CW, Hsu CY, Chou YC, et al. The relationship between varicoceles and obesity in a young adult population. Int J Androl 2009; 32:385–390 [Google Scholar]
10. Handel LN, Shetty R, Sigman M. The relationship between varicoceles and obesity. J Urol 2006; 176:2138–2140; discussion, 2140 [Google Scholar]
11. Spittel JA Jr, Deweerd JH, Shick RM. Acute varicocele: a vascular clue to renal tumor. Proc Staff Meet Mayo Clin 1959; 34:134–137 [Google Scholar]
12. Rifkin MD, Foy PM, Kurtz AB, Pasto ME, Goldberg BB. The role of diagnostic ultrasonography in varicocele evaluation. J Ultrasound Med 1983; 2:271–275 [Google Scholar]
13. Bhosale PR, Patnana M, Viswanathan C, Szklaruk J. The inguinal canal: anatomy and imaging features of common and uncommon masses. RadioGraphics 2008; 28:819–835 [Google Scholar]
14. Pauroso S, Di Leo N, Fulle I, Di Segni M, Alessi S, Maggini E. Varicocele: ultrasonographic assessment in daily clinical practice. J Ultrasound2011; 14:199–204 [Google Scholar]
15. Horstman WG, Middleton WD, Melson GL, Siegel BA. Color Doppler US of the scrotum. RadioGraphics 1991; 11:941–957; discussion, 958 [Google Scholar]
16. Belker AM. The varicocele and male infertility. Urol Clin North Am 1981; 8:41–51 [Google Scholar]
17. Practice Committee of the American Society for Reproductive Medicine; Society for Male Reproduction and Urology. Report on varicocele and infertility: a committee opinion. Fertil Steril 2014; 102:1556–1560 [Google Scholar]
18. Gibbons RP, Monte JE, Correa RJ Jr, Mason JT. Manifestations of renal cell carcinoma. Urology 1976; 8:201–206 [Google Scholar]
19. El-Saeity NS, Sidhu PS. "Scrotal varicocele, exclude a renal tumour": is this evidence based? Clin Radiol 2006; 61:593–599 [Google Scholar]
20. Chiba K, Ramasamy R, Lamb DJ, Lipshultz LI. The varicocele: diagnostic dilemmas, therapeutic challenges and future perspectives. Asian J Androl 2016; 18:276–281 [Google Scholar]
21. Luciani LG, Cestari R, Tallarigo C. Incidental renal cell carcinoma: age and stage characterization and clinical implications—study of 1092 patients (1982–1997). Urology 2000; 56:58–62 [Google Scholar]
Address correspondence to D. T. Fetzer ().

Full Text | PDF (971 KB) | PDF Plus (835 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 808-814
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20154) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Comparison of Tin Filter–Based Spectral Shaping CT and Low-Dose Protocol for Detection of Urinary Calculi

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 808-814. 10.2214/AJR.18.20154

ABSTRACT :

OBJECTIVE. The purpose of this study was to assess the performance of tin filter–based spectral shaping CT compared with routine low-dose CT for detection of urolithiasis.

MATERIALS AND METHODS. Unenhanced third-generation dual-source CT scans of 129 consecutively registered patients were retrospectively reviewed: 43 patients underwent CT for detection of renal stones with tin filtration (Sn150 kV); 43 patients underwent a routine low-dose CT protocol at 100 kV; and 43 patients underwent a routine CT protocol with automated tube potential selection (110–120 kV). Image quality was evaluated subjectively and objectively. Volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE) were recorded. To prospectively compare the performances of the spectral shaping protocol (Sn150 kV) with the standard (120 kV) and routine low-dose (100 kV) protocols, a phantom (sheep kidneys) containing stones were also scanned with each protocol and evaluated by two radiologists.

RESULTS. CT with tin filtration resulted in 28% and 66% reduction in CTDIvol compared with CT performed with routine low-dose and standard-dose protocols (p < 0.05). Accordingly, it also led to 24% and 55% reduction in SSDE compared with the low-dose and standard protocols (p < 0.05). Subjective image quality and signal-to-noise ratio were similar between the tin filtration and the routine low-dose groups (p > 0.05). The objective image noise was similar in the three groups (p > 0.05). The phantom study showed no difference in detection of renal stones between the three tube potential settings.

CONCLUSION. Using spectral shaping with tin filtration can substantially reduce radiation dose compared with routine standard- and low-dose abdominal CT for urinary stone disease.

Keywords: CT, dose reduction, image quality, kidney stone, spectral shielding, tin filtration

References
Previous section
1. Ziemba JB, Matlaga BR. Epidemiology and economics of nephrolithiasis. Investig Clin Urol 2017; 58:299–306 [Google Scholar]
2. Uribarri J, Oh MS, Carroll HJ. The first kidney stone. Ann Intern Med 1989; 111:1006–1009 [Google Scholar]
3. Brisbane W, Bailey MR, Sorensen MD. An overview of kidney stone imaging techniques. Nat Rev Urol 2016; 13:654–662 [Google Scholar]
4. Kwon H, Cho J, Oh J, et al. The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique. Br J Radiol 2015; 88:20150463 [Google Scholar]
5. Khawaja RD, Singh S, Blake M, et al. Ultra-low dose abdominal MDCT: using a knowledge-based iterative model reconstruction technique for substantial dose reduction in a prospective clinical study. Eur J Radiol 2015; 84:2–10 [Google Scholar]
6. Nakayama Y, Awai K, Funama Y, et al. Abdominal CT with low tube voltage: preliminary observations about radiation dose, contrast enhancement, image quality, and noise. Radiology 2005; 237:945–951 [Google Scholar]
7. Kalra MK, Maher MM, D'Souza RV, et al. Detection of urinary tract stones at low-radiation-dose CT with z-axis automatic tube current modulation: phantom and clinical studies. Radiology 2005; 235:523–529 [Google Scholar]
8. Niemann T, Kollmann T, Bongartz G. Diagnostic performance of low-dose CT for the detection of urolithiasis: a meta-analysis. AJR 2008; 191:396–401 [Google Scholar]
9. Poletti PA, Platon A, Rutschmann OT, Schmidlin FR, Iselin CE, Becker CD. Low-dose versus standard-dose CT protocol in patients with clinically suspected renal colic. AJR 2007; 188:927–933 [Google Scholar]
10. Sung MK, Singh S, Kalra MK. Current status of low dose multi-detector CT in the urinary tract. World J Radiol 2011; 3:256–265 [Google Scholar]
11. Yu L, Liu X, Leng S, et al. Radiation dose reduction in computed tomography: techniques and future perspective. Imaging Med 2009; 1:65–84 [Google Scholar]
12. Kulkarni NM, Uppot RN, Eisner BH, Sahani DV. Radiation dose reduction at multidetector CT with adaptive statistical iterative reconstruction for evaluation of urolithiasis: how low can we go? Radiology 2012; 265:158–166 [Google Scholar]
13. Braun FM, Johnson TR, Sommer WH, Thierfelder KM, Meinel FG. Chest CT using spectral filtration: radiation dose, image quality, and spectrum of clinical utility. Eur Radiol 2015; 25:1598–1606 [Google Scholar]
14. Gordic S, Morsbach F, Schmidt B, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection. Invest Radiol 2014; 49:465–473 [Google Scholar]
15. Suntharalingam S, Allmendinger T, Blex S, et al. Spectral beam shaping in unenhanced chest CT examinations: a phantom study on dose reduction and image quality. Acad Radiol 2018; 25:153–158 [Google Scholar]
16. Bodelle B, Fischbach C, Booz C, et al. Single-energy pediatric chest computed tomography with spectral filtration at 100 kVp: effects on radiation parameters and image quality. Pediatr Radiol 2017; 47:831–837 [Google Scholar]
17. Lell MM, May MS, Brand M, et al. Imaging the para-sinus region with a third-generation dual-source CT and the effect of tin filtration on image quality and radiation dose. AJNR 2015; 36:1225–1230 [Google Scholar]
18. Dewes P, Frellesen C, Scholtz JE, et al. Low-dose abdominal computed tomography for detection of urinary stone disease: impact of additional spectral shaping of the X-ray beam on image quality and dose parameters. Eur J Radiol 2016; 85:1058–1062 [Google Scholar]
19. Zhang GM, Shi B, Sun H, et al. High-pitch low-dose abdominopelvic CT with tin-filtration technique for detecting urinary stones. Abdom Radiol (NY) 2017; 42:2127–2134 [Google Scholar]
20. Haubenreisser H, Meyer M, Sudarski S, Allmendinger T, Schoenberg SO, Henzler T. Un-enhanced third-generation dual-source chest CT using a tin filter for spectral shaping at 100kVp. Eur J Radiol 2015; 84:1608–1613 [Google Scholar]
21. May MS, Brand M, Lell MM, et al. Radiation dose reduction in parasinus CT by spectral shaping. Neuroradiology 2017; 59:169–176 [Google Scholar]
22. Liu W, Liu J, Xue H, et al. Feasibility of pediatric chest CT using spectral filtration on third-generation dual-source CT. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2017; 39:21–27 [Google Scholar]
23. Seyal AR, Arslanoglu A, Abboud SF, Sahin A, Horowitz JM, Yaghmai V. CT of the abdomen with reduced tube voltage in adults: a practical approach. RadioGraphics 2015; 35:1922–1939 [Google Scholar]
Address correspondence to V. Yaghmai ().

A. Mozaffary and T. Agirlar Trabzonlu have received educational grants from Siemens Healthineers.

Full Text | PDF (858 KB) | PDF Plus (863 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 815-822
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.20266) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Safety and Image Quality of 1.5-T Endorectal Coil Multiparametric MRI of the Prostate or Prostatectomy Fossa for Patients With Pacemaker or Implantable Cardioverter-Defibrillator

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 815-822. 10.2214/AJR.18.20266

ABSTRACT :

OBJECTIVE. The purpose of this study is to report the patient safety and image quality of 1.5-T multiparametric MRI of the prostate in patients with cardiac implantable electronic devices (CIEDs).

MATERIALS AND METHODS. In this retrospective study, a database was searched to identify prostate multiparametric 1.5-T MRI examinations performed with endorectal coils for patients with CIEDs from 2012 to 2016 (study group) and matched patients without CIEDs (control group). Clinical safety in the study group was reviewed. The specific absorption rate (SAR) and signal-to-noise ratio (SNR) were measured in both groups. Imaging quality and artifact on T2-weighted images, DW images, and dynamic contrast-enhanced images were rated on a 5-point scale by two independent readers.

RESULTS. The study group consisted of total 28 multiparametric MRI examinations in 25 patients. There were no serious device-related adverse effects observed (0/28; 0%), and the estimated whole-body SAR in the study group was never greater than 1.5 W/kg. The SNR values tended to be lower in the study group than in the control group. However, overall perceived image preferences and influences of artifacts on image quality for the study group were not significantly different from those for the control group (p > 0.05), which were rated above average (rating 3) by both readers 1 and 2.

CONCLUSION. Multiparametric 1.5-T MRI examination of the prostate can be safely performed in selected patients with CIEDs under controlled conditions with applicable image quality while maintaining a SAR less than 1.5 W/kg.

Keywords: image quality, implantable cardioverterdefibrillator, MRI, pacemaker, prostate

References
Previous section
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin 2017; 67:7–30 [Google Scholar]
2. Valerio M, Donaldson I, Emberton M, et al. Detection of clinically significant prostate cancer using magnetic resonance imaging-ultrasound fusion targeted biopsy: a systematic review. Eur Urol 2015; 68:8–19 [Google Scholar]
3. Mottet N, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1. Screening, diagnosis, and local treatment with curative intent. Eur Urol 2017; 71:618–629 [Google Scholar]
4. Cornford P, Bellmunt J, Bolla M, et al. EAU-ESTROSIOG guidelines on prostate cancer. Part II. Treatment of relapsing, metastatic, and castration-resistant prostate cancer. Eur Urol 2017; 71:630–642 [Google Scholar]
5. Kalin R, Stanton MS. Current clinical issues for MRI scanning of pacemaker and defibrillator patients. Pacing Clin Electrophysiol 2005; 28:326–328 [Google Scholar]
6. Chua JD, Wilkoff BL, Lee I, Juratli N, Longworth DL, Gordon SM. Diagnosis and management of infections involving implantable electrophysiologic cardiac devices. Ann Intern Med 2000; 133:604–608 [Google Scholar]
7. Colletti PM, Shinbane JS, Shellock FG. "MR-conditional" pacemakers: the radiologist's role in multidisciplinary management. AJR 2011; 197:[web] W457–W459 [Google Scholar]
8. Sommer T, Vahlhaus C, Lauck G, et al. MR imaging and cardiac pacemakers: in-vitro evaluation and in-vivo studies in 51 patients at 0.5 T. Radiology 2000; 215:869–879 [Google Scholar]
9. Sommer T, Naehle CP, Yang A, et al. Strategy for safe performance of extrathoracic magnetic resonance imaging at 1.5 tesla in the presence of cardiac pacemakers in non-pacemaker-dependent patients: a prospective study with 115 examinations. Circulation 2006; 114:1285–1292 [Google Scholar]
10. Russo RJ. Determining the risks of clinically indicated nonthoracic magnetic resonance imaging at 1.5 T for patients with pacemakers and implantable cardioverter-defibrillators: rationale and design of the MagnaSafe Registry. Am Heart J 2013; 165:266–272 [Google Scholar]
11. Gimbel JR, Bello D, Schmitt M, et al. Randomized trial of pacemaker and lead system for safe scanning at 1.5 Tesla. Heart Rhythm 2013; 10:685–691 [Google Scholar]
12. Wollmann CG, Thudt K, Kaiser B, Salomonowitz E, Mayr H, Globits S. Safe performance of magnetic resonance of the heart in patients with magnetic resonance conditional pacemaker systems: the safety issue of the ESTIMATE study. J Cardiovasc Magn Reson 2014; 16:30 [Google Scholar]
13. Nordbeck P, Ertl G, Ritter O. Magnetic resonance imaging safety in pacemaker and implantable cardioverter defibrillator patients: how far have we come? Eur Heart J 2015; 36:1505–1511 [Google Scholar]
14. Sheldon SH, Bunch TJ, Cogert GA, et al. Multicenter study of the safety and effects of magnetic resonance imaging in patients with coronary sinus left ventricular pacing leads. Heart Rhythm 2015; 12:345–349 [Google Scholar]
15. Russo RJ, Costa HS, Silva PD, et al. Assessing the risks associated with MRI in patients with a pacemaker or defibrillator. N Engl J Med 2017; 376:755–764 [Google Scholar]
16. Sasaki T, Hansford R, Zviman MM, et al. Quantitative assessment of artifacts on cardiac magnetic resonance imaging of patients with pacemakers and implantable cardioverter-defibrillators. Circ Cardiovasc Imaging 2011; 4:662–670 [Google Scholar]
17. Camacho JC, Moreno CC, Shah AD, et al. Safety and quality of 1.5-T MRI in patients with conventional and MRI-conditional cardiac implantable electronic devices after implementation of a standardized protocol. AJR 2016; 207:599–604 [Google Scholar]
18. Bertelsen L, Petersen HH, Philbert BT, Svendsen JH, Thomsen C, Vejlstrup N. Safety of magnetic resonance scanning without monitoring of patients with pacemakers. Europace 2016; 19:818–823 [Google Scholar]
19. Luechinger R, Zeijlemaker VA, Pedersen EM, et al. In vivo heating of pacemaker leads during magnetic resonance imaging. Eur Heart J 2005; 26:376–383; discussion, 325–377 [Google Scholar]
20. Shah ZK, Elias SN, Abaza R, et al. Performance comparison of 1.5-T endorectal coil MRI with 3.0-T nonendorectal coil MRI in patients with prostate cancer. Acad Radiol 2015; 22:467–474 [Google Scholar]
21. Park BK, Kim B, Kim CK, Lee HM, Kwon GY. Comparison of phased-array 3.0-T and endorectal 1.5-T magnetic resonance imaging in the evaluation of local staging accuracy for prostate cancer. J Comput Assist Tomogr 2007; 31:534–538 [Google Scholar]
22. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging–Reporting and Data System: 2015, Version 2. Eur Urol 2016; 69:16–40 [Google Scholar]
23. Kitajima K, Hartman RP, Froemming AT, Hagen CE, Takahashi N, Kawashima A. Detection of local recurrence of prostate cancer after radical prostatectomy using endorectal coil MRI at 3 T: addition of DWI and dynamic contrast enhancement to t2-weighted MRI. AJR 2015; 205:807–816 [Google Scholar]
24. Panebianco V, Barchetti F, Sciarra A, et al. Prostate cancer recurrence after radical prostatectomy: the role of 3-T diffusion imaging in multi-parametric magnetic resonance imaging. Eur Radiol 2013; 23:1745–1752 [Google Scholar]
Address correspondence to A. Kawashima ().

Full Text | PDF (822 KB) | PDF Plus (846 KB) | Supplemental Material




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 823-829
Posted online on February 4, 2019.
(https://doi.org/10.2214/AJR.18.20295) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

The Influence of Background Signal Intensity Changes on Cancer Detection in Prostate MRI

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 823-829. 10.2214/AJR.18.20295

ABSTRACT :

OBJECTIVE. The objective of this study was to develop a scoring system for background signal intensity changes or prostate homogeneity on prostate MRI and to assess these changes' influence on cancer detection.

MATERIALS AND METHODS. This institutional review board–approved, HIPAA-compliant, retrospective study included 418 prostate MRI examinations in 385 men who subsequently underwent MRI-guided biopsy. The Likert score for suspicion of cancer assigned by the primary radiologist was extracted from the original report, and histopathologic work-up of the biopsy cores served as the reference standard. Two readers assessed the amount of changes on T2-weighted sequences and assigned a predefined prostate signal-intensity homogeneity score of 1–5 (1 = poor, extensive changes; 5 = excellent, no changes). The sensitivity and specificity of Likert scores for detection of prostate cancer and clinically significant cancer (Gleason score ≥ 3+4) were estimated in and compared between subgroups of patients with different signal-intensity homogeneity scores (≤ 2, 3, and ≥ 4).

RESULTS. Interreader agreement on signal-intensity homogeneity scores was substantial (κ = 0.783). Sensitivity for prostate cancer detection increased when scores were better (i.e., higher) (reader 1, from 0.41 to 0.71; reader 2, from 0.53 to 0.73; p ≤ 0.007, both readers). In the detection of significant cancer (Gleason score ≥ 3+4), sensitivity also increased with higher signal-intensity scores (reader 1, from 0.50 to 0.82; reader 2, from 0.63 to 0.86; p ≤ 0.028), though specificity decreased significantly for one reader (from 0.67 to 0.38; p = 0.009).

CONCLUSION. Background signal-intensity changes on T2-weighted images significantly limit prostate cancer detection. The proposed scoring system could improve the standardization of prostate MRI reporting and provide guidance for applying prostate MRI results appropriately in clinical decision-making.

Keywords: MRI, prostate cancer

Supported in part by National Institutes of Health/National Cancer Institute Cancer Center Support Grant P30 CA008748.

Acknowledgment
Previous sectionNext section

We thank Ada Muellner for editing the manuscript.

References
Previous section
1. Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 2018; 378:1767–1777 [Google Scholar]
2. Ahmed HU, El-Shater Bosaily A, Brown LC, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 2017; 389:815–822 [Google Scholar]
3. Valerio M, Donaldson I, Emberton M, et al. Detection of clinically significant prostate cancer using magnetic resonance imaging-ultrasound fusion targeted biopsy: a systematic review. Eur Urol 2015; 68:8–19 [Google Scholar]
4. Li Y, Mongan J, Behr SC, et al. Beyond prostate adenocarcinoma: expanding the differential diagnosis in prostate pathologic conditions. RadioGraphics 2016; 36:1055–1075 [Google Scholar]
5. White S, Hricak H, Forstner R, et al. Prostate cancer: effect of postbiopsy hemorrhage on interpretation of MR images. Radiology 1995; 195:385–390 [Google Scholar]
6. Carney PA, Miglioretti DL, Yankaskas BC, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med 2003; 138:168–175 [Google Scholar]
7. Sfanos KS, Yegnasubramanian S, Nelson WG, de Marzo AM. The inflammatory microenvironment and microbiome in prostate cancer development. Nat Rev Urol 2018; 15:11–24 [Google Scholar]
8. Freer PE. Mammographic breast density: impact on breast cancer risk and implications for screening. RadioGraphics 2015; 35:302–315 [Google Scholar]
9. Park KK, Lee SH, Lim BJ, Kim JH, Chung BH. The effects of the period between biopsy and diffusion-weighted magnetic resonance imaging on cancer staging in localized prostate cancer. BJU Int 2010; 106:1148–1151 [Google Scholar]
10. Rosenkrantz AB, Mussi TC, Hindman N, et al. Impact of delay after biopsy and post-biopsy haemorrhage on prostate cancer tumour detection using multi-parametric MRI: a multi-reader study. Clin Radiol 2012; 67:e83–e90 [Google Scholar]
11. Tamada T, Sone T, Jo Y, et al. Prostate cancer: relationships between postbiopsy hemorrhage and tumor detectability at MR diagnosis. Radiology 2008; 248:531–539 [Google Scholar]
12. Berg WA, Zhang Z, Lehrer D, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA 2012; 307:1394–1404 [Google Scholar]
13. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging Reporting and Data System 2015, version 2. Eur Urol 2016; 69:16–40 [Google Scholar]
Address correspondence to A. M. Hötker ().

Full Text | PDF (781 KB) | PDF Plus (796 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 830-838
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20415) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Active Surveillance Versus Nephron-Sparing Surgery for a Bosniak IIF or III Renal Cyst: A Cost-Effectiveness Analysis

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 830-838. 10.2214/AJR.18.20415

ABSTRACT :

OBJECTIVE. The objective of our study was to evaluate the cost-effectiveness of active surveillance (AS) versus nephron-sparing surgery (NSS) in patients with a Bosniak IIF or III renal cyst.

MATERIALS AND METHODS. Markov models were developed to estimate life expectancy and lifetime costs for 60-year-old patients with a Bosniak IIF or III renal cyst (the reference cases) managed by AS versus NSS. The models incorporated the malignancy rates, reclassification rates during follow-up, treatment effectiveness, complications and costs, and short- and long-term outcomes. An incremental cost-effectiveness analysis was performed to identify management preference under an assumed $75,000 per quality-adjusted life-year (QALY) societal willingness-to-pay threshold, using data from studies in the literature and the 2015 Medicare Physician Fee Schedule. The effects of key parameters were addressed in a multiway sensitivity analysis.

RESULTS. The prevalence of malignancy for Bosniak IIF and III renal cysts was 26% (25/96) and 52% (542/1046). Under base case assumptions for Bosniak IIF cysts, the incremental cost-effectiveness ratio of NSS relative to AS was $731,309 per QALY for women, exceeding the assumed societal willingness-to-pay threshold, and AS outperformed NSS for both life expectancy and cost for men. For Bosniak III cysts, AS yielded greater life expectancy (24.8 and 19.4 more days) and lower lifetime costs (cost difference of $12,128 and $11,901) than NSS for men and women, indicating dominance of AS over NSS. Superiority of AS held true in sensitivity analyses for men 46 years old or older and women 57 years old or older even when all parameters were set to favor NSS.

CONCLUSION. AS is more cost-effective than NSS for patients with a Bosniak IIF or III renal cyst.

Keywords: active surveillance, Bosniak, cost-effectiveness analysis, renal cysts

References
Previous section
1. Bosniak MA. The current radiological approach to renal cysts. Radiology 1986; 158:1–10 [Google Scholar]
2. Bosniak MA. Problems in the radiologic diagnosis of renal parenchymal tumors. Urol Clin North Am 1993; 20:217–230 [Google Scholar]
3. Israel GM, Hindman N, Bosniak MA. Evaluation of cystic renal masses: comparison of CT and MR imaging by using the Bosniak classification system. Radiology 2004; 231:365–371 [Google Scholar]
4. Weibl P, Klatte T, Waldert M, Remzi M. Complex renal cystic masses: current standards and controversies. Int Urol Nephrol 2012; 44:13–18 [Google Scholar]
5. Park BK, Kim CK, Kim EY. Differentiation of Bosniak categories IIF and III cystic masses: what radiologists should know. J Comput Assist Tomogr 2010; 34:847–854 [Google Scholar]
6. Israel GM, Bosniak MA. Follow-up CT of moderately complex cystic lesions of the kidney (Bosniak category IIF). AJR 2003; 181:627–633 [Google Scholar]
7. Smith AD, Allen BC, Sanyal R, et al. Outcomes and complications related to the management of Bosniak cystic renal lesions. AJR 2015; 204:[web] W550–W556 [Google Scholar]
8. Silverman SG, Israel GM, Herts BR, Richie JP. Management of the incidental renal mass. Radiology 2008; 249:16–31 [Google Scholar]
9. Berland LL, Silverman SG, Gore RM, et al. Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. J Am Coll Radiol 2010; 7:754–773 [Google Scholar]
10. Oh TH, Seo IY. The role of Bosniak classification in malignant tumor diagnosis: a single institution experience. Investig Clin Urol 2016; 57:100–105 [Google Scholar]
11. Campbell SC, Novick AC, Belldegrun A, et al. Guideline for management of the clinical T1 renal mass. J Urol 2009; 182:1271–1279 [Google Scholar]
12. Campbell S, Uzzo RG, Allaf ME, et al. Renal mass and localized renal cancer: AUA Guideline. J Urol 2017; 198:520–529 [Google Scholar]
13. Herts BR, Silverman SG, Hindman NM, et al. Management of the incidental renal mass on CT: a white paper of the ACR Incidental Findings Committee. J Am Coll Radiol 2018; 15:264–273 [Google Scholar]
14. Smith AD, Remer EM, Cox KL, et al. Bosniak category IIF and III cystic renal lesions: outcomes and associations. Radiology 2012; 262:152–160 [Google Scholar]
15. Graumann O, Osther SS, Karstoft J, Horlyck A, Osther PJ. Evaluation of Bosniak category IIF complex renal cysts. Insights Imaging 2013; 4:471–480 [Google Scholar]
16. Goenka AH, Remer EM, Smith AD, Obuchowski NA, Klink J, Campbell SC. Development of a clinical prediction model for assessment of malignancy risk in Bosniak III renal lesions. Urology 2013; 82:630–635 [Google Scholar]
17. Webster WS, Thompson RH, Cheville JC, Lohse CM, Blute ML, Leibovich BC. Surgical resection provides excellent outcomes for patients with cystic clear cell renal cell carcinoma. Urology 2007; 70:900–904; discussion, 904 [Google Scholar]
18. El-Mokadem I, Budak M, Pillai S, Lang S, Doull R, Goodman C, et al. Progression, interobserver agreement, and malignancy rate in complex renal cysts (≥ Bosniak category IIF). Urol Oncol 2014; 32:24.e1–e7 [Google Scholar]
19. Borghesi M, Brunocilla E, Volpe A, et al. Active surveillance for clinically localized renal tumors: an updated review of current indications and clinical outcomes. Int J Urol 2015; 22:432–438 [Google Scholar]
20. Pandharipande PV, Gervais DA, Mueller PR, Hur C, Gazelle GS. Radiofrequency ablation versus nephron-sparing surgery for small unilateral renal cell carcinoma: cost-effectiveness analysis. Radiology 2008; 248:169–178 [Google Scholar]
21. Pandharipande PV, Gervais DA, Hartman RI, et al. Renal mass biopsy to guide treatment decisions for small incidental renal tumors: a cost-effectiveness analysis. Radiology 2010; 256:836–846 [Google Scholar]
22. Siegel JE, Weinstein MC, Russell LB, Gold MR. Recommendations for reporting cost-effectiveness analyses: Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996; 276:1339–1341 [Google Scholar]
23. Garber AM, Phelps CE. Economic foundations of cost-effectiveness analysis. J Health Econ 1997; 16:1–31 [Google Scholar]
24. Bambha K, Kim WR. Cost-effectiveness analysis and incremental cost-effectiveness ratios: uses and pitfalls. Eur J Gastroenterol Hepatol 2004; 16:519–526 [Google Scholar]
25. Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the Panel on Cost-Effectiveness in Health and Medicine. JAMA 1996; 276:1253–1258 [Google Scholar]
26. Gazelle GS, McMahon PM, Beinfeld MT, Halpern EF, Weinstein MC. Metastatic colorectal carcinoma: cost-effectiveness of percutaneous radiofrequency ablation versus that of hepatic resection. Radiology 2004; 233:729–739 [Google Scholar]
27. Fryback DG, Dasbach EJ, Klein R, et al. The Beaver Dam Health Outcomes Study: initial catalog of health-state quality factors. Med Decis Making 1993; 13:89–102 [Google Scholar]
28. Gillick MR. Medicare coverage for technological innovations: time for new criteria? N Engl J Med 2004; 350:2199–2203 [Google Scholar]
29. Neumann PJ, Rosen AB, Weinstein MC. Medicare and cost-effectiveness analysis. N Engl J Med 2005; 353:1516–1522 [Google Scholar]
30. Goldman L. Cost-effectiveness in a flat world: can ICDs help the United States get rhythm? N Engl J Med 2005; 353:1513–1515 [Google Scholar]
31. Winkelmayer WC, Weinstein MC, Mittleman MA, Glynn RJ, Pliskin JS. Health economic evaluations: the special case of end-stage renal disease treatment. Med Decis Making 2002; 22:417–430 [Google Scholar]
32. Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 2015; 67:913–924 [Google Scholar]
33. Hindman NM, Hecht EM, Bosniak MA. Follow-up for Bosniak category 2F cystic renal lesions. Radiology 2014; 272:757–766 [Google Scholar]
34. Manikandan R, Srinivasan V, Rane A. Which is the real gold standard for small-volume renal tumors? Radical nephrectomy versus nephron-sparing surgery. J Endourol 2004; 18:39–44 [Google Scholar]
35. Hubbard RA, Zhou XH. A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression. J Appl Stat 2011; 38:2313–2326 [Google Scholar]
36. Andersen PK, Borgan O, Gill RD, Keiding N. Statistical models based on counting processes. New York, NY: Springer Science & Business Media, 2012 [Google Scholar]
37. Spaliviero M, Herts BR, Magi-Galluzzi C, et al. Laparoscopic partial nephrectomy for cystic masses. J Urol 2005; 174:614–619 [Google Scholar]
38. Pinheiro T, Sepulveda F, Natalin RH, et al. Is it safe and effective to treat complex renal cysts by the laparoscopic approach? J Endourol 2011; 25:471–476 [Google Scholar]
39. Gill IS, Kavoussi LR, Lane BR, et al. Comparison of 1,800 laparoscopic and open partial nephrectomies for single renal tumors. J Urol 2007; 178:41–46 [Google Scholar]
40. Lane BR, Gill IS. 5-Year outcomes of laparoscopic partial nephrectomy. J Urol 2007; 177:70–74; discussion, 74 [Google Scholar]
41. Van Poppel H, Da Pozzo L, Albrecht W, et al.; European Organization for Research and Treatment of Cancer (EORTC); National Cancer Institute of Canada Clinical Trials Group (NCIC CTG); Southwest Oncology Group (SWOG); Eastern Cooperative Oncology Group (ECOG). A prospective randomized EORTC intergroup phase 3 study comparing the complications of elective nephron-sparing surgery and radical nephrectomy for low-stage renal cell carcinoma. Eur Urol 2007; 51:1606–1615 [Google Scholar]
42. Thompson RH, Atwell T, Schmit G, et al. Comparison of partial nephrectomy and percutaneous ablation for cT1 renal masses. Eur Urol 2015; 67:252–259 [Google Scholar]
43. Van Poppel H, Da Pozzo L, Albrecht W, et al. A prospective, randomised EORTC intergroup phase 3 study comparing the oncologic outcome of elective nephron-sparing surgery and radical nephrectomy for low-stage renal cell carcinoma. Eur Urol 2011; 59:543–552 [Google Scholar]
44. Cancer stat facts: kidney and renal pelvis cancer. National Cancer Institute SEER Program website.www.seer.cancer.gov/statfacts/html/kidrp.html. Accessed November 4, 2016 [Google Scholar]
45. Itano NB, Blute ML, Spotts B, Zincke H. Outcome of isolated renal cell carcinoma fossa recurrence after nephrectomy. J Urol 2000; 164:322–325 [Google Scholar]
46. Arias E. United States life tables, 2010. Natl Vital Stat Rep 2014; 63:1–63 [Google Scholar]
47. Chang SL, Cipriano LE, Harshman LC, Garber AM, Chung BI. Cost-effectiveness analysis of nephron sparing options for the management of small renal masses. J Urol 2011; 185:1591–1597 [Google Scholar]
48. Taplin SH, Barlow W, Urban N, et al. Stage, age, comorbidity, and direct costs of colon, prostate, and breast cancer care. J Natl Cancer Inst1995; 87:417–426 [Google Scholar]
49. Remák E, Charbonneau C, Negrier S, Kim ST, Motzer RJ. Economic evaluation of sunitinib ma-late for the first-line treatment of metastatic renal cell carcinoma. J Clin Oncol 2008; 26:3995–4000 [Google Scholar]
50. Centers for Medicare and Medicaid Services website. 2015 Physician fee schedule. www.cms.gov/apps/physician-fee-schedule/search/search-criteria.aspx. Accessed February 13, 2019 [Google Scholar]
51. Reese AC, Johnson PT, Gorin MA, et al. Pathological characteristics and radiographic correlates of complex renal cysts. Urol Oncol 2014; 32:1010–1016 [Google Scholar]
52. Raphel TJ, Weaver DT, Berland LL, et al. Imaging follow-up of low-risk incidental pancreas and kidney findings: effects of patient age and comorbidity on projected life expectancy. Radiology 2018; 287:504–514 [Google Scholar]
53. Sevcenco S, Spick C, Helbich TH, et al. Malignancy rates and diagnostic performance of the Bosniak classification for the diagnosis of cystic renal lesions in computed tomography: a systematic review and meta-analysis. Eur Radiol 2017; 27:2239–2247 [Google Scholar]
54. American College of Radiology website. ACR manual on contrast media v10.3. www.acr.org/Quality-Safety/Resources/Contrast-Manual. Published 2017. Accessed February 13, 2019 [Google Scholar]
55. Weibl P, Klatte T, Kollarik B, et al. Interpersonal variability and present diagnostic dilemmas in Bosniak classification system. Scand J Urol Nephrol 2011; 45:239–244 [Google Scholar]
56. Weibl P, Hora M, Kollarik B, Shariat SF, Klatte T. Management, pathology and outcomes of Bosniak category IIF and III cystic renal lesions. World J Urol 2015; 33:295–300 [Google Scholar]
57. Clevert DA, Minaifar N, Weckbach S, et al. Multislice computed tomography versus contrast-enhanced ultrasound in evaluation of complex cystic renal masses using the Bosniak classification system. Clin Hemorheol Microcirc 2008; 39:171–178 [Google Scholar]
58. Song C, Min GE, Song K, et al. Differential diagnosis of complex cystic renal mass using multi-phase computerized tomography. J Urol 2009; 181:2446–2450 [Google Scholar]
59. Han HH, Choi KH, Oh YT, Yang SC, Han WK. Differential diagnosis of complex renal cysts based on lesion size along with the Bosniak renal cyst classification. Yonsei Med J 2012; 53:729–733 [Google Scholar]
60. Hwang JH, Lee CK, Yu HS, Cho KS, Choi YD, Ham WS. Clinical outcomes of Bosniak Category IIF complex renal cysts in Korean patients. Korean J Urol 2012; 53:386–390 [Google Scholar]
61. Barr RG, Peterson C, Hindi A. Evaluation of indeterminate renal masses with contrast-enhanced US: a diagnostic performance study. Radiology 2014; 271:133–142 [Google Scholar]
62. Brown W, Amis E Jr, Kaplan S, Blaivas J, Axelrod S. Renal cystic lesions: predictive value of preoperative computerized tomography. (abstract) J Urol 1989; 141:426A [Google Scholar]
63. Aronson S, Frazier HA, Baluch JD, Hartman DS, Christenson PJ. Cystic renal masses: usefulness of the Bosniak classification. Urol Radiol1991; 13:83–90 [Google Scholar]
64. Wilson TE, Doelle EA, Cohan RH, Wojno K, Korobkin M. Cystic renal masses: a reevaluation of the usefulness of the Bosniak classification system. Acad Radiol 1996; 3:564–570 [Google Scholar]
65. Cloix P, Martin X, Pangaud C, et al. Surgical management of complex renal cysts: a series of 32 cases. J Urol 1996; 156:28–30 [Google Scholar]
66. Siegel CL, McFarland EG, Brink JA, Fisher AJ, Humphrey P, Heiken JP. CT of cystic renal masses: analysis of diagnostic performance and interobserver variation. AJR 1997; 169:813–818 [Google Scholar]
67. Koga S, Nishikido M, Inuzuka S, et al. An evaluation of Bosniak's radiological classification of cystic renal masses. BJU Int 2000; 86:607–609 [Google Scholar]
68. Curry NS, Cochran ST, Bissada NK. Cystic renal masses: accurate Bosniak classification requires adequate renal CT. AJR 2000; 175:339–342 [Google Scholar]
69. Limb J, Santiago L, Kaswick J, Bellman GC. Laparoscopic evaluation of indeterminate renal cysts: long-term follow-up. J Endourol 2002; 16:79–82 [Google Scholar]
70. Harisinghani MG, Maher MM, Gervais DA, et al. Incidence of malignancy in complex cystic renal masses (Bosniak category III): should imaging-guided biopsy precede surgery? AJR 2003; 180:755–758 [Google Scholar]
71. Kostiukov SI, Medvedev VL, Kogan MI. Diagnosis and laparoscopic treatment of renal cysts of Bosniak type III and IV (in Russian). Urologiia2008; 3:21–24 [Google Scholar]
72. Quaia E, Bertolotto M, Cioffi V, et al. Comparison of contrast-enhanced sonography with unenhanced sonography and contrast-enhanced CT in the diagnosis of malignancy in complex cystic renal masses. AJR 2008; 191:1239–1249 [Google Scholar]
73. O'Malley RL, Godoy G, Hecht EM, Stifelman MD, Taneja SS. Bosniak category IIF designation and surgery for complex renal cysts. J Urol2009; 182:1091–1095 [Google Scholar]
74. Kim DY, Kim JK, Min GE, Ahn HJ, Cho KS. Malignant renal cysts: diagnostic performance and strong predictors at MDCT. Acta Radiol 2010; 51:590–598 [Google Scholar]
75. You D, Shim M, Jeong IG, et al. Multilocular cystic renal cell carcinoma: clinicopathological features and preoperative prediction using multi-phase computed tomography. BJU Int 2011; 108:1444–1449 [Google Scholar]
76. Allen BC, Chen MY, Childs DD, Zagoria RJ. Imaging-guided radiofrequency ablation of cystic renal neoplasms. AJR 2013; 200:1365–1369 [Google Scholar]
77. Bata P, Tarnoki AD, Tarnoki DL, et al. Bosniak category III cysts are more likely to be malignant than we expected in the era of multidetector computed tomography technology. J Res Med Sci 2014; 19:634–638 [Google Scholar]
78. Graumann O, Osther SS, Karstoft J, Horlyck A, Osther PJ. Bosniak classification system: a prospective comparison of CT, contrast-enhanced US, and MR for categorizing complex renal cystic masses. Acta Radiol 2016; 57:1409–1417 [Google Scholar]
79. Ferreira AM, Reis RB, Kajiwara PP, Silva GE, Elias J Jr, Muglia VF. MRI evaluation of complex renal cysts using the Bosniak classification: a comparison to CT. Abdom Radiol (NY) 2016; 41:2011–2019 [Google Scholar]
80. Katayama H, Yamaguchi K, Kozuka T, Takashima T, Seez P, Matsuura K. Adverse reactions to ionic and nonionic contrast media: a report from the Japanese Committee on the Safety of Contrast Media. Radiology 1990; 175:621–628 [Google Scholar]
81. Lasser EC, Lyon SG, Berry CC. Reports on contrast media reactions: analysis of data from reports to the U.S. Food and Drug Administration. Radiology 1997; 203:605–610 [Google Scholar]
82. Ness RM, Holmes AM, Klein R, Dittus R. Utility valuations for outcome states of colorectal cancer. Am J Gastroenterol 1999; 94:1650–1657 [Google Scholar]
Address correspondence to A. D. Smith ().

A. D. Smith is president of Radiostics LLC and is president of and has patents issued and pending for eMASS LLC, Liver Nodularity LLC, and Color Enhanced Detection LLC.

Full Text | PDF (861 KB) | PDF Plus (937 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 839-846
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20498) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Prebiopsy Biparametric MRI for Clinically Significant Prostate Cancer Detection With PI-RADS Version 2: A Multicenter Study

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 839-846. 10.2214/AJR.18.20498

ABSTRACT :

OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) with respect to prebiopsy MRI with and without dynamic contrast enhancement in the detection of clinically significant cancer (CSC).

MATERIALS AND METHODS. A total of 113 patients with prostate cancer who underwent radical prostatectomy and prebiopsy multiparametric 3-T MRI (mpMRI) that included T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI (DCE-MRI) were enrolled in a retrospective study conducted at two institutions. For detecting CSC at prebiopsy mpMRI with DCE-MRI and biparametric MRI (bpMRI) without DCE-MRI, two independent radiologists using PI-RADSv2 scored suspicious lesions in all patients.

RESULTS. CSC was identified in 74.3% (84/113) of patients. For CSC detection rate, no statistical differences between bpMRI and mpMRI were found for any PI-RADS score (p > 0.05). For cancer in the peripheral zone, reader 1 upgraded 22 lesions and reader 2 upgraded 13 lesions from PI-RADS score 3 at bpMRI to PI-RADS 4 (3 + 1) at mpMRI. The CSC detection rate of PI-RADS 3 + 1 lesions at mpMRI (reader 1, 63.6%; reader 2, 69.2%) was slightly greater than that of PI-RADS 3 lesions at bpMRI (reader 1, 53.8%; reader 2, 60.0%), which was not statistically different (p > 0.05). Interreader agreement on PI-RADS scoring was moderate for both bpMRI (κ = 0.540) and mpMRI (κ = 0.478).

CONCLUSION. For detecting CSC, the diagnostic performance of prebiopsy bpMRI without DCE-MRI is similar to that of mpMRI with DCE-MRI.

Keywords: diagnosis, MRI, multicenter study, PI-RADS, prostate cancer

Supported by Samsung Biomedical Research Institute grant OTX0001931 and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1A2B4006020).

References
Previous section
1. Ahmed HU, El-Shater Bosaily A, Brown LC, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 2017; 389:815–822 [Google Scholar]
2. Radtke JP, Kuru TH, Boxler S, et al. Comparative analysis of transperineal template saturation prostate biopsy versus magnetic resonance imaging targeted biopsy with magnetic resonance imaging-ultrasound fusion guidance. J Urol 2015; 193:87–94 [Google Scholar]
3. Fütterer JJ, Briganti A, De Visschere P, et al. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 2015; 68:1045–1053 [Google Scholar]
4. Siddiqui MM, Rais-Bahrami S, Turkbey B, et al. Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA 2015; 313:390–397 [Google Scholar]
5. Porpiglia F, Manfredi M, Mele F, et al. Diagnostic pathway with multiparametric magnetic resonance imaging versus standard pathway: results from a randomized prospective study in biopsy-naive patients with suspected prostate cancer. Eur Urol 2017; 72:282–288 [Google Scholar]
6. Stanzione A, Imbriaco M, Cocozza S, et al. Biparametric 3T magnetic resonance imaging for prostatic cancer detection in a biopsy-naive patient population: a further improvement of PI-RADS v2? Eur J Radiol 2016; 85:2269–2274 [Google Scholar]
7. Washino S, Okochi T, Saito K, et al. Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naive patients. BJU Int 2017; 119:225–233 [Google Scholar]
8. Hoeks CM, Somford DM, van Oort IM, et al. Value of 3-T multiparametric magnetic resonance imaging and magnetic resonance-guided biopsy for early risk restratification in active surveillance of low-risk prostate cancer: a prospective multicenter cohort study. Invest Radiol 2014; 49:165–172 [Google Scholar]
9. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS prostate imaging-reporting and data system: 2015, version 2. Eur Urol 2016; 69:16–40 [Google Scholar]
10. Kuhl CK, Bruhn R, Kramer N, Nebelung S, Heidenreich A, Schrading S. Abbreviated biparametric prostate MR imaging in men with elevated prostate-specific antigen. Radiology 2017; 285:493–505 [Google Scholar]
11. Jambor I, Bostrom PJ, Taimen P, et al. Novel biparametric MRI and targeted biopsy improves risk stratification in men with a clinical suspicion of prostate cancer (IMPROD Trial). J Magn Reson Imaging 2017; 46:1089–1095 [Google Scholar]
12. Greer MD, Shih JH, Lay N, et al. Validation of the dominant sequence paradigm and role of dynamic contrast-enhanced imaging in PI-RADS version 2. Radiology 2017; 285:859–869 [Google Scholar]
13. Druskin SC, Ward R, Purysko AS, et al. Dynamic contrast enhanced magnetic resonance imaging improves classification of prostate lesions: a study of pathological outcomes on targeted prostate biopsy. J Urol 2017; 198:1301–1308 [Google Scholar]
14. Yoshizako T, Wada A, Hayashi T, et al. Usefulness of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in the diagnosis of prostate transition-zone cancer. Acta Radiol 2008; 49:1207–1213 [Google Scholar]
15. Tewes S, Mokov N, Hartung D, et al. Standardized reporting of prostate MRI: comparison of the Prostate Imaging Reporting and Data System (PI-RADS) version 1 and version 2. PLoS One 2016; 11:e0162879 [Google Scholar]
16. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44:837–845 [Google Scholar]
17. Rosenkrantz AB, Verma S, Choyke P, et al. Prostate magnetic resonance imaging and magnetic resonance imaging targeted biopsy in patients with a prior negative biopsy: a consensus statement by AUA and SAR. J Urol 2016; 196:1613–1618 [Google Scholar]
18. Padhani AR, Petralia G, Sanguedolce F. Magnetic resonance imaging before prostate biopsy: time to talk. Eur Urol 2016; 69:1–3 [Google Scholar]
19. Venderink W, van Luijtelaar A, Bomers JG, et al. Results of targeted biopsy in men with magnetic resonance imaging lesions classified equivocal, likely or highly likely to be clinically significant prostate cancer. Eur Urol 2017 Feb 28 [Epub ahead of print] [Google Scholar]
20. Felker ER, Raman SS, Margolis DJ, et al. Risk stratification among men with Prostate Imaging Reporting and Data System version 2 category 3 transition zone lesions: is biopsy always necessary? AJR 2017; 209:1272–1277 [Google Scholar]
21. Brizmohun Appayya M, Sidhu HS, Dikaios N, et al. Characterizing indeterminate (Likert-scored 3/5) peripheral zone prostate lesions with PSA density, PI-RADS scoring and qualitative descriptors on multi-parametric MRI. Br J Radiol 2018; 91:20170645 [Google Scholar]
22. Zhao C, Gao G, Fang D, et al. The efficiency of multiparametric magnetic resonance imaging (mpMRI) using PI-RADS version 2 in the diagnosis of clinically significant prostate cancer. Clin Imaging 2016; 40:885–888 [Google Scholar]
23. Rosenkrantz AB, Babb JS, Taneja SS, Ream JM. Proposed adjustments to PI-RADS version 2 decision rules: impact on prostate cancer detection. Radiology 2017; 283:119–129 [Google Scholar]
24. Grey AD, Chana MS, Popert R, Wolfe K, Liyanage SH, Acher PL. Diagnostic accuracy of magnetic resonance imaging (MRI) prostate imaging reporting and data system (PI-RADS) scoring in a transperineal prostate biopsy setting. BJU Int 2015; 115:728–735 [Google Scholar]
25. Ito K. Prostate cancer in Asian men. Nat Rev Urol 2014; 11:197–212 [Google Scholar]
26. Rosenkrantz AB, Ginocchio LA, Cornfeld D, et al. Interobserver reproducibility of the PI-RADS version 2 lexicon: a multicenter study of six experienced prostate radiologists. Radiology 2016; 280:793–804 [Google Scholar]
27. Glazer DI, Mayo-Smith WW, Sainani NI, et al. Interreader agreement of Prostate Imaging Reporting and Data System version 2 using an in-bore MRI-guided prostate biopsy cohort: a single institution's initial experience. AJR 2017; 209:[web]W145–W151 [Google Scholar]
28. Chen F, Cen S, Palmer S. Application of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2): interobserver agreement and positive predictive value for localization of intermediate- and high-grade prostate cancers on multiparametric magnetic resonance imaging. Acad Radiol 2017; 24:1101–1106 [Google Scholar]
29. Scialpi M, Aisa MC, D'Andrea A, Martorana E. Simplified Prostate Imaging Reporting and Data System for biparametric prostate MRI: a proposal. AJR 2018; 211:379–382 [Google Scholar]
30. NiMhurchu E, O'Kelly F, Murphy IG, et al. Predictive value of PI-RADS classification in MRI-directed transrectal ultrasound guided prostate biopsy. Clin Radiol 2016; 71:375–380 [Google Scholar]
Address correspondence to C. K. Kim ().

Full Text | PDF (982 KB) | PDF Plus (1012 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 847-854
Posted online on February 26, 2019.
(https://doi.org/10.2214/AJR.18.20571) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

A Systematic Review of the Existing Prostate Imaging Reporting and Data System Version 2 (PI-RADSv2) Literature and Subset Meta-Analysis of PI-RADSv2 Categories Stratified by Gleason Scores

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 847-854. 10.2214/AJR.18.20571

ABSTRACT :

OBJECTIVE. The objective of this study was to quantitatively and qualitatively assess the methodologic heterogeneity of the current Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) literature and estimate the proportions of Gleason scores (GSs) diagnosed across PI-RADSv2 categories.

MATERIALS AND METHODS. This study was a systematic review and meta-analysis and was performed in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only English-language studies and studies published before April 1, 2018, were assessed. The primary outcome of the meta-analysis was the estimated percentage of patients with GS ≥ 3 + 4 within each individual PI-RADSv2 score. We calculated the pooled estimates and 95% CIs on the basis of a random-effects model using the meta-analysis routine of Stata (version 13.1).

RESULTS. Our search revealed 434 titles, and 59 of these studies were selected. These studies were remarkable for their technical and terminological diverseness. Thirteen studies had sufficient data to be included in the meta-analysis. The prevalence of ≥ GS 3 + 4 in lesions assigned a PI-RADSv2 score of 3 or higher was approximately 45%. Lesions assigned PI-RADSv2 scores 1 or 2, 3, 4, and 5 represented high-grade disease in approximately 6%, 12%, 48%, and 72% of patients.

CONCLUSION. The data available in the literature are highly heterogeneous and challenging to analyze because of variations in terminology, patient cohort selection, criteria, imaging parameters, and reference standards. In spite of this heterogeneity, our meta-analysis shows that PI-RADSv2 has good sensitivity when a score of ≥ 3 is considered as a positive test.

Keywords: multiparametric MRI (mpMRI), prostate imaging, Prostate Imaging Reporting and Data System (PI-RADS), Prostate Imaging Reporting and Data System version 2 (PI-RADSv2)

A. C. Westphalen is a member of the scientific advisory board for 3D Biopsy, LLC.

References
Previous section
1. American Cancer Society website. Cancer facts and figures 2018. www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2018/cancer-facts-and-figures-2018.pdf. Published 2018. Accessed December 3, 2018 [Google Scholar]
2. Ferlay J, Soerjomataram I, Ervik M, eds. GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide in 2012. Lyon, France: International Agency for Research on Cancer. Published 2012 [Google Scholar]
3. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging Reporting and Data System: 2015, version 2. Eur Urol 2016; 69:16–40 [Google Scholar]
4. Sheridan AD, Nath SK, Aneja S, et al. MRI-ultra-sound fusion targeted biopsy of Prostate Imaging Reporting and Data System version 2 category 5 lesions found false-positive at multiparametric prostate MRI. AJR 2018; 210:[web]W218–W225 [Google Scholar]
5. Hoffmann R, Logan C, O'Callaghan M, Gormly K, Chan K, Foreman D. Does the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 improve accuracy in reporting anterior lesions on multiparametric magnetic resonance imaging (mpMRI)? Int Urol Nephrol 2018; 50:13–19 [Google Scholar]
6. Sheridan AD, Nath SK, Syed JS, et al. Risk of clinically significant prostate cancer associated with Prostate Imaging Reporting and Data System category 3 (equivocal) lesions identified on multi-parametric prostate MRI. AJR 2018; 210:347–357 [Google Scholar]
7. Song W, Bang SH, Jeon HG, et al. Role of PI-RADS version 2 for prediction of upgrading in biopsy-proven prostate cancer with Gleason score 6. Clin Genitourin Cancer 2018; 16:281–287 [Google Scholar]
8. Xu N, Wu YP, Chen DN, et al. Can Prostate Imaging Reporting and Data System version 2 reduce unnecessary prostate biopsies in men with PSA levels of 4-10 ng/mL? J Cancer Res Clin Oncol 2018; 144:987–995 [Google Scholar]
9. Ullrich T, Quentin M, Arsov C, et al. Risk stratification of equivocal lesions on multiparametric magnetic resonance imaging of the prostate. J Urol2018; 199:691–698 [Google Scholar]
10. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 2009; 6:e1000100 [Google Scholar]
11. Shankar PR, Curci NE, Davenport MS. Characteristics of PI-RADS 4 lesions within the prostatic peripheral zone: a retrospective diagnostic accuracy study evaluating 170 lesions. Abdom Radiol (NY) 2017; 43:2176–2182 [Google Scholar]
12. Hakozaki Y, Matsushima H, Kumagai J, et al. A prospective study of magnetic resonance imaging and ultrasonography (MRI/US)-fusion targeted biopsy and concurrent systematic transperineal biopsy with the average of 18-cores to detect clinically significant prostate cancer. BMC Urol 2017; 17:117 [Google Scholar]
13. Tewes S, Mokov N, Hartung D, et al. Standardized reporting of prostate MRI: comparison of the Prostate Imaging Reporting and Data System (PI-RADS) version 1 and version 2. PLoS One 2016; 11:e0162879 [Google Scholar]
14. Borofsky S, George AK, Gaur S, et al. What are we missing? False-negative cancers at multipara-metric MR imaging of the prostate. Radiology2018; 286:186–195 [Google Scholar]
15. Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ 2001; 322:1479–1480 [Google Scholar]
16. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327:557–560 [Google Scholar]
17. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ 2011; 342:d549 [Google Scholar]
18. Mehralivand S, Bednarova S, Shih JH, et al. Prospective evaluation of PI-RADS version 2 using the International Society of Urological Pathology Prostate Cancer Grade Group System. J Urol 2017; 198:583–590 [Google Scholar]
19. Tan N, Lin WC, Khoshnoodi P, et al. In-bore 3-T MR-guided transrectal targeted prostate biopsy: Prostate Imaging Reporting and Data System version 2-based diagnostic performance for detection of prostate cancer. Radiology 2017; 283:130–139 [Google Scholar]
20. Woo S, Kim SY, Lee J, Kim SH, Cho JY. PI-RADS version 2 for prediction of pathological downgrading after radical prostatectomy: a preliminary study in patients with biopsy-proven Gleason Score 7 (3 + 4) prostate cancer. Eur Radiol 2016; 26:3580–3587 [Google Scholar]
21. An JY, Sidana A, Holzman SA, et al. Ruling out clinically significant prostate cancer with negative multi-parametric MRI. Int Urol Nephrol 2018; 50:7–12 [Google Scholar]
22. Martorana E, Pirola GM, Scialpi M, et al. Lesion volume predicts prostate cancer risk and aggressiveness: validation of its value alone and matched with prostate imaging reporting and data system score. BJU Int 2017; 120:92–103 [Google Scholar]
23. Hansen NL, Kesch C, Barrett T, et al. Multicentre evaluation of targeted and systematic biopsies using magnetic resonance and ultrasound image-fusion guided transperineal prostate biopsy in patients with a previous negative biopsy. BJU Int 2017; 120:631–638 [Google Scholar]
24. Felker ER, Raman SS, Margolis DJ, et al. Risk stratification among men with Prostate Imaging Reporting and Data System version 2 category 3 transition zone lesions: is biopsy always necessary? AJR 2017; 209:1272–1277 [Google Scholar]
25. Lim CS, McInnes MD, Lim RS, et al. Prognostic value of Prostate Imaging and Data Reporting System (PI-RADS) v. 2 assessment categories 4 and 5 compared to histopathological outcomes after radical prostatectomy. J Magn Reson Imaging 2017; 46:257–266 [Google Scholar]
26. Esses SJ, Taneja SS, Rosenkrantz AB. Imaging facilities' adherence to PI-RADSv2 minimum technical standards for the performance of prostate MRI. Acad Radiol 2018; 25:188–195 [Google Scholar]
27. D'Orsi CJ, Mendelson EB, Morris EA, et al. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology, 2013 [Google Scholar]
28. Siddiqui MM, Rais-Bahrami S, Turkbey B, et al. Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA 2015; 313:390–397 [Google Scholar]
29. Wysock JS, Rosenkrantz AB, Huang WC, et al. A prospective, blinded comparison of magnetic resonance (MR) imaging-ultrasound fusion and visual estimation in the performance of MR-targeted prostate biopsy: the PROFUS trial. Eur Urol 2014; 66:343–351 [Google Scholar]
30. Venderink W, van der Leest M, van Luijtelaar A, et al. Retrospective comparison of direct in-bore magnetic resonance imaging (MRI)-guided biopsy and fusion-guided biopsy in patients with MRI lesions which are likely or highly likely to be clinically significant prostate cancer. World J Urol2017; 35:1849–1855 [Google Scholar]
31. Rosenkrantz AB, Ginocchio LA, Cornfeld D, et al. Interobserver reproducibility of the PI-RADS version 2 lexicon: a multicenter study of six experienced prostate radiologists. Radiology 2016; 280:793–804 [Google Scholar]
Address correspondence to A. C. Westphalen ().

Full Text | PDF (914 KB) | PDF Plus (943 KB) | Supplemental Material




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W100-W105
Posted online on February 4, 2019.
(https://doi.org/10.2214/AJR.18.20527) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Genitourinary Imaging

Original Research

Diagnostic Accuracy of Dual-Energy CT for Evaluation of Renal Masses: Systematic Review and Meta-Analysis

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: W100-W105. 10.2214/AJR.18.20527

ABSTRACT :

OBJECTIVE. The purpose of this study is to determine the diagnostic accuracy of dual-energy CT (DECT) using quantitative iodine concentration in patients with renal masses using histopathologic analysis or follow-up imaging as the reference standard. The secondary objective is to compare the accuracy of DECT (using iodine concentration) to that of conventional CT (using Hounsfield unit measurements).

MATERIALS AND METHODS. We searched the MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases for studies evaluating the accuracy of DECT for renal mass characterization (1947–2018). To be included, studies had to evaluate quantitative iodine concentrations in human patients with indeterminate renal masses. Risk of bias and applicability were assessed using quality assessment of diagnostic accuracy studies–2. A bivariate random-effects model was used to determine pooled sensitivity and specificity. Variability was assessed by subgroup analyses (DECT technique and risk of bias) and metaregression using test type and threshold applied as covariates.

RESULTS. Of 201 studies identified, five were included (367 patients). Pooled sensitivity and specificity for DECT were 96.6% (95% CI, 85.9–99.3%) and 95.1% (95% CI, 90.7–97.5%), respectively. Metaregression evaluating the influence of the test type (DECT vs conventional CT) did not identify differences in accuracy (p = 0.06). No differences in accuracy based on risk of bias or DECT technique were identified. Limitations include the small number of studies, most of which were at risk of bias.

CONCLUSION. DECT with iodine quantification shows sensitivity and specificity greater than 95% for evaluation of renal masses and may be an alternative to conventional CT for assessment of renal masses. Larger scale trials are needed to corroborate our findings.

Keywords: kidney neoplasms, meta-analysis, routine diagnostic tests, x-ray CT

WEB

This is a web exclusive article.

References
Previous section
1. Heilbrun ME, Remer EM, Casalino DD, et al. ACR Appropriateness Criteria indeterminate renal mass. J Am Coll Radiol 2015; 12:333–341 [Google Scholar]
2. Hakim SW, Schieda N, Hodgdon T, McInnes MD, Dilauro M, Flood TA. Angiomyolipoma (AML) without visible fat: ultrasound, CT and MR imaging features with pathological correlation. Eur Radiol 2016; 26:592–600 [Google Scholar]
3. O'Connor SD, Silverman SG, Ip IK, Maehara CK, Khorasani R. Simple cyst-appearing renal masses at unenhanced CT: can they be presumed to be benign? Radiology 2013; 269:793–800 [Google Scholar]
4. Schieda N, Vakili M, Dilauro M, Hodgdon T, Flood TA, Shabana WM. Solid renal cell carcinoma measuring water attenuation (−10 to 20 HU) on unenhanced CT. AJR 2015; 205:1215–1221 [Google Scholar]
5. Schieda N, Kielar AZ, Al Dandan O, McInnes MD, Flood TA. Ten uncommon and unusual variants of renal angiomyolipoma (AML): radiologicpathologic correlation. Clin Radiol 2015; 70:206–220 [Google Scholar]
6. Krishna S, Murray CA, McInnes MD, et al. CT imaging of solid renal masses: pitfalls and solutions. Clin Radiol 2017; 72:708–721 [Google Scholar]
7. Maki DD, Birnbaum BA, Chakraborty DP, Jacobs JE, Carvalho BM, Herman GT. Renal cyst pseudoenhancement: beam-hardening effects on CT numbers. Radiology 1999; 213:468–472 [Google Scholar]
8. Israel GM, Bosniak MA. Pitfalls in renal mass evaluation and how to avoid them. RadioGraphics 2008; 28:1325–1338 [Google Scholar]
9. Dilauro M, Quon M, McInnes MD, et al. Comparison of contrast-enhanced multiphase renal protocol CT versus MRI for diagnosis of papillary renal cell carcinoma. AJR 2016; 206:319–325 [Google Scholar]
10. Egbert ND, Caoili EM, Cohan RH, et al. Differentiation of papillary renal cell carcinoma subtypes on CT and MRI. AJR 2013; 201:347–355 [Google Scholar]
11. Connolly MJ, McInnes MDF, El-Khodary M, Mc-Grath TA, Schieda N. Diagnostic accuracy of virtual non-contrast enhanced dual-energy CT for diagnosis of adrenal adenoma: a systematic review and meta-analysis. Eur Radiol 2017; 27:4324–4335 [Google Scholar]
12. Mileto A, Nelson RC, Samei E, et al. Impact of dual-energy multi-detector row CT with virtual monochromatic imaging on renal cyst pseudoenhancement: in vitro and in vivo study. Radiology 2014; 272:767–776 [Google Scholar]
13. Mileto A, Nelson RC, Paulson EK, Marin D. Dual-energy MDCT for imaging the renal mass. AJR 2015; 204:[web]W640–W647 [Google Scholar]
14. Kaza RK, Ananthakrishnan L, Kambadakone A, Platt JF. Update of dual-energy CT applications in the genitourinary tract. AJR 2017; 208:1185–1192 [Google Scholar]
15. Manoharan D, Sharma S, Das CJ, Kumar R, Singh G, Kumar P. Single-acquisition triple-bolus dual-energy CT protocol for comprehensive evaluation of renal masses: a single-center randomized non-inferiority trial. AJR 2018; 211:[web]W22–W32 [Google Scholar]
16. Kaza RK, Caoili EM, Cohan RH, Platt JF. Distinguishing enhancing from nonenhancing renal lesions with fast kilovoltage-switching dual-energy CT. AJR 2011; 197:1375–1381 [Google Scholar]
17. Mileto A, Marin D, Ramirez-Giraldo JC, et al. Accuracy of contrast-enhanced dual-energy MDCT for the assessment of iodine uptake in renal lesions. AJR 2014; 202:[web]W466–W474 [Google Scholar]
18. Marin D, Davis D, Roy Choudhury K, et al. Characterization of small focal renal lesions: diagnostic accuracy with single-phase contrast-enhanced dual-energy CT with material attenuation analysis compared with conventional attenuation measurements. Radiology 2017; 284:737–747 [Google Scholar]
19. Chandarana H, Megibow AJ, Cohen BA, et al. Iodine quantification with dual-energy CT: phantom study and preliminary experience with renal masses. AJR 2011; 196:[web]W693–W700 [Google Scholar]
20. Zarzour JG, Milner D, Valentin R, et al. Quantitative iodine content threshold for discrimination of renal cell carcinomas using rapid kV-switching dual-energy CT. Abdom Radiol (NY) 2017; 42:727–734 [Google Scholar]
21. McInnes MDF, Moher D, Thombs BD, et al. Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 2018; 319:388–396 [Google Scholar]
22. McGrath TA, Alabousi M, Skidmore B, et al. Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review. Syst Rev 2017; 6:194 [Google Scholar]
23. deVet HCWEA, Riphagen II, Aertgeerts B, Pewsner D. Cochrane handbook for systematic reviews of diagnostic test accuracy, 0.4 ed. Cochrane Collaboration website. training.cochrane.org/resource/cochrane-handbook-systematic-reviews-diagnostic-test-accuracy. Published 2008. Accessed November 9, 2018 [Google Scholar]
24. McInnes MD, Bossuyt PM. Pitfalls of systematic reviews and meta-analyses in imaging research. Radiology 2015; 277:13–21 [Google Scholar]
25. Sharifabadi AD, Korevaar DA, McGrath TA, et al. Reporting bias in imaging: higher accuracy is linked to faster publication. Eur Radiol 2018; 28:3632–3639 [Google Scholar]
26. McGrath TA, McInnes MDF, van Es N, Leeflang MMG, Korevaar DA, Bossuyt PMM. Overinterpretation of research findings: evidence of "spin" in systematic reviews of diagnostic accuracy studies. Clin Chem 2017; 63:1353–1362 [Google Scholar]
27. Alabousi M, Alabousi A, McGrath T, et al. Epidemiology of systematic reviews in imaging journals. Eur Radiol 2018 Jul 26 [Epub ahead of print] [Google Scholar]
28. Krishna S, Sadoughi N, McInnes MD, Chatelain R, MacDonald DB, Schieda N. Attenuation and degree of enhancement with conventional 120-kVp polychromatic CT and 70-keV monochromatic rapid kilovoltage-switching dual-energy CT in cystic and solid renal masses. AJR 2018; 211:789–796 [Google Scholar]
29. American College of Radiology (ACR). ACR Appropriateness Criteria: indeterminate renal mass. ACR website.acsearch.acr.org/docs/69367/Narrative/. Published 1996. Updated 2014. Accessed November 9, 2018 [Google Scholar]
30. Al Harbi F, Tabatabaeefar L, Jewett MA, Finelli A, O'Malley M, Atri M. Enhancement threshold of small (< 4 cm) solid renal masses on CT. AJR2016; 206:554–558 [Google Scholar]
31. Israel GM, Bosniak MA. How I do it: evaluating renal masses. Radiology 2005; 236:441–450 [Google Scholar]
32. Doebler P, Holling H. Meta-analysis of diagnostic accuracy with mada. R Project website.cran.rproject.org/web/packages/mada/vignettes/mada.pdf. Published 2017. Accessed November 9, 2018 [Google Scholar]
33. Brown CL, Hartman RP, Dzyubak OP, et al. Dual-energy CT iodine overlay technique for characterization of renal masses as cyst or solid: a phantom feasibility study. Eur Radiol 2009; 19:1289–1295 [Google Scholar]
34. Graser A, Becker CR, Staehler M, et al. Single-phase dual-energy CT allows for characterization of renal masses as benign or malignant. Invest Radiol 2010; 45:399–405 [Google Scholar]
35. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155:529–536 [Google Scholar]
36. Leeflang MM, Moons KG, Reitsma JB, Zwinderman AH. Bias in sensitivity and specificity caused by data-driven selection of optimal cutoff values: mechanisms, magnitude, and solutions. Clin Chem 2008; 54:729–737 [Google Scholar]
37. Ascenti G, Mileto A, Krauss B, et al. Distinguishing enhancing from nonenhancing renal masses with dual-source dual-energy CT: iodine quantification versus standard enhancement measurements. Eur Radiol 2013; 23:2288–2295 [Google Scholar]
Address correspondence to M. D. F. McInnes ().

Full Text | PDF (708 KB) | PDF Plus (740 KB) | Supplemental Material




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 855-858
Posted online on February 26, 2019.
(https://doi.org/10.2214/AJR.18.20459) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Health Care Policy and Quality

Original Research

Optimization of MRI Turnaround Times Through the Use of Dockable Tables and Innovative Architectural Design Strategies

Share Share Claim CREDIT
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 855-858. 10.2214/AJR.18.20459

ABSTRACT :

OBJECTIVE. The purpose of this study is to increase the value of MRI by reengineering the MRI workflow at a new imaging center to shorten the interval (i.e., turnaround time) between each patient examination by at least 5 minutes.

MATERIALS AND METHODS. The elements of the MRI workflow that were optimized included the use of dockable tables, the location of patient preparation rooms, the number of doors per scanning room, and the storage location and duplication of coils. Turnaround times at the new center and at two existing centers were measured both for all patients and for situations when the next patient was ready to be brought into the scanner room after the previous patient's examination was completed.

RESULTS. Workflow optimizations included the use of dockable tables, dedicated patient preparation rooms, two doors in each MRI room, positioning the scanner to provide the most direct path to the scanner, and coil storage in the preparation rooms, with duplication of the most frequently used coils. At the new imaging center, the median and mean (± SD) turnaround times for situations in which patients were ready for scanning were 115 seconds (95% CI, 113–117 seconds) and 132 ± 72 seconds (95% CI, 129–135 seconds), respectively, and the median and mean turnaround times for all situations were 141 seconds (95% CI, 137–146 seconds) and 272 ± 270 seconds (95% CI, 263–282 seconds), respectively. For existing imaging centers, the median and mean turnaround times for situations in which patients were ready for scanning were 430 seconds (95% CI, 424–434 seconds) and 460 ± 156 seconds (95% CI, 455–465 seconds), respectively, and the median and mean turnaround times for all situations were 481 seconds (95% CI, 474–486 seconds) and 537 ± 219 seconds (95% CI, 532–543 seconds), respectively.

CONCLUSION. The optimized MRI workflow resulted in a mean time savings of 5 minutes 28 seconds per patient.

Keywords: dockable table, MRI, turnaround time, value

References
Previous section
1. Miles KA, Voo SA, Groves AM. Additional clinical value for PET/MRI in oncology: moving beyond simple diagnosis. J Nucl Med 2018; 59:1028–1032 [Google Scholar]
2. Cheung YY. No more waits and delays: streamlining workflow to decrease patient time of stay for image guided musculoskeletal procedures. RadioGraphics 2016; 36:856–871 [Google Scholar]
3. Beker K, Garces-Descovich A, Mangosing J, Cabral-Goncalves I, Hallett D, Mortele KJ. Optimizing MRI logistics: prospective analysis of performance, efficiency, and patient throughput. AJR 2017; 209:836–844 [Google Scholar]
4. Stattaus J, Maderwald S, Forsting M, Barkhausen J, Ladd ME. MR-guided core biopsy with MR fluoroscopy using a short, wide-bore 1.5-Tesla scanner: feasibility and initial results. J Magn Reson Imaging 2008; 27:1181–1187 [Google Scholar]
5. Zhang B, Sodickson DK, Cloos MA. A high-impedance detector-array glove for magnetic resonance imaging of the hand. Nat Biomed Eng 2018; 2:570–577 [Google Scholar]
6. Chandarana H, Feng L, Block TK, et al. Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling. Invest Radiol 2013; 48:10–16 [Google Scholar]
7. Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2017; 45:966–987 [Google Scholar]
8. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018; 79:3055–3071 [Google Scholar]
9. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013; 495:187–192 [Google Scholar]
10. Roth CJ, Boll DT, Wall LK, Merkle EM. Evaluation of MRI acquisition workflow with Lean Six Sigma method: case study of liver and knee examinations. AJR 2010; 195:[web]W187–W192 [Google Scholar]
11. Recht M, Macari M, Lawson K, et al. Impacting key performance indicators in an academic MR imaging department through process improvement. J Am Coll Radiol 2013; 10:202–206 [Google Scholar]
Address correspondence to M. P. Recht ().

Full Text | PDF (648 KB) | PDF Plus (660 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 859-866
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.19931) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Health Care Policy and Quality

Original Research

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 859-866. 10.2214/AJR.18.19931

ABSTRACT :

OBJECTIVE. Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources.

MATERIALS AND METHODS. Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1–9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non–house staff. Generalized linear models further estimated the modifying effect of the house staff variable.

RESULTS. Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17–0.64) and non–house staff (baseline increase, 0.58; 95% CI, 0.34–0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period.

CONCLUSION. Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non–house staff.

Keywords: appropriateness, clinical decision support, house staff, overutilization, radiology

References
Previous section
1. Ip IK, Raja AS, Gupta A, Andruchow J, Sodickson A, Khorasani R. Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED. Am J Emerg Med 2015; 33:320–325 [Google Scholar]
2. Hussey PS, Timbie JW, Burgette LF, Wenger NS, Nyweide DJ, Kahn KL. Appropriateness of advanced diagnostic imaging ordering before and after implementation of clinical decision support systems. JAMA 2015; 313:2181–2182 [Google Scholar]
3. Solberg LI, Wei F, Butler JC, Palattao KJ, Vinz CA, Marshall MA. Effects of electronic decision support on high-tech diagnostic imaging orders and patients. Am J Manag Care 2010; 16:102–106 [Google Scholar]
4. Lehnert BE, Bree RL. Analysis of appropriateness of outpatient CT and MRI referred from primary care clinics at an academic medical center: how critical is the need for improved decision support? J Am Coll Radiol 2010; 7:192–197 [Google Scholar]
5. Goldzweig CL, Orshansky G, Paige NM, et al. Electronic health record-based interventions for improving appropriate diagnostic imaging: a systematic review and meta-analysis. Ann Intern Med 2015; 162:557–565 [Google Scholar]
6. Gupta A, Ip IK, Raja AS, Andruchow JE, Sodickson A, Khorasani R. Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc 2014; 21:e347–e351 [Google Scholar]
7. Miyakis S, Karamanof G, Liontos M, Mountokalakis TD. Factors contributing to inappropriate ordering of tests in an academic medical department and the effect of an educational feedback strategy. Postgrad Med J 2006; 82:823–829 [Google Scholar]
8. Magin PJ, Morgan S, Tapley A, et al. Reducing general practice trainees' antibiotic prescribing for respiratory tract infections: an evaluation of a combined face-to-face workshop and online educational intervention. Educ Prim Care 2016; 27:98–105 [Google Scholar]
9. Fung D, Schabort I, MacLean CA, et al. Test ordering for preventive health care among family medicine residents. Can Fam Physician 2015; 61:256–262 [Google Scholar]
10. Moriarity AK, Klochko C, O'Brien M, Halabi S. The effect of clinical decision support for advanced inpatient imaging. J Am Coll Radiol 2015; 12:358–363 [Google Scholar]
11. Vidyarthi AR, Hamill T, Green AL, Rosenbluth G, Baron RB. Changing resident test ordering behavior: a multilevel intervention to decrease laboratory utilization at an academic medical center. Am J Med Qual 2015; 30:81–87 [Google Scholar]
12. Sood R, Sood A, Ghosh AK. Non-evidence-based variables affecting physicians' test-ordering tendencies: a systematic review. Neth J Med2007; 65:167–177 [Google Scholar]
13. Sedrak MS, Patel MS, Ziemba JB, et al. Residents' self-report on why they order perceived unnecessary inpatient laboratory tests. J Hosp Med2016; 11:869–872 [Google Scholar]
14. Yarbrough PM, Kukhareva PV, Horton D, Edholm K, Kawamoto K. Multifaceted intervention including education, rounding checklist implementation, cost feedback, and financial incentives reduces inpatient laboratory costs. J Hosp Med 2016; 11:348–354 [Google Scholar]
15. Raad S, Elliott R, Dickerson E, Khan B, Diab K. Reduction of laboratory utilization in the intensive care unit. J Intensive Care Med 2017; 32:500–507 [Google Scholar]
16. Sheng AY, Castro A, Lewiss RE. Awareness, utilization, and education of the ACR Appropriateness Criteria: a review and future directions. J Am Coll Radiol 2016; 13:131–136 [Google Scholar]
17. Retrouvey M, Trace AP, Shaves S. Radiologic knowledge and ordering habits of clinical residents: ACR Appropriateness Criteria awareness and perceptions. J Am Coll Radiol 2016; 13:725–729 [Google Scholar]
18. Taragin BH, Feng L, Ruzal-Shapiro C. Online radiology appropriateness survey: results and conclusions from an academic internal medicine residency. Acad Radiol 2003; 10:781–785 [Google Scholar]
19. Pitts SR, Morgan SR, Schrager JD, Berger TJ. Emergency department resource use by supervised residents vs attending physicians alone. JAMA 2014; 312:2394–2400 [Google Scholar]
20. Isaacs DM, Marinac J, Sun C. Radiograph use in low back pain: a United States Emergency Department database analysis. J Emerg Med2004; 26:37–45 [Google Scholar]
21. Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate? J Am Med Inform Assoc 2014; 21:23–26 [Google Scholar]
22. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10:523–530 [Google Scholar]
23. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330:765 [Google Scholar]
Address correspondence to J. Poeran ().

Full Text | PDF (707 KB) | PDF Plus (731 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 867-873
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.20474) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Musculoskeletal Imaging

Original Research

Ulnar Collateral Ligament Insertional Injuries in Pediatric Overhead Athletes: Are MRI Findings Predictive of Symptoms or Need for Surgery?

Share Share Claim CREDIT
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 867-873. 10.2214/AJR.18.20474

ABSTRACT :

OBJECTIVE. The purpose of this study was to determine whether ulnar collateral ligament (UCL) insertion below the articular margin (so-called T sign) exists in the pediatric population and whether MRI features can be used to identify insertional UCL injuries in overhead athletes that are symptomatic or require surgery.

MATERIALS AND METHODS. Retrospective review of elbow MR images of patients younger than 21 years from 2011 to 2017 yielded 26 control subjects who were not overhead athletes and 97 overhead athletes. According to the clinical diagnosis, 50 of the overhead athletes had symptoms. Two radiologists evaluated the UCL for thickness, abnormal insertional signal intensity, insertion distance, and adjacent marrow or soft-tissue edema. Insertion distance was defined as the coronal length of any T sign measured from the articular margin.

RESULTS. Mean insertion distance was greater in overhead athletes than in control subjects (1.42 vs 0.23 mm, p = 0.001) but not significantly different in athletes with symptoms compared with those without symptoms or in those who underwent operative treatment compared with those who did not. Mean UCL thickness was greater in overhead athletes than in control subjects (2.64 vs 1.74 mm, p < 0.0001), athletes with than those without symptoms (2.84 vs 2.41 mm, p = 0.005), and athletes who did versus those who did not undergo operative treatment (3.40 vs 2.73 mm, p = 0.011). Marrow (p = 0.002) and soft-tissue (p = 0.016) edema were found more frequently in athletes with symptoms. ROC analysis of UCL thickness and insertion distance as predictors of symptoms showed AUCs of 0.69 and 0.49, respectively.

CONCLUSION. The T sign is likely not an anatomic variation but is a poor predictor of symptoms and need for surgery. Soft-tissue and marrow edema are more frequently seen in overhead athletes with symptomatic injuries and can aid in the diagnosis of clinically relevant injury.

Keywords: insertional injuries, MRI, overhead athletes, pediatric, ulnar collateral ligament

Based on presentations at the Society of Skeletal Radiology, Santa Barbara, CA, and Radiological Society of North America, Chicago, IL, 2017 annual meetings.

References
Previous section
1. Morrey BF, An KN. Functional anatomy of the ligaments of the elbow. Clin Orthop Relat Res 1985; 84–90 [Google Scholar]
2. Werner SL, Fleisig GS, Dillman CJ, Andrews JR. Biomechanics of the elbow during baseball pitching. J Orthop Sports Phys Ther 1993; 17:274–278 [Google Scholar]
3. Fleisig GS, Andrews JR, Dillman CJ, Escamilla RF. Kinetics of baseball pitching with implications about injury mechanisms. Am J Sports Med1995; 23:233–239 [Google Scholar]
4. Ahmad CS, Lee TQ, ElAttrache NS. Biomechanical evaluation of a new ulnar collateral ligament reconstruction technique with interference screw fixation. Am J Sports Med 2003; 31:332–337 [Google Scholar]
5. Regan WD, Korinek SL, Morrey BF, An KN. Bio-mechanical study of ligaments around the elbow joint. Clin Orthop Relat Res 1991; 170–179 [Google Scholar]
6. Olsen SJ 2nd, Fleisig GS, Dun S, Loftice J, Andrews JR. Risk factors for shoulder and elbow injuries in adolescent baseball pitchers. Am J Sports Med 2006; 34:905–912 [Google Scholar]
7. Chen FS, Diaz VA, Loebenberg M, Rosen JE. Shoulder and elbow injuries in the skeletally immature athlete. J Am Acad Orthop Surg 2005; 13:172–185 [Google Scholar]
8. Petty DH, Andrews JR, Fleisig GS, Cain EL. Ulnar collateral ligament reconstruction in high school baseball players: clinical results and injury risk factors. Am J Sports Med 2004; 32:1158–1164 [Google Scholar]
9. Hodgins JL, Vitale M, Arons RR, Ahmad CS. Epidemiology of medial ulnar collateral ligament reconstruction: a 10-year study in New York State. Am J Sports Med 2016; 44:729–734 [Google Scholar]
10. Mahure SA, Mollon B, Shamah SD, Kwon YW, Rokito AS. Disproportionate trends in ulnar collateral ligament reconstruction: projections through 2025 and a literature review. J Shoulder Elbow Surg 2016; 25:1005–1012 [Google Scholar]
11. Pennock AT, Pytiak A, Stearns P, et al. Preseason assessment of radiographic abnormalities in elbows of Little League baseball players. J Bone Joint Surg Am 2016; 98:761–767 [Google Scholar]
12. Schwab GH, Bennett JB, Woods GW, Tullos HS. Biomechanics of elbow instability: the role of the medial collateral ligament. Clin Orthop Relat Res 1980; 42–52 [Google Scholar]
13. Callaway GH, Field LD, Deng XH, et al. Biomechanical evaluation of the medial collateral ligament of the elbow. J Bone Joint Surg Am 1997; 79:1223–1231 [Google Scholar]
14. Farrow LD, Mahoney AJ, Stefancin JJ, Taljanovic MS, Sheppard JE, Schickendantz MS. Quantitative analysis of the medial ulnar collateral ligament ulnar footprint and its relationship to the ulnar sublime tubercle. Am J Sports Med 2011; 39:1936–1941 [Google Scholar]
15. Timmerman LA, Schwartz ML, Andrews JR. Pre-operative evaluation of the ulnar collateral ligament by magnetic resonance imaging and computed tomography arthrography: evaluation in 25 baseball players with surgical confirmation. Am J Sports Med 1994; 22:26–31; discussion, 32 [Google Scholar]
16. Timmerman LA, Andrews JR. Undersurface tear of the ulnar collateral ligament in baseball players: a newly recognized lesion. Am J Sports Med 1994; 22:33–36 [Google Scholar]
17. Munshi M, Pretterklieber ML, Chung CB, et al. Anterior bundle of ulnar collateral ligament: evaluation of anatomic relationships by using MR imaging, MR arthrography, and gross anatomic and histologic analysis. Radiology 2004; 231:797–803 [Google Scholar]
18. Dugas JR, Ostrander RV, Cain EL, Kingsley D, Andrews JR Anatomy of the anterior bundle of the ulnar collateral ligament. J Shoulder Elbow Surg 2007; 16:657–660 [Google Scholar]
19. Del Grande F, Aro M, Farahani SJ, Wilckens J, Cosgarea A, Carrino JA. Three-Tesla MR imaging of the elbow in non-symptomatic professional baseball pitchers. Skeletal Radiol 2015; 44:115–123 [Google Scholar]
20. Gutierrez NM, Granville C, Kaplan L, Baraga M, Jose J. Elbow MRI findings do not correlate with future placement on the disabled list in asymptomatic professional baseball pitchers. Sports Health 2017; 9:222–229 [Google Scholar]
21. Wong TT, Lin DJ, Ayyala RS, KazAm JK. Elbow injuries in adult overhead athletes. AJR 2017; 208:W110–W120 [Google Scholar]
22. LiMarzi GM, O'Dell MC, Scherer K, Pettis C, Wasyliw CW, Bancroft LW. Magnetic resonance arthrography of the wrist and elbow. Magn Reson Imaging Clin N Am 2015; 23:441–455 [Google Scholar]
23. Nazarian LN, McShane JM, Ciccotti MG, O'Kane PL, Harwood MI. Dynamic US of the anterior band of the ulnar collateral ligament of the elbow in asymptomatic major league baseball pitchers. Radiology 2003; 227:149–154 [Google Scholar]
24. Hurd WJ, Eby S, Kaufman KR, Murthy NS. Magnetic resonance imaging of the throwing elbow in the uninjured, high school-aged baseball pitcher. Am J Sports Med 2011; 39:722–728 [Google Scholar]
25. Kooima CL, Anderson K, Craig JV, Teeter DM, van Holsbeeck M. Evidence of subclinical medial collateral ligament injury and posteromedial impingement in professional baseball players. Am J Sports Med 2004; 32:1602–1606 [Google Scholar]
26. Atanda A Jr, Averill LW, Wallace M, Niiler TA, Nazarian LN, Ciccotti MG. Factors related to increased ulnar collateral ligament thickness on stress sonography of the elbow in asymptomatic youth and adolescent baseball pitchers. Am J Sports Med 2016; 44:3179–3187 [Google Scholar]
27. Schwartz ML, al-Zahrani S, Morwessel RM, Andrews JR. Ulnar collateral ligament injury in the throwing athlete: evaluation with saline-enhanced MR arthrography. Radiology 1995; 197:297–299 [Google Scholar]
28. Magee T. Accuracy of 3-T MR arthrography versus conventional 3-T MRI of elbow tendons and ligaments compared with surgery. AJR 2015; 204:[web]W70–W75 [Google Scholar]
Address correspondence to T. T. Wong ().

Full Text | PDF (803 KB) | PDF Plus (833 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 874-882
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20347) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Musculoskeletal Imaging

Original Research

Naviculocuneiform and Second and Third Tarsometatarsal Articulations: Underappreciated Normal Anatomy and How It May Affect Fluoroscopy-Guided Injections

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 874-882. 10.2214/AJR.18.20347

ABSTRACT :

OBJECTIVE. Because the second and third tarsometatarsal (TMT) and naviculocuneiform joints normally communicate, the least arthritic or technically most straightforward joint was injected when a fluoroscopically guided therapeutic injection was ordered for one or both joints. We hypothesized that pain relief would be equivalent regardless of the joint injected and would result in less radiation and a lower steroid dose compared with patients who had both articulations injected.

MATERIALS AND METHODS. Seventy-eight patients were divided into four joint groups: naviculocuneiform requested and injected (n = 15), nonrequested naviculocuneiform or second and third TMT injected (n = 25), both injected (n = 23), and TMT requested and injected (n = 15). Variables recorded included patient age and sex, fluoroscopy time, steroid dose, pre- and postprocedural pain, osteoarthrosis (OA) grade, and confidence of intraarticular injection. Statistical analysis compared mean pain level change before and after injection, mean fluoroscopy time, and mean steroid dose between groups. The mean OA grade of the nonrequested joint was compared with that of the requested joint in patients whose injected and requested joints did not match (group 2).

RESULTS. Pre- and postinjection pain reduction (p = 0.630) and postinjection pain (p = 0.935) were not significantly different. Mean steroid dose (p< 0.001) and fluoroscopy time (p = 0.0001) were significantly increased for the both joint injection group. Within the nonrequested naviculocuneiform or second and third TMT injection group, there was a significant difference in OA grade between injected (least arthritic) and requested joints (p = 0.001).

CONCLUSION. When faced with challenging naviculocuneiform or second and third TMT joint injections, choosing the technically most straightforward joint may result in less radiation and steroid dose without compromising quality of care or pain reduction.

Keywords: anatomy, fluoroscopy, injection, naviculocuneiform, osteoarthrosis, second and third tarsometatarsal joints

Acknowledgments
Previous sectionNext section

We thank Nan Hu, Xuechen (Kathryn) Wang, and Ryan S. Hirschi for their help.

References
Previous section
1. Peterson CK, Buck F, Pfirrmann CW, Zanetti M, Hodler J. Fluoroscopically guided diagnostic and therapeutic injections into foot articulations: report of short-term patient responses and comparison of outcomes between various injection sites. AJR 2011; 197:949–953 [Google Scholar]
2. Buck FM, Pfirrmann CW, Brunner F, Hodler J, Peterson C. The posterolateral fluoroscopy-guided injection technique into the posterior subtalar joint: description of the procedure and pilot study on patient outcomes. Skeletal Radiol 2012; 41:699–705 [Google Scholar]
3. Shoja MM. Ankle and foot. In: Standring S, ed. Gray's anatomy: the anatomical basis of clinical practice, 41st ed. New York, NY: Elsevier, 2016:1418–1439 [Google Scholar]
4. Kelikian AS, Sarrafian SK. Functional anatomy of the foot and ankle. In: Kelikian AS, Sarrafian SK, eds. Sarrafian's anatomy of the foot and ankle: descriptive, topographic, functional, 3rd ed. Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins, 2011:507–644 [Google Scholar]
5. Crim JR. Tarsometatarsal joint. In: Crim JR, Man-aster BJ, eds. Imaging anatomy: knee, ankle, foot, 2nd ed. Philadelphia, PA: Elsevier, 2017:436–441 [Google Scholar]
6. Crim J. Foot. In: Crim J, ed. Specialty imaging: arthrography—principles and practice in radiology, 1st ed. Altona, MB, Canada: Amirsys, 2009:238–254 [Google Scholar]
7. Kellgren JH, Lawrence JS. Radiological assessment of osteoarthrosis. Ann Rheum Dis 1957; 16:494–502 [Google Scholar]
8. Jensen MP, McFarland CA. Increasing the reliability and validity of pain intensity measurement in chronic pain patients. Pain 1993; 55: 195–203 [Google Scholar]
9. Rastogi AK, Davis KW, Ross A, Rosas HG. Fundamentals of joint injection. AJR 2016; 207:484–494 [Google Scholar]
10. MacMahon PJ, Eustace SJ, Kavanagh EC. Injectable corticosteroid and local anesthetic preparations: a review for radiologists. Radiology2009; 252:647–661 [Google Scholar]
11. Rogojan C, Hetland ML. Depigmentation: a rare side effect to intra-articular glucocorticoid treatment. Clin Rheumatol 2004; 23:373–375 [Google Scholar]
12. Endo Y, Nwawka OK, Smith S, Burket JC. Tarsometatarsal joint communication during fluoroscopy-guided therapeutic joint injections and relationship with patient age and degree of osteoarthritis. Skeletal Radiol 2018; 47:271–277 [Google Scholar]
13. Renner K, McAlister JE, Galli MM, Hyer CF. Anatomic description of the naviculocuneiform articulation. J Foot Ankle Surg 2017; 56:19–21 [Google Scholar]
14. Preidler KW, Wang YC, Brossman J, Trudell D, Daenen B, Resnick D. Tarsometatarsal joint: anatomic details on MRI images. Radiology 1996; 199:733–736 [Google Scholar]
15. Siddiqui NA, Galizia MS, Almusa E, Omar IM. Evaluation of the tarsometatarsal joint using conventional radiography, CT, and MR imaging. RadioGraphics 2014; 34:514–531 [Google Scholar]
16. Carmont MR, Tomlinson JE, Blundell C, Davies MB, Moore DJ. Variability of joint communications in the foot and ankle demonstrated by contrast-enhanced diagnostic injections. Foot Ankle Int 2009; 30:439–442 [Google Scholar]
17. Peterson C, Hodler J. Evidence-based radiology. Part 2. Is there sufficient research to support the use of therapeutic injections into the peripheral joints? Skeletal Radiol 2010; 39:11–18 [Google Scholar]
18. Khoury NJ, el-Khoury GY, Saltzman CL, Brandser EA. Intraarticular foot and ankle injections to identify source pain before arthrodesis. AJR1996; 167:669–673 [Google Scholar]
19. Lucas PE, Hurwitz SR, Kaplan PA, Dussault RG, Maurer EJ. Fluoroscopically guided injections into the foot and ankle: localization of the source of pain as a guide to treatment—prospective study. Radiology 1997; 204:411–415 [Google Scholar]
20. Mitchell MJ, Bielecki D, Bergman AG, Kursunoglu-Brahme S, Sartoris DJ, Resnick D. Localization of specific joint causing hindfoot pain: value of injecting local anesthetics into individual joints during arthrography. AJR 1995; 164:1473–1476 [Google Scholar]
21. Habib GS, Bashir M, Jabbour A. Increased blood glucose levels following intra-articular injection of methylprednisolone acetate in patients with controlled diabetes and symptomatic osteoarthritis of the knee. Ann Rheum Dis 2008; 67:1790–1791 [Google Scholar]
22. Thumboo J, O'Duffy JD. A prospective study of the safety of joint and soft tissue aspirations and injections in patients taking warfarin sodium. Arthritis Rheum 1998; 41:736–739 [Google Scholar]
23. Newberg AH, Munn CS, Robbins AH. Complications of arthrography. Radiology 1985; 155:605–606 [Google Scholar]
24. Menz HB, Jordan KP, Roddy E, Croft PR. Characteristics of primary care consultations for musculoskeletal foot and ankle problems in the UK. Rheumatology 2010; 49:1391–1398 [Google Scholar]
Address correspondence to B. G. Hansford ().

Full Text | PDF (995 KB) | PDF Plus (1017 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 883-891
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20531) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Neuroradiology/Head and Neck Imaging

Original Research

Integrated PET-MRI for Glioma Surveillance: Perfusion-Metabolism Discordance Rate and Association With Molecular Profiling

Share Share Claim CREDIT
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 883-891. 10.2214/AJR.18.20531

ABSTRACT :

OBJECTIVE. Both 18F-FDG PET and perfusion MRI are commonly used techniques for posttreatment glioma surveillance. Using integrated PET-MRI, we assessed the rate of discordance between simultaneously acquired FDG PET images and dynamic contrast-enhanced (DCE) perfusion MR images and determined whether tumor genetics predicts discordance.

MATERIALS AND METHODS. Forty-one consecutive patients with high-grade gliomas (20 with grade IV gliomas and 21 with grade III gliomas) underwent a standardized tumor protocol performed using an integrated 3-T PET-MRI scanner. Quantitative measures of standardized uptake value, plasma volume, and permeability were obtained from segmented whole-tumor volumes of interest and targeted ROIs. ROC curve analysis and the Youden index were used to identify optimal cutoffs for FDG PET and DCE-MRI. Two-by-two contingency tables and percent agreement were used to assess accuracy and concordance. Twenty-six patients (63%) from the cohort underwent next-generation sequencing for tumor genetics.

RESULTS. The best-performing FDG PET and DCE-MRI cutoffs achieved sensitivities of 94% and 91%, respectively; specificities of 56% and 89%, respectively; and accuracies of 80% and 83%, respectively. FDG PET and DCE-MRI findings were discordant for 11 patients (27%), with DCE-MRI findings correct for six of these patients (55%). Tumor grade, tumor volume, bevacizumab exposure, and time since radiation predicted discordance between FDG PET and DCE-MRI findings, with an ROC AUC value of 0.78. Isocitrate dehydrogenase gene and receptor tyrosine kinase gene pathway mutations increased the ROC AUC value to 0.83.

CONCLUSION. FDG PET and DCE-MRI show comparable accuracy and sensitivity in identifying tumor progression. These modalities were shown to have discordant findings for more than a quarter of the patients assessed. Tumor genetics may contribute to perfusion-metabolism discordance, warranting further investigation.

Keywords: glioma, perfusion, permeability, PET-MRI, tumor

Based on a presentation at the American Society of Neuroradiology 2018 annual meeting, Vancouver, BC, Canada.

Supported in part by award UL1TR000457 from the National Center for Advancing Translational Sciences of the National Institutes of Health.

Acknowledgment
Previous sectionNext section

We thank Gulce Askin, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, for her statistical guidance.

References
Previous section
1. Chao ST, Suh JH, Raja S, et al. The sensitivity and specificity of FDG PET in distinguishing recurrent brain tumor from radionecrosis in patients treated with stereotactic radiosurgery. Int J Cancer 2001; 96:191–197 [Google Scholar]
2. Valk PE, Budinger TF, Levin VA, et al. PET of malignant cerebral tumors after interstitial brachytherapy. J Neurosurg 1988; 69:830–838 [Google Scholar]
3. Janus TJ, Kim EE, Tilbury R, et al. Use of [18F] fluorodeoxyglucose positron emission tomography in patients with primary malignant brain tumors. Ann Neurol 1993; 33:540–548 [Google Scholar]
4. Ricci PE, Karis JP, Heiserman JE, et al. Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography? AJNR 1998; 19:407–413 [Google Scholar]
5. Ozsunar Y, Mullins ME, Kwong K, et al. Glioma recurrence versus radiation necrosis? A pilot comparison of arterial spin-labeled, dynamic susceptibility contrast enhanced MRI, and FDG-PET imaging. Acad Radiol 2010; 17:282–290 [Google Scholar]
6. Gómez-Río M, Rodríguez-Fernández A, Ramos-Font C, et al. Diagnostic accuracy of 201Thallium-SPECT and 18F-FDG-PET in the clinical assessment of glioma recurrence. Eur J Nucl Med Mol Imaging 2008; 35:966–975 [Google Scholar]
7. Schifter T, Hoffman JM, Hanson MW, et al. Serial FDG-PET studies in the prediction of survival in patients with primary brain tumors. J Comput Assist Tomogr 1993; 17:509–561 [Google Scholar]
8. Song YS, Choi SH, Park C-K, et al. True progression versus pseudoprogression in the treatment of glioblastomas: a comparison study of normalized cerebral blood volume and apparent diffusion coefficient by histogram analysis. Korean J Radiol 2013; 14:662–672 [Google Scholar]
9. Cha J, Kim ST, Kim H-J, et al. Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR 2014; 35:1309–1317 [Google Scholar]
10. Parvez K, Parvez A, Zadeh G. The diagnosis and treatment of pseudoprogression, radiation necrosis and brain tumor recurrence. Int J Mol Sci2014; 15:11832–11846 [Google Scholar]
11. Verma N, Cowperthwaite MC, Burnett MG, et al. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol 2013; 15:515–534 [Google Scholar]
12. Mullins ME, Barest GD, Schaefer PW, et al. Radiation necrosis versus glioma recurrence: conventional MR imaging clues to diagnosis. AJNR2005; 26:1967–1972 [Google Scholar]
13. Suh CH, Kim HS, Choi YJ, et al. Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. AJNR 2013; 34:2278–2286 [Google Scholar]
14. Prager AJ, Martinez N, Beal K, et al. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR 2015; 36:877–885 [Google Scholar]
15. Barajas RF, Chang JS, Segal MR, et al. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2009; 253:486–496 [Google Scholar]
16. Nihashi T, Dahabreh IJ, Terasawa T. Diagnostic accuracy of PET for recurrent glioma diagnosis: a meta-analysis. AJNR 2013; 34:944–950 [Google Scholar]
17. Patel P, Baradaran H, Delgado D, et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol 2017; 19:118–127 [Google Scholar]
18. Jena A, Taneja S, Jha A, et al. Multiparametric evaluation in differentiating glioma recurrence from treatment-induced necrosis using simultaneous 18F-FDG-PET/MRI: a single-institution retrospective study. AJNR 2017; 38:899–907 [Google Scholar]
19. Hatzoglou V, Yang TJ, Omuro A, et al. A prospective trial of dynamic contrast-enhanced MRI perfusion and fluorine-18 FDG PET-CT in differentiating brain tumor progression from radiation injury after cranial irradiation. Neuro Oncol 2016; 18:873–880 [Google Scholar]
20. Sacconi B, Raad RA, Lee J, et al. Concurrent functional and metabolic assessment of brain tumors using hybrid PET/MR imaging. J Neurooncol 2016; 127:287–293 [Google Scholar]
21. Galldiks N, Langen K. Amino acid PET: an imaging option to identify treatment response, post-therapeutic effects, and tumor recurrence? Front Neurol 2016; 7:120 [Google Scholar]
22. Yoon JH, Kim JH, Kang WJ, et al. Grading of cerebral glioma with multiparametric MR imaging and 18F-FDG-PET: concordance and accuracy. Eur Radiol 2014; 24:380–389 [Google Scholar]
23. Thomas AA, Arevalo-Perez J, Kaley T, et al. Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma. J Neurooncol 2015; 125:183–190 [Google Scholar]
24. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 2013; 31:1023–1031 [Google Scholar]
25. Eckel-Passow JE, Lachance DH, Molinaro AM, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 2015; 372:2499–2508 [Google Scholar]
26. Diplas BH, He X, Brosnan-Cashman JA, et al. The genomic landscape of TERT promoter wildtype-IDH wildtype glioblastoma. Nat Commun2018; 9:2087 [Google Scholar]
27. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 1979; 86:420–428 [Google Scholar]
28. Cicchetti DV, Sparrow SA. Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. Am J Ment Defic 1981; 86:127–137 [Google Scholar]
29. Lyons K, Seghers V, Sorensen JI, et al. Comparison of standardized uptake values in normal structures between PET/CT and PET/MRI in a tertiary pediatric hospital: a prospective study. AJR 2015; 205:1094–1101 [Google Scholar]
30. Hygino da Cruz LC Jr, Rodriguez I, Domingues RC, et al. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR 2011; 32:1978–1985 [Google Scholar]
Address correspondence to G. C. Chiang ().

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Full Text | PDF (1103 KB) | PDF Plus (866 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 892-898
Posted online on February 11, 2019.
(https://doi.org/10.2214/AJR.18.20044) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Neuroradiology/Head and Neck Imaging

Original Research

Neurofibromatosis Type 1: Description of a Novel Diagnostic Scoring System in Pediatric Optic Nerve Glioma

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 892-898. 10.2214/AJR.18.20044

ABSTRACT :

OBJECTIVE. Neurofibromatosis type 1 (NF1) is a multisystemic genetic disease in which patients develop benign tumors including optic nerve gliomas (ONG). Optic nerve thickening and tortuosity are radiologic markers of tumors but can also be present in children with NF1 who do not have gliomas, thus complicating screening and diagnosis. We undertook this study to retrospectively determine quantitative and qualitative diagnostic criteria using MRI of the orbits for ONG in children with NF1.

MATERIALS AND METHODS. MR images of the orbits obtained from 2003 to 2016 for children with and without NF1 were reviewed. Optic nerves were divided into three groups: NF1 with glioma (n = 71 nerves), NF1 without glioma (n = 151 nerves), and healthy control subjects (n = 66 nerves). The diameter of each nerve was measured at multiple locations. Two radiologists assessed tortuosity using validated criteria, and subarachnoid dilatation was quantified. Last, a composite score using both optic nerve diameter and tortuosity was proposed.

RESULTS. The mean diameter of the optic nerve was significantly larger in patients with NF1 with glioma compared with those with NF1 without glioma and with control subjects at all locations. Maximal nerve diameter greater than 2 SD above the mean maximal diameter for control nerves was considered abnormally enlarged. The tortuosity parameters were all significantly associated with ONG compared with absence of ONG in NF1. A scoring system derived from these data were highly reliable in differentiating ONG from absence of ONG in NF1.

CONCLUSION. The radiologic diagnosis of ONG in patients with NF1 is challenging. The scoring systems we describe provide a framework for simple radiologic criteria for ONG in these patients.

Keywords: glioma, neurofibromatosis type 1, optic, pediatric, tortuosity

References
Previous section
1. Bekisz O, Darimont F, Rompen EH. Diffuse but unilateral gingival enlargement associated with von Recklinghausen neurofibromatosis: a case report. J Clin Periodontol 2000; 27:361–365 [Google Scholar]
2. Hillier JC, Moskovic E. The soft-tissue manifestations of neurofibromatosis type 1. Clin Radiol 2005; 60:960–967 [Google Scholar]
3. Cotran RS, Kumar V, Robbins SL. Robbins pathologic basis of disease, 8th ed. Philadelphia, PA: Saunders, 2010 [Google Scholar]
4. Cooper DN, Upadhyaya M. The germline mutational spectrum in neurofibromatosis type 1 and genotype-phenotype correlations. In: Upadhyaya M, Cooper D, eds. Neurofibromatosis type 1. Heidelberg, Germany: Springer, 2012 [Google Scholar]
5. Zhu Y, Ghosh P, Charnay P, Burns DK, Parada LF. Neurofibromas in NF1: Schwann cell origin and role of tumor environment. Science 2002; 296:920–922 [Google Scholar]
6. Viskochil D. Genetics of neurofibromatosis 1 and the NF1 gene. J Child Neurol 2002; 17:562–570; discussion, 571–572 [Google Scholar]
7. Friedman JM, Gutmann DH, MacCollin M, Riccardi VM. Neurofibromatosis: phenotype, natural history, and pathogenesis. Baltimore, MD: Johns Hopkins University Press, 1999 [Google Scholar]
8. Rasmussen SA, Yang Q, Friedman JM. Mortality in neurofibromatosis 1: an analysis using U.S. death certificates. Am J Hum Genet 2001; 68:1110–1118 [Google Scholar]
9. Solga AC, Gutmann DH. NF1-associated optic glioma. In: Upadhyaya M, Cooper D, eds. Neurofibromatosis type 1: molecular and cellular biology. Heidelberg, Germany: Springer, 2012:341–352 [Google Scholar]
10. Yu J, Deshmukh H, Gutmann RJ, et al. Alterations of BRAF and HIPK2 loci predominate in sporadic pilocytic astrocytoma. Neurology 2009; 73:1526–1531 [Google Scholar]
11. Seminog OO, Goldacre MJ. Risk of benign tumours of nervous system, and of malignant neoplasms, in people with neurofibromatosis: population-based record-linkage study. Br J Cancer 2013; 108:193–198 [Google Scholar]
12. Niemeyer CM, Arico M, Basso G, et al. Chronic myelomonocytic leukemia in childhood: a retrospective analysis of 110 cases—European Working Group on Myelodysplastic Syndromes in Childhood (EWOG-MDS). Blood 1997; 89:3534–3543 [Google Scholar]
13. Agaimy A, Vassos N, Croner RS. Gastrointestinal manifestations of neurofibromatosis type 1 (Recklinghausen's disease): clinicopathological spectrum with pathogenetic considerations. Int J Clin Exp Pathol 2012; 5:852–862 [Google Scholar]
14. Madanikia SA, Bergner A, Ye X, Blakeley JO. Increased risk of breast cancer in women with NF1. Am J Med Genet A 2012; 158A:3056–3060 [Google Scholar]
15. Blazo MA, Lewis RA, Chintagumpala MM, Frazier M, McCluggage C, Plon SE. Outcomes of systematic screening for optic pathway tumors in children with neurofibromatosis type 1. Am J Med Genet A 2004; 127A:224–229 [Google Scholar]
16. Listernick R, Charrow J, Greenwald M, Mets M. Natural history of optic pathway tumors in children with neurofibromatosis type 1: a longitudinal study. J Pediatr 1994; 125:63–66 [Google Scholar]
17. Rasool N, Odel JG, Kazim M. Optic pathway glioma of childhood. Curr Opin Ophthalmol 2017; 28:289–295 [Google Scholar]
18. Prada CE, Hufnagel RB, Hummel TR, et al. The use of magnetic resonance imaging screening for optic pathway gliomas in children with neurofibromatosis type 1. J Pediatr 2015; 167:851.e1–856.e1 [Google Scholar]
19. Beres SJ, Avery RA. Optic pathway gliomas secondary to neurofibromatosis type 1. Semin Pediatr Neurol 2017; 24:92–99 [Google Scholar]
20. Dodge HW Jr, Love JG, Craig WM, et al. Gliomas of the optic nerves. AMA Arch Neurol Psychiatry 1958; 79:607–621 [Google Scholar]
21. Balcer LJ, Liu GT, Heller G, et al. Visual loss in children with neurofibromatosis type 1 and optic pathway gliomas: relation to tumor location by magnetic resonance imaging. Am J Ophthalmol 2001; 131:442–445 [Google Scholar]
22. Thiagalingam S, Flaherty M, Billson F, North K. Neurofibromatosis type 1 and optic pathway gliomas: follow-up of 54 patients. Ophthalmology2004; 111:568–577 [Google Scholar]
23. Dunn DW, Purvin V. Optic pathway gliomas in neurofibromatosis. Dev Med Child Neurol 1990; 32:820–824 [Google Scholar]
24. DiMario FJ Jr, Ramsby G, Greenstein R, Langshur S, Dunham B. Neurofibromatosis type 1: magnetic resonance imaging findings. J Child Neurol 1993; 8:32–39 [Google Scholar]
25. Avery RA, Mansoor A, Idrees R, et al. Quantitative MRI criteria for optic pathway enlargement in neurofibromatosis type 1. Neurology 2016; 86:2264–2270 [Google Scholar]
26. Van Es S, North KN, McHugh K, De Silva M. MRI findings in children with neurofibromatosis type 1: a prospective study. Pediatr Radiol 1996; 26:478–487 [Google Scholar]
27. Armstrong GT, Localio AR, Feygin T, et al. Defining optic nerve tortuosity. AJNR 2007; 28:666–671 [Google Scholar]
28. Listernick R, Darling C, Greenwald M, Strauss L, Charrow J. Optic pathway tumors in children: the effect of neurofibromatosis type 1 on clinical manifestations and natural history. J Pediatr 1995; 127:718–722 [Google Scholar]
29. Zhang YQ, Li J, Xu L, et al. Anterior visual pathway assessment by magnetic resonance imaging in normal-pressure glaucoma. Acta Ophthalmol 2012; 90:e295–e302 [Google Scholar]
30. Mesa-Gutiérrez JC, Quinones SM, Ginebreda JA. Optic nerve sheath meningocele. Clin Ophthalmol 2008; 2:661–668 [Google Scholar]
31. Jungkim S, Khurshid SG, Fenton S. Dural ectasia of the optic nerve sheath. Acta Ophthalmol Scand 2005; 83:266–267 [Google Scholar]
32. Levin MH, Armstrong GT, Broad JH, et al. Risk of optic pathway glioma in children with neurofibromatosis type 1 and optic nerve tortuosity or nerve sheath thickening. Br J Ophthalmol 2016; 100:510–514 [Google Scholar]
33. Ji J, Shimony J, Gao F, McKinstry RC, Gutmann DH. Optic nerve tortuosity in children with neurofibromatosis type 1. Pediatr Radiol 2013; 43:1336–1343 [Google Scholar]
34. Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16:933–951 [Google Scholar]
35. Rogosnitzky M, Branch S. Gadolinium-based contrast agent toxicity: a review of known and proposed mechanisms. Biometals 2016; 29:365–376 [Google Scholar]
36. Kanda T, Ishii K, Kawaguchi H, Kitajima K, Takenaka D. High signal intensity in the dentate nucleus and globus pallidus on unenhanced T1-weighted MR images: relationship with increasing cumulative dose of a gadolinium-based contrast material. Radiology 2014; 270:834–841 [Google Scholar]
37. Segal L, Darvish-Zargar M, Dilenge ME, Ortenberg J, Polomeno RC. Optic pathway gliomas in patients with neurofibromatosis type 1: follow-up of 44 patients. J AAPOS 2010; 14:155–158 [Google Scholar]
Address correspondence to H. Eid ().

Full Text | PDF (880 KB) | PDF Plus (914 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 899-904
Posted online on January 30, 2019.
(https://doi.org/10.2214/AJR.18.20336) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Neuroradiology/Head and Neck Imaging

Original Research

Neurointerventional Radiology for the Aspiring Radiology Resident: Current State of the Field and Future Directions

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 899-904. 10.2214/AJR.18.20336

ABSTRACT :

OBJECTIVE. The purposes of this study were to document recent trends in stroke intervention at a tertiary-care facility with a comprehensive stroke center and to analyze current procedure volumes and the employment of specialty providers in neurointerventional radiology (NIR).

MATERIALS AND METHODS. Institutional trends in the volume of mechanical thrombectomy were analyzed on the basis of the number of patients who underwent mechanical thrombectomy from 2013 to 2017. To evaluate the current status of mechanical thrombectomy volumes in the United States, the number of patients in the Medicare fee-for-service database who underwent mechanical thrombectomy in 2016 was assessed. The specialty backgrounds of the various providers who performed mechanical thrombectomy were analyzed. Procedure volumes for intracranial stenting, embolization, and vertebral augmentation procedures were assessed.

RESULTS. From 2013 to 2017, the total numbers of mechanical thrombectomy procedures for acute ischemic stroke were 19 in 2013 and 111 in 2017. The total volume of mechanical thrombectomy procedures in the Medicare fee-for-service population in 2016 was 7479. For intracranial endovascular procedures, 20,850 were performed in the U.S. Medicare population in 2015 and 22,511 in 2016. Radiologists performed 45% of procedures in 2016; neurosurgeons, 41%; and neurologists, 11%. When the total numbers of percutaneous brain and spine procedures were combined, radiologists performed 41%; neurosurgeons, 23%; and neurologists, 3%. In 2016, there were a total of 220 active NIR staff at the NIR programs with rotating residents or fellows. In these programs, 49% of staff members were neuroradiologists, 41% were neurosurgeons, and 10% were neurologists. Of the 72 NIR departments with confirmed rotating fellows or residents, 14 had only neuroradiologists on staff, six had only neurosurgeons, and one had only neurologists.

CONCLUSION. Increasing radiology resident interest and participation in NIR should ensure a steady influx of radiologists into the field, continuing the strong tradition of radiology participation, leadership, and innovation in NIR.

Keywords: fellowship, interventional neuroradiology, neurointerventional radiology, radiology residents, training

References
Previous section
1. Berkhemer OA, Fransen PS, Beumer D, et al. A randomized trial of intra-arterial treatment for acute ischemic stroke. N Engl J Med 2015; 372:11–20 [Google Scholar]
2. Campbell BCV, Donnan GA, Lees KR, et al. Endovascular stent thrombectomy: the new standard of care for large vessel ischaemic stroke. Lancet Neurol 2015; 14:846–854 [Google Scholar]
3. Saver JL, Goyal M, Bonafe A, et al. Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke. N Engl J Med 2015; 372:2285–2295 [Google Scholar]
4. Campbell BC, Mitchell PJ, Yan B, et al. A multicenter, randomized, controlled study to investigate extending the time for thrombolysis in emergency neurological deficits with intra-arterial therapy (EXTEND-IA). Int J Stroke 2014; 9:126–132 [Google Scholar]
5. Nogueira RG, Jadhav AP, Haussen DC, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 2018; 378:11–21 [Google Scholar]
6. Kallmes DF, Comstock BA, Heagerty PJ, et al. A randomized trial of vertebroplasty for osteoporotic spinal fractures. N Engl J Med 2009; 361:569–579 [Google Scholar]
7. Buchbinder R, Osborne RH, Ebeling PR, et al. A randomized trial of vertebroplasty for painful osteoporotic vertebral fractures. N Engl J Med2009; 361:557–568 [Google Scholar]
8. Langreth R. Common spine surgery shows no benefit. Forbes website www.forbes.com/2009/08/05/vertebroplasty-healthcare-reform-business-healthcarebackpain.html#37f1b3b02db9. Published August 5, 2009. Accessed September 15, 2018 [Google Scholar]
9. Klazen CA, Lohle PN, de Vries J, et al. Vertebroplasty versus conservative treatment in acute osteoporotic vertebral compression fractures (VERTOS II): an open-label randomised trial. Lancet 2010; 376:1085–1092 [Google Scholar]
10. Cox M, Levin DC, Parker L, Morrison W, Long S, Rao VM. Vertebral augmentation after recent randomized controlled trials: a new rise in kyphoplasty volumes. J Am Coll Radiol 2016; 13:28–32 [Google Scholar]
11. Chen AT, Cohen DB, Skolasky RL. Impact of nonoperative treatment, vertebroplasty, and kyphoplasty on survival and morbidity after vertebral compression fracture in the medicare population. J Bone Joint Surg Am 2013; 95:1729–1736 [Google Scholar]
12. Lange A, Kasperk C, Alvares L, Sauermann S, Braun S. Survival and cost comparison of kyphoplasty and percutaneous vertebroplasty using German claims data. Spine 2014; 39:318–326 [Google Scholar]
13. Firanescu C, Lohle PN, De Vries J, et al. A randomised sham controlled trial of vertebroplasty for painful acute osteoporotic vertebral fractures (VERTOS IV). Trials 2011; 12:93 [Google Scholar]
14. Kroll H, Duszak R Jr, Nsiah E, Hughes DR, Sumer S, Wintermark M. Trends in lumbar puncture over 2 decades: a dramatic shift to radiology. AJR 2015; 204:15–19 [Google Scholar]
15. Kavanagh EC, Roberts CC, Frangos A, et al. Musculoskeletal biopsy: utilization and provider distribution in the United States Medicare population. Acad Radiol 2007; 14:371–375 [Google Scholar]
16. Manchikanti L, Pampati V, Hirsch JA. Retrospective cohort study of usage patterns of epidural injections for spinal pain in the US fee-for-service Medicare population from 2000 to 2014. BMJ Open 2016; 6:e013042 [Google Scholar]
17. National Resident Matching Program website. Results and data: 2017 main residency match. www.nrmp.org/wp-content/uploads/2017/06/Main-Match-Results-and-Data-2017.pdf. Published 2017. Accessed November 9, 2018 [Google Scholar]
18. National Resident Matching Program website. Match results statistics: radiology—2017. www.nrmp.org/wp-content/uploads/2017/06/Radiology-Match-Results-Statistics-AY2018.pdf. Published 2017. Accessed November 9, 2018 [Google Scholar]
19. Cox M, Levin DC, Parker L, Rao VM. Relative roles of radiologists and other physicians in percutaneous endovascular neurointerventions. J Am Coll Radiol 2015; 12:1030–1035 [Google Scholar]
20. Fiorella D, Hirsch JA, Woo HH, et al. Should neurointerventional fellowship training be suspended indefinitely? J Neurointerv Surg 2012; 4:315–318 [Google Scholar]
21. Connors B. Are there too many fellowships or not enough training? J Neurointerv Surg 2016; 8:e9–e10 [Google Scholar]
22. Hopkins LN, Holmes DR. Public health urgency created by the success of mechanical thrombectomy studies in stroke. Circulation 2017; 135:1188–1190 [Google Scholar]
23. Society of Neurointerventional Surgery; American Association of Neurological Surgeons; Congress of Neurological Surgeons; Society of Vascular and Interventional Neurology. Letter by the Society of Neurointerventional Surgery, the Cerebrovascular Section of the American Association of Neurological Surgeons and the Congress of Neurological Surgeons, and the Society of Vascular and Interventional Neurology regarding article, "public health urgency created by the success of mechanical thrombectomy studies in stroke." Circulation 2017; 136:779–780 [Google Scholar]
24. Hopkins LN, Holmes DR Jr. Response by Hopkins and Holmes to letter regarding article, "public health urgency created by the success of mechanical thrombectomy studies in stroke." Circulation 2017; 136:781–782 [Google Scholar]
25. Meyers PM, Blackham KA, Abruzzo TA, et al. Society of Neurointerventional Surgery standards of practice: general considerations. J Neurointerv Surg 2012; 4:11–15 [Google Scholar]
26. Bambakidis NC, Cockroft K, Hu YC, et al. Procedural requirements and certification paradigms for stroke care delivery: perspective of neurointerventional professional societies. Stroke 2017; 48:2901–2904 [Google Scholar]
27. Burton TM. A breakthrough stroke treatment can save lives—if it's available. Wall Street Journal website www.wsj.com/articles/a-treatment-is-revolutionizing-stroke-carebut-not-everyone-receives-it-1517933226. Published February 6, 2018. Accessed April 22, 2018 [Google Scholar]
28. Evans MRB, White P, Cowley P, Werring D. Revolution in acute ischaemic stroke care: a practical guide to mechanical thrombectomy. Pract Neurol 2017; 17:252–265 [Google Scholar]
29. Fiorella D, Molyneux A, Coon A, et al. Demographic, procedural and 30-day safety results from the WEB Intra-saccular Therapy Study (WEB-IT). J Neurointerv Surg 2017; 9:1191–1196 [Google Scholar]
Address correspondence to M. Cox ().

Full Text | PDF (874 KB) | PDF Plus (900 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 905-913
Posted online on December 27, 2018.
(https://doi.org/10.2214/AJR.18.19811) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Pediatric Imaging

Original Research

Imaging Patterns of Pediatric Pulmonary Blastomycosis

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 905-913. 10.2214/AJR.18.19811

ABSTRACT :

OBJECTIVE. The objective of our study was to characterize and update the radiologic patterns of pediatric pulmonary blastomycosis, and correlate the radiologic patterns with patient age.

MATERIALS AND METHODS. Patients 0–18 years old with pulmonary blastomycosis who underwent chest imaging from 2005 to 2016 were included in this study. The following data were collected: age, sex, clinical information, and imaging findings including presence of extrapulmonary involvement and scarring on follow-up examinations. Concordance between radiography and CT was analyzed.

RESULTS. Thirty-six patients (28 boys and eight girls) ranging in age from 3 months to 17 years (mean, 10.5 years) were identified. Consolidation was found in 94.4% of patients and was unilateral in 76.5% of cases and bilateral in 23.5%. Upper (70.6%) and middle (47.1%) lobes were more frequently involved. Air bronchograms were identified in 76.5% of patients with consolidations, masslike consolidation was found in 55.9%, cavitation in 38.2%, and bubbly pattern (i.e., multiple small cavities) in 32.4%. In all patients younger than 5 years, consolidations involved multiple lobes. In 67.6% of patients, consolidations were associated with the following additional pulmonary or pleural abnormalities: pulmonary nodules (50% of patients), diffuse patchy opacification (26.5%), reticulonodular pattern (41.2%), atelectasis (5.9%), pleural effusion (20.6%), and hilar lymphadenopathy (23.5%). Pulmonary scarring was found in 70.4% of patients. Five patients had extrapulmonary involvement. The concordance between radiography and CT was excellent for location and extension of consolidation and diagnosis of cavitation, bubbly pattern, and nodules.

CONCLUSION. The most common pattern of lung involvement from pulmonary blastomycosis in our series was a combination of consolidations with bilateral lung nodules and reticulonodular opacification.

Keywords: chest CT, chest radiography, different age groups, imaging patterns, pediatric pulmonary blastomycosis

References
Previous section
1. Saccente M, Woods GL. Clinical and laboratory update on blastomycosis. Clin Microbiol Rev 2010; 23:367–381 [Google Scholar]
2. Frost HM, Anderson J, Ivacic L, Meece J. Blastomycosis in children: an analysis of clinical, epidemiologic, and genetic features. J Pediatric Infect Dis Soc 2017; 6:49–56 [Google Scholar]
3. Fanella S, Skinner S, Trepman E, Embil JM. Blastomycosis in children and adolescents: a 30-year experience from Manitoba. Med Mycol 2011; 49:627–632 [Google Scholar]
4. Patel RG, Patel B, Petrini MF, Carter RR 3rd, Griffith J. Clinical presentation, radiographic findings, and diagnostic methods of pulmonary blastomycosis: a review of 100 consecutive cases. South Med J 1999; 92:289–295 [Google Scholar]
5. Roy M, Benedict K, Deak E, et al. A large community outbreak of blastomycosis in Wisconsin with geographic and ethnic clustering. Clin Infect Dis 2013; 57:655–662 [Google Scholar]
6. Brown LR, Swensen SJ, Van Scoy RE, Prakash UB, Coles DT, Colby TV. Roentgenologic features of pulmonary blastomycosis. Mayo Clin Proc1991; 66:29–38 [Google Scholar]
7. Sheflin JR, Campbell JA, Thompson GP. Pulmonary blastomycosis: findings on chest radiographs in 63 patients. AJR 1990; 154:1177–1180 [Google Scholar]
8. Ronald S, Strzelczyk J, Moore S, et al. Computed tomographic scan evaluation of pulmonary blastomycosis. Can J Infect Dis Med Microbiol2009; 20:112–116 [Google Scholar]
9. Fang W, Washington L, Kumar N. Imaging manifestations of blastomycosis: a pulmonary infection with potential dissemination. RadioGraphics2007; 27:641–655 [Google Scholar]
10. Alkrinawi S, Reed MH, Pasterkamp H. Pulmonary blastomycosis in children: findings on chest radiographs. AJR 1995; 165:651–654 [Google Scholar]
11. Pelly L, Al Juaid A, Fanella S. Severe blastomycosis in infants. Pediatr Infect Dis J 2014; 33:1189–1191 [Google Scholar]
12. Anderson EJ, Ahn PB, Yogev R, Jaggi P, Shippee DB, Shulman ST. Blastomycosis in children: a study of 14 cases. J Pediatric Infect Dis Soc2013; 2:386–390 [Google Scholar]
13. Shankar J, Restriped A, Clemons KV, Stevens DA. Hormones and the resistance of women to paracoccidioidomycosis. Clin Microbiol Rev2011; 24:296–313 [Google Scholar]
14. Nambu A, Ozawa K, Kobayashi N, Tago M. Imaging of community-acquired pneumonia: roles of imaging examinations, imaging diagnosis of specific pathogens and discrimination from noninfectious diseases. World J Radiol 2014; 6:779–793 [Google Scholar]
15. Winer-Muram HT, Beals DD, Cole FH. Blastomycosis of the lung: CT features. Radiology 1992; 182:829–832 [Google Scholar]

Full Text | PDF (866 KB) | PDF Plus (858 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 914-918
Posted online on February 4, 2019.
(https://doi.org/10.2214/AJR.18.20000) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Vascular and Interventional Radiology

Technical Innovation

Feasibility of Image Fusion for Concurrent MRI Evaluation of Vessel Lumen and Vascular Calcifications in Peripheral Arterial Disease

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 914-918. 10.2214/AJR.18.20000

ABSTRACT :

OBJECTIVE. With MR angiography of peripheral arterial disease, calcifications are unapparent, so a separate calcification-sensitive pulse sequence (proton density–weighted in-phase 3D stack-of-stars [PDIP-SOS]) is needed for complete assessment. We hypothesized that, despite being acquired separately, MR angiography and PDIP-SOS images could be coregistered and fused without loss of significant diagnostic information.

CONCLUSION. In a prospective study of 15 patients, MR image fusion enabled the simultaneous display of vessel lumen and vascular calcifications similarly to CT angiography.

Keywords: CT angiography, image fusion, MR angiography, unenhanced, vascular calcifications

Supported by grants R01 HL130093 and R21 HL126015 from the National Institutes of Health.

References
Previous section
1. Met R, Bipat S, Legemate DA, Reekers JA, Koelemay MJ. Diagnostic performance of computed tomography angiography in peripheral arterial disease: a systematic review and meta-analysis. JAMA 2009; 301:415–424 [Google Scholar]
2. Menke J, Larsen J. Meta-analysis: accuracy of contrast-enhanced magnetic resonance angiography for assessing steno-occlusions in peripheral arterial disease. Ann Intern Med 2010; 153:325–334 [Google Scholar]
3. Tranche-Iparraguirre S, Marin-Iranzo R, Fernandez-de Sanmamed R, Riesgo-Garcia A, Hevia-Rodriguez E, Garcia-Casas JB. Peripheral arterial disease and kidney failure: a frequent association. Nefrologia 2012; 32:313–320 [Google Scholar]
4. Miyazaki M, Lee VS. Nonenhanced MR angiography. Radiology 2008; 248:20–43 [Google Scholar]
5. Manunga JM, Gloviczki P, Oderich GS, et al. Femoral artery calcification as a determinant of success for percutaneous access for endovascular abdominal aortic aneurysm repair. J Vasc Surg 2013; 58:1208–1212 [Google Scholar]
6. Niskanen L, Siitonen O, Suhonen M, Uusitupa MI. Medial artery calcification predicts cardiovascular mortality in patients with NIDDM. Diabetes Care 1994; 17:1252–1256 [Google Scholar]
7. Guzman RJ, Brinkley DM, Schumacher PM, Donahue RM, Beavers H, Qin X. Tibial artery calcification as a marker of amputation risk in patients with peripheral arterial disease. J Am Coll Cardiol 2008; 51:1967–1974 [Google Scholar]
8. Ouwendijk R, Kock MC, van Dijk LC, van Sambeek MR, Stijnen T, Hunink MG. Vessel wall calcifications at multi-detector row CT angiography in patients with peripheral arterial disease: effect on clinical utility and clinical predictors. Radiology 2006; 241:603–608 [Google Scholar]
9. Ferreira Botelho MP, Koktzoglou I, Collins JD, et al. MR imaging of iliofemoral peripheral vascular calcifications using proton density-weighted, in-phase three-dimensional stack-of-stars gradient echo. Magn Reson Med 2017; 77:2146–2152 [Google Scholar]
10. Edelman RR, Sheehan JJ, Dunkle E, Schindler N, Carr J, Koktzoglou I. Quiescent-interval single-shot unenhanced magnetic resonance angiography of peripheral vascular disease: technical considerations and clinical feasibility. Magn Reson Med 2010; 63:951–958 [Google Scholar]
11. Tang W, Hu J, Zhang H, Wu P, He H. Kappa coefficient: a popular measure of rater agreement. Shanghai Arch Psychiatry 2015; 27:62–67 [Google Scholar]
12. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012; 22:276–282 [Google Scholar]
13. Hargreaves BA, Worters PW, Pauly KB, Pauly JM, Koch KM, Gold GE. Metal-induced artifacts in MRI. AJR 2011; 197:547–555 [Google Scholar]
14. Makki D, Naikoti K, Murali SR. Magnetic resonance imaging signal artefacts from invisible metal debris following surgery to the elbow. Shoulder Elbow 2018; 10:133–135 [Google Scholar]
Address correspondence to R. R. Edelman ().

The authors have received research support and royalties from Siemens Healthcare.

Full Text | PDF (676 KB) | PDF Plus (692 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 919-924
Posted online on February 4, 2019.
(https://doi.org/10.2214/AJR.18.20306) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Women's Imaging

Original Research

Qualitative Radiogenomics: Association Between BI-RADS Calcification Descriptors and Recurrence Risk as Assessed by the Oncotype DX Ductal Carcinoma In Situ Score

Share Share Claim CREDIT
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 919-924. 10.2214/AJR.18.20306

ABSTRACT :

OBJECTIVE. Treatment of ductal carcinoma in situ (DCIS) is controversial given the variable recurrence and progression to invasive carcinoma. Identifying women who would benefit from adjuvant radiation therapy on the basis of their recurrence risk may allow more individualized management strategies. The Oncotype DX Breast DCIS Score—which we refer to here as the "DCIS score"—is a validated surrogate marker of local recurrence. This study evaluated the association between BI-RADS mammographic calcification descriptors and the DCIS score.

MATERIALS AND METHODS. Fifty-eight women diagnosed with DCIS presenting with calcifications who had Oncotype DX Breast DCIS assay results were identified. Pretreatment BI-RADS mammographic calcification features were collected including morphology, distribution, and maximum span. The association between calcification descriptors and DCIS score was assessed with logistic regression modeling. Mean DCIS scores were calculated for calcification features significantly associated with DCIS score. All analyses were adjusted for patient age, DCIS grade, and progesterone receptor status.

RESULTS. Of the suspicious calcifications that proved to represent DCIS, 19.0% were amorphous; 25.9%, coarse heterogeneous; 39.7%, fine pleomorphic; and 15.5%, fine linear or fine linear branching in morphology. The mean DCIS scores by calcification morphology were 22.3, 35.5, 36.7, and 44.1, respectively. Amorphous calcification morphology had a significantly lower adjusted mean DCIS score compared with fine pleomorphic morphology (p = 0.01) and fine linear or fine linear branching morphology (p = 0.02). The adjusted odds ratio (OR) of intermediate or high risk of recurrence (defined as a DCIS score ≥ 39) was significantly higher for women with fine pleomorphic calcifications (OR = 53.1, p = 0.01) and for those with fine linear or fine linear branching calcifications (OR = 24.0, p = 0.04) than for women with amorphous calcifications.

CONCLUSION. Women with amorphous calcification morphology had the lowest DCIS scores compared with women with fine pleomorphic and fine linear or fine linear branching morphologies. Both fine pleomorphic and fine linear or fine linear branching morphologies were associated with higher odds of intermediate or high risk of recurrence. These findings suggest mammographic features are potential biomarkers of DCIS recurrence and could help individualize treatment decisions.

Keywords: biomarkers, calcifications, ductal carcinoma in situ (DCIS), Oncotype DX

References
Previous section
1. Erbas B, Provenzano E, Armes J, Gertig D. The natural history of ductal carcinoma in situ of the breast: a review. Breast Cancer Res Treat 2006; 97:135–144 [Google Scholar]
2. Fisher B, Land S, Mamounas E, Dignam J, Fisher ER, Wolmark N. Prevention of invasive breast cancer in women with ductal carcinoma in situ: an update of the National Surgical Adjuvant Breast and Bowel Project experience. Semin Oncol 2001; 28:400–418 [Google Scholar]
3. Holmberg L, Garmo H, Granstrand B, et al. Absolute risk reductions for local recurrence after postoperative radiotherapy after sector resection for ductal carcinoma in situ of the breast. J Clin Oncol 2008; 26:1247–1252 [Google Scholar]
4. Donker M, Litiere S, Werutsky G, et al. Breast-conserving treatment with or without radiotherapy in ductal carcinoma in situ: 15-year recurrence rates and outcome after a recurrence, from the EORTC 10853 randomized phase III trial. J Clin Oncol 2013; 31:4054–4059 [Google Scholar]
5. Toss M, Miligy I, Thompson AM, et al. Current trials to reduce surgical intervention in ductal carcinoma in situ of the breast: critical review. Breast2017; 35:151–156 [Google Scholar]
6. Lalani N, Rakovitch E. Improving therapeutic ratios with the Oncotype DX® Ductal Carcinoma In Situ (DCIS) score. Cureus 2017; 9:e1185 [Google Scholar]
7. Lagios MD, Silverstein MJ. Risk of recurrence of ductal carcinoma in situ by Oncotype DX technology: some concerns. Cancer 2014; 120:1085 [Google Scholar]
8. Solin LJ, Gray R, Baehner FL, et al. A multigene expression assay to predict local recurrence risk for ductal carcinoma in situ of the breast. J Natl Cancer Inst 2013; 105:701–710 [Google Scholar]
9. Wong JS, Chen YH, Gadd MA, et al. Eight-year update of a prospective study of wide excision alone for small low- or intermediate-grade ductal carcinoma in situ (DCIS). Breast Cancer Res Treat 2014; 143:343–350 [Google Scholar]
10. Alvarado M, Carter DL, Guenther JM, et al. The impact of genomic testing on the recommendation for radiation therapy in patients with ductal carcinoma in situ: a prospective clinical utility assessment of the 12-gene DCIS score result. J Surg Oncol 2015; 111:935–940 [Google Scholar]
11. Rakovitch E, Nofech-Mozes S, Hanna W, et al. A population-based validation study of the DCIS score predicting recurrence risk in individuals treated by breast-conserving surgery alone. Breast Cancer Res Treat 2015; 152:389–398 [Google Scholar]
12. Evans A, Pinder S, Wilson R, et al. Ductal carcinoma in situ of the breast: correlation between mammographic and pathologic findings. AJR1994; 162:1307–1311 [Google Scholar]
13. Stomper PC, Connolly JL, Meyer JE, Harris JR. Clinically occult ductal carcinoma in situ detected with mammography: analysis of 100 cases with radiologic-pathologic correlation. Radiology 1989; 172:235–241 [Google Scholar]
14. Dershaw DD, Abramson A, Kinne DW. Ductal carcinoma in situ: mammographic findings and clinical implications. Radiology 1989; 170:411–415 [Google Scholar]
15. Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: association between Oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology 2017; 286:60–70 [Google Scholar]
16. Dialani V, Gaur S, Mehta TS, et al. Prediction of low versus high recurrence scores in estrogen receptor-positive, lymph node-negative invasive breast cancer on the basis of radiologic-pathologic features: comparison with Oncotype DX test recurrence scores. Radiology 2016; 280:370–378 [Google Scholar]
17. Yepes MM, Romilly AP, Collado-Mesa F, et al. Can mammographic and sonographic imaging features predict the Oncotype DX recurrence score in T1 and T2, hormone receptor positive, HER2 negative and axillary lymph node negative breast cancers? Breast Cancer Res Treat 2014; 148:117–123 [Google Scholar]
18. D'Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology, 2013 [Google Scholar]
19. Tabár L, Chen HH, Duffy SW, et al. A novel method for prediction of long-term outcome of women with T1a, T1b, and 10–14 mm invasive breast cancers: a prospective study. Lancet 2000; 355:429–433 [Google Scholar]
20. Thurfjell E, Thurfjell MG, Lindgren A. Mammographic finding as predictor of survival in 1–9 mm invasive breast cancers: worse prognosis for cases presenting as calcifications alone. Breast Cancer Res Treat 2001; 67:177–180 [Google Scholar]
21. Bennett RL, Evans AJ, Kutt E, et al. Pathological and mammographic prognostic factors for screen detected cancers in a multi-centre randomised, controlled trial of mammographic screening in women from age 40 to 48 years. Breast 2011; 20:525–528 [Google Scholar]
22. Rominger MB, Steinmetz C, Westerman R, Ramaswamy A, Albert US. Microcalcification-associated breast cancer: presentation, successful first excision, long-term recurrence and survival rate. Breast Care (Basel) 2015; 10:380–385 [Google Scholar]
23. Zunzunegui RG, Chung MA, Oruwari J, Golding D, Marchant DJ, Cady B. Casting-type calcifications with invasion and high-grade ductal carcinoma in situ: a more aggressive disease? Arch Surg 2003; 138:537–540 [Google Scholar]
24. Holmberg L, Wong YN, Tabár L, et al. Mammography casting-type calcification and risk of local recurrence in DCIS: analyses from a randomised study. Br J Cancer 2013; 108:812–819 [Google Scholar]
25. Tse GM, Tan PH, Cheung HS, Chu WC, Lam WW. Intermediate to highly suspicious calcification in breast lesions: a radio-pathologic correlation. Breast Cancer Res Treat 2008; 110:1–7 [Google Scholar]
26. Pilewskie M, Olcese C, Patil S, Van Zee KJ. Women with low-risk DCIS eligible for the LORIS Trial after complete surgical excision: how low is their risk after standard therapy? Ann Surg Oncol 2016; 23:4253–4261 [Google Scholar]
27. Elshof LE, Tryfonidis K, Slaets L, et al. Feasibility of a prospective, randomised, open-label, international multicentre, phase III, non-inferiority trial to assess the safety of active surveillance for low risk ductal carcinoma in situ: the LORD study. Eur J Cancer 2015; 51:1497–1510 [Google Scholar]
28. Youngwirth LM, Boughey JC, Hwang ES. Surgery versus monitoring and endocrine therapy for low-risk DCIS: the COMET trial. Bull Am Coll Surg 2017; 102:62–63 [Google Scholar]
29. Groen EJ, Elshof LE, Visser LL, et al. Finding the balance between over- and under-treatment of ductal carcinoma in situ (DCIS). Breast 2017; 31:274–283 [Google Scholar]
Address correspondence to G. A. Woodard ().

Full Text | PDF (650 KB) | PDF Plus (680 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 925-932
Posted online on February 11, 2019.
(https://doi.org/10.2214/AJR.18.20421) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Women's Imaging

Original Research

Overstated Harms of Breast Cancer Screening? A Large Outcomes Analysis of Complications Associated With 9-Gauge Stereotactic Vacuum-Assisted Breast Biopsy

Share Share Claim CREDIT
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 925-932. 10.2214/AJR.18.20421

ABSTRACT :

OBJECTIVE. The purpose of this study was to assess the rate, type, and severity of complications related to 9-gauge stereotactic vacuum-assisted breast biopsy (SVAB) and to delineate associated factors that may contribute to a higher rate of complications.

MATERIALS AND METHODS. This retrospective study included 4776 patients who underwent SVAB between 2003 and 2016. A total of 319 patients with documented postbiopsy complications were identified. Complications were subcategorized as bleeding, pain, lightheadedness, bruising, and other complications, and their severity was classified as minor, moderate, or severe. Hematoma volumes were correlated with biopsy location and complication severity. A group of control subjects who underwent SVAB but had no complications was compared with the group of study patients with regard to age, biopsy location, lesion type, and pathologic findings. Postbiopsy screening adherence was assessed. Statistical analyses were performed using the Fisher exact, Mann-Whitney, Kruskal-Wallis, and Spearman rank correlation tests.

RESULTS. Of the 319 patients with complications who were identified (representing 6.7% of the 4776 patients who underwent SVAB), 307 (96.2%) had mild complications, 12 (3.8%) had moderate complications, and no patients had severe complications. The most common complication was bleeding or hematoma (89.3% of patients [285/319]), followed by pain (6.9% [22/319]), lightheadedness (0.9% [3/319]), bruising (0.9% [3/319]), and other complications (1.9% [6/319]). No significant differences were noted between the study group and the control group in terms of age (p = 0.474), biopsy location (p = 0.065), histologic findings (p = 0.056), or lesion type (p = 0.568). Hematoma volume (median, 7.5 cm3) did not correspond to the severity of complications. Larger hematoma volumes were associated with a posterior biopsy location (p = 0.008). The rate of return to annual screening after biopsy was not adversely affected by the presence of biopsy complications.

CONCLUSION. Clinically significant complications associated with SVAB were exceedingly rare (0.3%) in this large study spanning 13 years.

Keywords: breast, complications, mammography, screening mammography, stereotactic breast biopsy

Based on a presentation at the Radiological Society of North America 2017 annual meeting, Chicago, IL.

References
Previous section
1. Tabár L, Vitak B, Chen HH, Yen MF, Duffy SW, Smith RA. Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 2001; 91:1724–1731 [Google Scholar]
2. Duffy SW, Tabár L, Chen HH, et al. The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties. Cancer 2002; 95:458–469 [Google Scholar]
3. Hendrick RE, Smith RA, Rutledge JH 3rd, Smart CR. Benefit of screening mammography in women aged 40-49: a new meta-analysis of randomized controlled trials. J Natl Cancer Inst Monogr 1997; 22:87–92 [Google Scholar]
4. Hardesty LA, Lind KE, Gutierrez EJ. Compliance with screening mammography guidelines after a false-positive mammogram. J Am Coll Radiol2016; 13:1032–1038 [Google Scholar]
5. Smith RA, Duffy SW, Gabe R, Tabar L, Yen AM, Chen TH. The randomized trials of breast cancer screening: what have we learned? Radiol Clin North Am 2004; 42:793–806 [Google Scholar]
6. Kopans DB. Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer2002; 94:580–581; author reply, 581–583 [Google Scholar]
7. Allison KH, Abraham LA, Weaver DL, et al. Trends in breast biopsy pathology diagnoses among women undergoing mammography in the United States: a report from the Breast Cancer Surveillance Consortium. Cancer 2015; 121:1369–1378 [Google Scholar]
8. U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2009; 151:716–726, W-236 [Google Scholar]
9. U.S. Preventive Services Task Force. Screening for breast cancer: recommendations and rationale. Ann Intern Med 2002; 137:344–346 [Google Scholar]
10. Bruening W, Tipton K, Tipton K, Treadwell JR, Launders J, Schoelles K. Systematic review: comparative effectiveness of core-needle and open surgical biopsy to diagnose breast lesions. Ann Intern Med 2010; 152:238–246 [Google Scholar]
11. Fajardo LLPE, Caudry DJ, Gatsonis CA, et al. Stereotactic and sonographic large-core biopsy of nonpalpable breast lesions: results of the Radiologic Diagnostic Oncology Group V study. Acad Radiol 2004; 11:293–308 [Google Scholar]
12. White RR, Haplerin TJ, Olson JA Jr, Soo MS, Bentley RC, Seigler HF. Impact of core-needle breast biopsy on the surgical management of mammographic abnormalities. Ann Surg 2001; 233:769–777 [Google Scholar]
13. Burkhardt JH, Sunshine JH. Core-needle and surgical breast biopsy: comparison of three methods of assessing cost. Radiology 1999; 212:181–188 [Google Scholar]
14. Humphrey KL, Lee JM, Donelan K, et al. Percutaneous breast biopsy: effect on short-term quality of life. Radiology 2014; 270:362–368 [Google Scholar]
15. Parker SH, Burbank F, Jackman RJ, et al. Percutaneous large-core breast biopsy: a multi-institutional study. Radiology 1994; 193:359–364 [Google Scholar]
16. Burbank F. Stereotactic breast biopsy: comparison of 14- and 11-gauge Mammotome probe performance and complication rates. Am Surg1997; 63:988–995 [Google Scholar]
17. Melotti MK, Berg WA. Core needle breast biopsy in patients undergoing anticoagulation therapy: preliminary results. AJR 2000; 174:245–249 [Google Scholar]
18. Pfleiderer SO, Brunzlow H, Schulz-Wendtland R, et al. Two-year follow-up of stereotactically guided 9-G breast biopsy: a multicenter evaluation of a self-contained vacuum-assisted device. Clin Imaging 2009; 33:343–347 [Google Scholar]
19. Safioleas PM, Koulocheri D, Michalopoulos N, et al. The value of stereotactic vacuum assisted breast biopsy in the investigation of microcalcifications: a six-year experience with 853 patients. J BUON 2017; 22:340–347 [Google Scholar]
20. Mariscotti G, Durando M, Robella M, et al. Mammotome(®) and EnCor(®): comparison of two systems for stereotactic vacuum-assisted core biopsy in the characterisation of suspicious mammographic microcalcifications alone. Radiol Med 2015; 120:369–376 [Google Scholar]
21. Diebold T, Hahn T, Solbach C, et al. Evaluation of the stereotactic 8G vacuum-assisted breast biopsy in the histologic evaluation of suspicious mammography findings (BI-RADS IV). Invest Radiol 2005; 40:465–471 [Google Scholar]
22. Lifrange E, Dondelinger RF, Quatresooz P, Vandevorst G, Colin C. Stereotactic breast biopsy with an 8-gauge, directional, vacuum-assisted probe: initial experience. Eur Radiol 2002; 12:2180–2187 [Google Scholar]
23. Hahn M, Okamgba S, Scheler P, et al. Vacuum-assisted breast biopsy: a comparison of 11-gauge and 8-gauge needles in benign breast disease. World J Surg Oncol 2008; 6:51 [Google Scholar]
24. Wang ZL, Liu G, Huang Y, Wan WB, Li JL. Percutaneous excisional biopsy of clinically benign breast lesions with vacuum-assisted system: comparison of three devices. Eur J Radiol 2012; 81:725–730 [Google Scholar]
25. Ruggirello I, Nori J, Desideri I, et al. Stereotactic vacuum-assisted breast biopsy: comparison between 11- and 8-gauge needles. Eur J Surg Oncol 2017; 43:2257–2260 [Google Scholar]
26. Chetlen AL, Kasales C, Mack J, Schetter S, Zhu J. Hematoma formation during breast core needle biopsy in women taking antithrombotic therapy. AJR 2013; 201:215–222 [Google Scholar]
27. Schaefer FK, Order BM, Eckmann-Scholz C, et al. Interventional bleeding, hematoma and scar-formation after vacuum-biopsy under stereotactic guidance: Mammotome(®)-system 11 g/8 g vs. ATEC(®)-system 12 g/9 g. Eur J Radiol 2012; 81:e739–e745 [Google Scholar]
28. Lewin AA, Gao Y, Lin Young LL, et al. Stereotactic breast biopsy with benign results does not negatively affect future screening adherence. J Am Coll Radiol 2018; 15:622–629 [Google Scholar]
29. Bruening W, Schoelles K, Treadwell J, Launders J, Fontanarosa J, Tipton K. Comparative effectiveness of core-needle and open surgical biopsy for the diagnosis of breast lesions. Rockville, MD: Agency for Healthcare Research and Quality, 2009 [Google Scholar]
30. Jesinger RA, Lattin GE Jr, Ballard EA, Zelasko SM, Glassman LM. Vascular abnormalities of the breast: arterial and venous disorders, vascular masses, and mimic lesions with radiologic-pathologic correlation. RadioGraphics 2011; 31:E117–E136 [Google Scholar]
31. Somerville P, Seifert PJ, Destounis SV, Murphy PF, Young W. Anticoagulation and bleeding risk after core needle biopsy. AJR 2008; 191:1194–1197 [Google Scholar]
32. Somerville P. Stereotactic breast biopsy: comparison of 14- and 11-gauge Mammotome probe performance and complication rates. Am Surg1997; 63:988–995 [Google Scholar]
33. Philpotts LE, Hooley RJ, Lee CH. Comparison of automated versus vacuum-assisted biopsy methods for sonographically guided core biopsy of the breast. AJR 2003; 180:347–351 [Google Scholar]
34. Lee KE, Kim HH, Shin HJ, Cha JH. Stereotactic biopsy of the breast using a decubitus table: comparison of histologic underestimation rates between 11- and 8-gauge vacuum-assisted breast biopsy. Springerplus 2013; 2:551 [Google Scholar]
35. Viala J, Gignier P, Perret B, et al. Stereotactic vacuum-assisted biopsies on a digital breast 3D-tomosynthesis system. Breast J 2013; 19:4–9 [Google Scholar]
36. Schrading S, Distelmaier M, Dirrichs T, et al. Digital breast tomosynthesis-guided vacuum-assisted breast biopsy: initial experiences and comparison with prone stereotactic vacuum-assisted biopsy. Radiology 2015; 274:654–662 [Google Scholar]
37. Esen G, Tutar B, Uras C, Calay Z, Ince U, Tutar O. Vacuum-assisted stereotactic breast biopsy in the diagnosis and management of suspicious microcalcifications. Diagn Interv Radiol 2016; 22:326–333 [Google Scholar]
38. Waldherr C, Berclaz G, Altermatt HJ, et al. Tomosynthesis-guided vacuum-assisted breast biopsy: a feasibility study. Eur Radiol 2016; 26:1582–1589 [Google Scholar]
39. Eller A, Janka R, Lux M, et al. Stereotactic vacuum-assisted breast biopsy (VABB): a patients' survey. Anticancer Res 2014; 34:3831–3837 [Google Scholar]
40. Miller SJ, Sohl SJ, Schnur JB, et al. Pre-biopsy psychological factors predict patient biopsy experience. Int J Behav Med 2014; 21:144–148 [Google Scholar]
Address correspondence to L. L. Young Lin ().

Full Text | PDF (818 KB) | PDF Plus (858 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 933-942
Posted online on February 19, 2019.
(https://doi.org/10.2214/AJR.18.20138) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Women's Imaging

Original Research

Ability of Dual-Energy CT to Detect Silicone Gel Breast Implant Rupture and Nodal Silicone Spread

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: 933-942. 10.2214/AJR.18.20138

ABSTRACT :

OBJECTIVE. The purpose of this study is to compare the performance of dual-energy CT (DECT) with that of breast MRI for detection of silicone gel breast implant rupture and nodal spread of silicone.

SUBJECTS AND METHODS. This prospective study enrolled consecutive patients with current or prior silicone gel implants and clinical suspicion of implant rupture or extra-capsular silicone. All patients underwent MRI followed by unenhanced DECT. A breast radiologist not participating in image evaluation established reference standards for implant rupture (intra- or extracapsular) and regional nodal silicone spread (to axillary nodes and internal mammary [IM] and mediastinal nodes) using MRI, surgical findings, and medical records. After undergoing reader training, two radiologists who were blinded to all medical records interpreted randomized images in two sessions, indicating confidence in diagnosis using a 100-point visual scale.

RESULTS. A total of 46 patients who had a subpectoral silicone gel implant (n = 31), a subglandular silicone gel implant (n = 14), or a silicone gel implant that was removed (n = 1) underwent MRI and DECT (mean [± SD] volume CT dose index, 8.2 ± 2.0 mGy). Nineteen patients had implant rupture, and 13 of these patients had silicone within the IM or axillary nodes. Pooled data showed no significant difference between MRI and DECT interpretation of intra- or extracapsular rupture of implants (AUC value for intracapsular rupture, 0.958 [for MRI] vs 0.989 [for DECT]; p = 0.28; AUC value for extracapsular rupture, 0.864 [for MRI] vs 0.878 [for DECT]; p = 0.78). No difference was noted in the AUC value for silicone spread to regional lymph nodes: 0.823–0.866 [for MRI] vs 0.892–0.906 [for DECT]; p = 0.34–0.54).

CONCLUSION. DECT performs similar to MRI for the detection of silicone gel implant rupture and the presence of silicone in regional lymph nodes, and it may be an alternative for patients who are unable or unwilling to undergo MRI.

Keywords: breast implant, dual-energy CT, implant rupture, MRI, silicone

Mayo Clinic receives a grant from Siemens Healthineers and provided the CT system used in this research.

Based on a presentation at the Radiological Society of North America 2017 annual meeting, Chicago, IL.

Supported in part by a research grant from Siemens Healthcare.

References
Previous section
1. Wong T, Lo LW, Fung PY, et al. Magnetic resonance imaging of breast augmentation: a pictorial review. Insights Imaging 2016; 7:399–410 [Google Scholar]
2. U.S. Food and Drug Administration. FDA update on the safety of silicone gel-filled breast implants. FDA website.www.fda.gov/downloads/MedicalDevices/ProductsandMedicalProcedures/ImplantsandProsthetics/BreastImplants/UCM260090.pdf. Published June 2011. Accessed January 30, 2018 [Google Scholar]
3. Yang N, Muradali D. The augmented breast: a pictorial review of the abnormal and unusual. AJR 2011; 196:[web]W451–W460 [Google Scholar]
4. Brown SL, Middleton MS, Berg WA, Soo MS, Pennello G. Prevalence of rupture of silicone gel breast implants revealed on MR imaging in a population of women in Birmingham, Alabama. AJR 2000; 175:1057–1064 [Google Scholar]
5. Soo MS, Kornguth PJ, Walsh R, et al. Intracapsular implant rupture: MR findings of incomplete shell collapse. J Magn Reson Imaging 1997; 7:724–730 [Google Scholar]
6. Seiler SJ, Sharma PB, Hayes JC, et al. Multimodality imaging-based evaluation of single-lumen silicone breast implants for rupture. RadioGraphics 2017; 37:366–382 [Google Scholar]
7. Ahn CY, Shaw WW. Regional silicone-gel migration in patients with ruptured implants. Ann Plast Surg 1994; 33:201–208 [Google Scholar]
8. Berg WA, Nguyen TK, Middleton MS, Soo MS, Pennello G, Brown SL. MR imaging of extracapsular silicone from breast implants: diagnostic pitfalls. AJR 2002; 178:465–472 [Google Scholar]
9. Zambacos GJ, Molnar C, Mandrekas AD. Silicone lymphadenopathy after breast augmentation: case reports, review of the literature, and current thoughts. Aesthetic Plast Surg 2013; 37:278–289 [Google Scholar]
10. Uğurluer G, Kibar M, Yavuz S, Kuzucu A, Serin M. False positive 18F-FDG uptake in mediastinal lymph nodes detected with positron emission tomography in breast cancer: a case report. Case Rep Med 2013; 2013:459753 [Google Scholar]
11. Adams ST, Cox J, Rao GS. Axillary silicone lymphadenopathy presenting with a lump and altered sensation in the breast: a case report. J Med Case Rep 2009; 3:6442 [Google Scholar]
12. Ahn CY, DeBruhl ND, Gorczyca DP, Shaw WW, Bassett LW. Comparative silicone breast implant evaluation using mammography, sonography, and magnetic resonance imaging: experience with 59 implants. Plast Reconstr Surg 1994; 94:620–627 [Google Scholar]
13. Grubstein A, Cohen M, Steinmetz A, Cohen D. Siliconomas mimicking cancer. Clin Imaging 2011; 35:228–231 [Google Scholar]
14. Kao CC, Rand RP, Holt CA, Pierce RH, Timmons JH, Wood DE. Internal mammary silicone lymph-adenopathy mimicking recurrent breast cancer. Plast Reconstr Surg 1997; 99:225–229 [Google Scholar]
15. Gurvits GE. Silicone pneumonitis after a cosmetic augmentation procedure. N Engl J Med 2006; 354:211–212 [Google Scholar]
16. Pfleiderer B, Campbell T, Hulka CA, et al. Silicone gel-filled breast implants in women: findings at H-1 MR spectroscopy. Radiology 1996; 201:777–783 [Google Scholar]
17. Johnson TR, Himsl I, Hellerhoff K, et al. Dual-energy CT for the evaluation of silicone breast implants. Eur Radiol 2013; 23:991–996 [Google Scholar]
18. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44:837–845 [Google Scholar]
19. Di Benedetto G, Cecchini S, Grassetti L, et al. Comparative study of breast implant rupture using mammography, sonography, and magnetic resonance imaging: correlation with surgical findings. Breast J 2008; 14:532–537 [Google Scholar]
20. Goodman CM, Cohen V, Thornby J, Netscher D. The life span of silicone gel breast implants and a comparison of mammography, ultrasonography, and magnetic resonance imaging in detecting implant rupture: a meta-analysis. Ann Plast Surg 1998; 41:577–585; discussion, 585–576 [Google Scholar]
21. Hölmich LR, Vejborg I, Conrad C, Sletting S, McLaughlin JK. The diagnosis of breast implant rupture: MRI findings compared with findings at explantation. Eur J Radiol 2005; 53:213–225 [Google Scholar]
22. Ikeda DM, Borofsky HB, Herfkens RJ, Sawyer-Glover AM, Birdwell RL, Glover GH. Silicone breast implant rupture: pitfalls of magnetic resonance imaging and relative efficacies of magnetic resonance, mammography, and ultrasound. Plast Reconstr Surg 1999; 104:2054–2062 [Google Scholar]
23. Heden P, Nava MB, van Tetering JP, et al. Prevalence of rupture in inamed silicone breast implants. Plast Reconstr Surg 2006; 118:303–308; discussion, 309–312 [Google Scholar]
24. Middleton MS, McNamara MP. Breast implant imaging. Philadelphia, PA: Lippincott Williams & Wilkins, 2003 [Google Scholar]
25. Sutton EJ, Watson EJ, Gibbons G, et al. Incidence of internal mammary lymph nodes with silicone breast implants at MR imaging after oncoplastic surgery. Radiology 2015; 277:381–387 [Google Scholar]
26. Doerge S, Glazebrook KN, Leng S, McCollough CM. Ultility of dual-energy CT for evaluation of silicone within internal mammary nodes. Austin J Clin Case Rep 2017; 4:1112 [Google Scholar]
27. Glazebrook KN, Leng S, Jacobson SR, McCollough CM. Dual-energy CT for evaluation of intra- and extracapsular silicone implant rupture. Case Rep Radiol 2016; 2016:6323709 [Google Scholar]
28. Smathers RL, Boone JM, Lee LJ, Berns EA, Miller RA, Wright AM. Radiation dose reduction for augmentation mammography. AJR 2007; 188:1414–1421 [Google Scholar]
29. Prionas ND, Lindfors KK, Ray S, et al. Contrast-enhanced dedicated breast CT: initial clinical experience. Radiology 2010; 256:714–723 [Google Scholar]
Address correspondence to J. G. Fletcher ().

Full Text | PDF (1073 KB) | PDF Plus (871 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. 943-946
Posted online on January 23, 2019.
(https://doi.org/10.2214/AJR.18.20363) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Women's Imaging

Technical Innovation

Tomosynthesis-Guided Needle Localization of Breast and Axillary Lesions: Our Initial Experience

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: 943-946. 10.2214/AJR.18.20363

ABSTRACT :

OBJECTIVE. The purpose of this study is to review tomosynthesis-guided wire and seed needle localizations of the breast and axilla performed at our institution.

CONCLUSION. Tomosynthesis-guided needle localizations were performed for 38 lesions, including 14 architectural distortions, five groups of calcifications, two focal asymmetries, three masses, four breast clips, and 10 axillary clips. All lesions were successfully removed at surgery, indicating that breast and axillary lesions can be precisely localized under tomosynthesis.

Keywords: axilla, breast, localization, surgery, tomosynthesis

References
Previous section
1. Schrading S, Distelmaier M, Dirrichs T, et al. Digital breast tomosynthesis-guided vacuum-assisted breast biopsy: initial experiences and comparison with prone stereotactic vacuum-assisted biopsy. Radiology 2015; 274:654–662 [Google Scholar]
2. Freer PE, Niell B, Rafferty EA. Preoperative tomosynthesis-guided needle localization of mammographically and sonographically occult breast lesions. Radiology 2015; 275:377–383 [Google Scholar]
3. Durand MA, Wang S, Hooley RJ, Raghu M, Philpotts LE. Tomosynthesis-detected architectural distortion: management algorithm with radiologic-pathologic correlation. RadioGraphics 2016; 36:311–321 [Google Scholar]
4. Nguyen TT, Hieken TJ, Glazebrook KN, Boughey JC. Localizing the clipped node in patients with node-positive breast cancer treated with neoadjuvant chemotherapy: early learning experience and challenges. Ann Surg Oncol 2017; 24:3011–3016 [Google Scholar]
5. Bloomquist EV, Ajkay N, Patil S, Collett AE, Frazier TG, Barrio AV. A randomized prospective comparison of patient-assessed satisfaction and clinical outcomes with radioactive seed localization versus wire localization. Breast J 2016; 22:151–157 [Google Scholar]
6. Chan BK, Wiseberg-Firtell JA, Jois RH, Jensen K, Audisio RA. Localization techniques for guided surgical excision of non-palpable breast lesions. Cochrane Database Syst Rev 2015; 12:CD009206 [Google Scholar]
7. Haas BM, Kalra V, Geisel J, Raghu M, Durand M, Philpotts LE. Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening. Radiology 2013; 269:694–700 [Google Scholar]
8. Noroozian M, Hadjiiski L, Rahnama-Moghadam S, et al. Digital breast tomosynthesis is comparable to mammographic spot views for mass characterization. Radiology 2012; 262:61–68 [Google Scholar]
Address correspondence to S. Choudhery ().

Full Text | PDF (639 KB) | PDF Plus (646 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W106-W106
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20803) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Medicolegal—Malpractice and Ethical Issues in Radiology
Referring Physician's Receipt of Radiology Report With Abnormal Findings May Be Guaranteed, but Is the Report Read and Action Taken?

Share Share
+ Affiliations:

Citation: American Journal of Roentgenology. 2019;212: W106-W106. 10.2214/AJR.18.20803

WEB—This is a web exclusive article.

All opinions expressed herein are those of the author and do not necessarily reflect those of the AJR or the ARRS.

This monthly column answers common professional liability questions. The legal advice provided herein is intended to be general in nature and in specific circumstances is not a substitute for formal legal opinions obtained from the reader's personal legal counsel.

Reference

1. Berlin L. Legal outcome of a failure to communicate an unexpected finding. J Am Coll Radiol 2018; 15:1356–1358 [Google Scholar]
Full Text | PDF (503 KB) | PDF Plus (506 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W107-W108
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20379) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Hallway Conversations in Physics
What Is Peak Skin Dose?

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W107-W108. 10.2214/AJR.18.20379

WEB—This is a web exclusive article.

References

1. International Electrotechnical Commission. Medical electrical equipment. Part 2-43. Particular requirements for the safety of X-ray equipment for interventional procedures (IEC 60601-2-43). Geneva, Switzerland: International Electrotechnical Commission, 2000 [Google Scholar]
2. Balter S, Hopewell JW, Miller DL, Wagner LK, Zelefsky MJ. Fluoroscopically guided interventional procedures: a review of radiation effects on patients' skin and hair. Radiology 2010; 254:326–341 [Google Scholar]
3. Wagner LK, Eifel PJ, Geise RA. Potential biological effects following high x-ray dose interventional procedures. J Vasc Interv Radiol 1994; 5:71–84 [Google Scholar]
4. Stecker MS, Balter S, Towbin RB, et al; SIR Safety and Health Committee. CIRSE Standards of Practice Committee: guidelines for patient radiation dose management. J Vasc Interv Radiol 2009; 20(suppl 7):S263–S273 [Google Scholar]
5. Jones AK, Pasciak AS. Calculating the peak skin dose resulting from fluoroscopically guided interventions. Part I. Methods. J Appl Clin Med Phys 2011; 12:3670 [Google Scholar]
6. Balter S. Methods for measuring fluoroscopic skin dose. Pediatr Radiol 2006; 36(suppl 2):136–140 [Google Scholar]
7. National Council on Radiation Protection and Measurements. Radiation dose management for fluoroscopically-guided interventional medical procedures: NCRP report 168. Bethesda, MD: National Council on Radiation Protection and Measurements, 2010 [Google Scholar]
Full Text | PDF (565 KB) | PDF Plus (566 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W109-W109
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20645) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Methodologic Issues in Prediction of Posthepatectomy Liver Failure

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W109-W109. 10.2214/AJR.18.20645

WEB—This is a web exclusive article.

References

1. Kim DK, Choi JI, Choi MH, et al. Prediction of posthepatectomy liver failure: MRI with hepatocyte-specific contrast agent versus indocyanine green clearance test. AJR 2018; 211:580–587 [Google Scholar]
2. Grobbee DE, Hoes AW. Clinical epidemiology: principles, methods, and applications for clinical research, 2nd ed. Burlington, MA: Jones and Bartlett, 2015 [Google Scholar]
3. Szklo M, Nieto FJ. Epidemiology beyond the basics, 3rd ed. New York, NY: Jones and Bartlett, 2014 [Google Scholar]
4. Sabour S. Prediction of preterm delivery using levels of VEGF and leptin in amniotic fluid from the second trimester: prediction rules. Arch Gynecol Obstet 2015; 291:719 [Google Scholar]
5. Sabour S, Ghassemi F. Predictive value of confocal scanning laser for the onset of visual field loss. Ophthalmology 2013; 120:e31–e32 [Google Scholar]
Full Text | PDF (509 KB) | PDF Plus (513 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W110-W110
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20772) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Reply to "Methodologic Issues in Prediction of Posthepatectomy Liver Failure"

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W110-W110. 10.2214/AJR.18.20772

WEB—This is a web exclusive article.

References

1. Abbasi M, Naderi M. Methodologic issues in prediction of posthepatectomy liver failure. (letter) AJR 2019; 212:[web]W109 [Google Scholar]
2. Kim DK, Choi JI, Choi MH, et al. Prediction of posthepatectomy liver failure: MRI with hepatocyte-specific contrast agent versus indocyanine green clearance test. AJR 2018; 211:580–587 [Google Scholar]
Full Text | PDF (501 KB) | PDF Plus (504 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W111-W111
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20671) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

CT Dose Management: Our Experience in Implementing a Program With an Education-Focused Approach

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W111-W111. 10.2214/AJR.18.20671

WEB—This is a web exclusive article.

References

1. Szczykutowicz TP, Bour R, Ranallo F, Pozniak M. The current state of CT dose management across radiology: well intentioned but not universally well executed. AJR 2018; 211:405–408 [Google Scholar]
2. Jin DH, Lamberton GR, Broome DR, et al. Effect of reduced radiation CT protocols on the detection of renal calculi. Radiology 2010; 255:100–107 [Google Scholar]
3. Seyal AR, Arslanoglu A, Abboud SF, Sahin A, Horowitz JM, Yaghmai V. CT of the abdomen with reduced tube voltage in adults: a practical approach. RadioGraphics 2015; 35:1922–1939 [Google Scholar]
4. Costello JE, Cecava ND, Tucker JE, Bau JL. CT radiation dose: current controversies and dose reduction strategies. AJR 2013; 201:1283–1290 [Google Scholar]
Full Text | PDF (506 KB) | PDF Plus (512 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W112-W112
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20777) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Reply to "CT Dose Management: Our Experience in Implementing a Program With an Education-Focused Approach"

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W112-W112. 10.2214/AJR.18.20777

WEB—This is a web exclusive article.

Reference

1. Velasco S, Torres-Cortes D, Aguirre D. CT dose management: our experience in implementing a program with an education-focused approach. (letter) AJR 2019; 212:[web]W111 [Google Scholar]
Full Text | PDF (499 KB) | PDF Plus (500 KB)




American Journal of Roentgenology
April, Vol. 212, No. 4, pp. W113-W113
Posted online on March 22, 2019.
(https://doi.org/10.2214/AJR.18.20724) 
View Table of Contents | RSS TOC, Citation  What is RSS? | Email Alert 

Letters

Use of Bioelectrical Phase Angle in the Estimation of Accurate Body Composition

Share Share
+ Affiliation:

Citation: American Journal of Roentgenology. 2019;212: W113-W113. 10.2214/AJR.18.20724

WEB—This is a web exclusive article.

References

1. Wu Y, Ali I, Teunissen B, et al. Using body mass index and bioelectric impedance analysis to assess the need for positive oral contrast agents before abdominopelvic CT. AJR 2018; 211:340–346 [Google Scholar]
2. Lu HK, Chiang LM, Chen YY, et al. Hand-to-hand model for bioelectrical impedance analysis to estimate fat free mass in a healthy population. Nutrients 2016; 8:654 [Google Scholar]
3. Norman K, Stobäus N, Pirlich M, Bosy-Westphal A. Bioelectrical phase angle and impedance vector analysis: clinical applicability of impedance parameters. Clin Nutr 2012; 31:854–861 [Google Scholar]
4. Bosy-Westphal A, Danielzik S, Dorhofer RP, Later W, Wiese S, Muller MJ. Phase angle from bioelectrical impedance analysis: population reference values by age, sex, and body mass index. JPEN J Parenter Enteral Nutr 2006; 30:309–316 [Google Scholar]
Full Text | PDF (510 KB) | PDF Plus (515 KB)

Αναρτήθηκε από στις

Δεν υπάρχουν σχόλια:

Δημοσίευση σχολίου

Εγγραφή σε: Σχόλια ανάρτησης (Atom)

Δημοφιλείς αναρτήσεις