Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Unenhanced Breast MRI With Diffusion-Weighted Imaging for Breast Cancer Detection: Effects of Training on Performance and Agreement of Subspecialty Radiologists

  • Yeon Soo Kim (Department of Radiology, Seoul National University Hospital) ;
  • Su Hyun Lee (Department of Radiology, Seoul National University Hospital) ;
  • Soo-Yeon Kim (Department of Radiology, Seoul National University Hospital) ;
  • Eun Sil Kim (Department of Radiology, Seoul National University Hospital) ;
  • Ah Reum Park (Department of Radiology, Seoul National University Hospital) ;
  • Jung Min Chang (Department of Radiology, Seoul National University Hospital) ;
  • Vivian Youngjean Park (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Jung Hyun Yoon (Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Bong Joo Kang (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Bo La Yun (Department of Radiology, Seoul National University College of Medicine) ;
  • Tae Hee Kim (Department of Radiology, Ajou University Medical Center) ;
  • Eun Sook Ko (Department of Radiology and Center for Imaging Science, Samsung Medical Center) ;
  • A Jung Chu (Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center) ;
  • Jin You Kim (Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine) ;
  • Inyoung Youn (Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine) ;
  • Eun Young Chae (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Woo Jung Choi (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Hee Jeong Kim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Soo Hee Kang (Medical Research Collaborating Center, Seoul National University Hospital) ;
  • Su Min Ha (Department of Radiology, Seoul National University Hospital) ;
  • Woo Kyung Moon (Department of Radiology, Seoul National University Hospital)
  • Received : 2023.06.04
  • Accepted : 2023.10.30
  • Published : 2024.01.01
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Abstract

Objective: To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). Materials and Methods: A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm2 was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). Results: Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002). Conclusion: Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

Keywords

Acknowledgement

We thank Hee Jung Shin, MD, a professor at Asan Medical Center and Min Jung Kim, MD, a professor at Severance Hospital, for helping us with organization.

References

  1. Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, et al. Diffusion-weighted MRI for unenhanced breast cancer screening. Radiology 2019;293:504-520 
  2. Baltzer PAT, Bickel H, Spick C, Wengert G, Woitek R, Kapetas P, et al. Potential of noncontrast magnetic resonance imaging with diffusion-weighted imaging in characterization of breast lesions: intraindividual comparison with dynamic contrast-enhanced magnetic resonance imaging. Invest Radiol 2018;53:229-235 
  3. Bickelhaupt S, Laun FB, Tesdorff J, Lederer W, Daniel H, Stieber A, et al. Fast and noninvasive characterization of suspicious lesions detected at breast cancer X-Ray screening: capability of diffusion-weighted MR imaging with MIPs. Radiology 2016;278:689-697 
  4. Rahbar H, Zhang Z, Chenevert TL, Romanoff J, Kitsch AE, Hanna LG, et al. Utility of diffusion-weighted imaging to decrease unnecessary biopsies prompted by breast MRI: a Trial of the ECOG-ACRIN Cancer Research Group (A6702). Clin Cancer Res 2019;25:1756-1765 
  5. Lee SH, Shin HJ, Moon WK. Diffusion-weighted magnetic resonance imaging of the breast: standardization of image acquisition and interpretation. Korean J Radiol 2021;22:9-22 
  6. McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, et al. Intracranial gadolinium deposition after contrast-enhanced MR Imaging. Radiology 2015;275:772-782 
  7. Ha SM, Chang JM, Lee SH, Kim ES, Kim SY, Cho N, et al. Diffusion-weighted MRI at 3.0 T for detection of occult disease in the contralateral breast in women with newly diagnosed breast cancer. Breast Cancer Res Treat 2020;182:283-297 
  8. Kang JW, Shin HJ, Shin KC, Chae EY, Choi WJ, Cha JH, et al. Unenhanced magnetic resonance screening using fused diffusion-weighted imaging and maximum-intensity projection in patients with a personal history of breast cancer: role of fused DWI for postoperative screening. Breast Cancer Res Treat 2017;165:119-128 
  9. Kazama T, Kuroki Y, Kikuchi M, Sato Y, Nagashima T, Miyazawa Y, et al. Diffusion-weighted MRI as an adjunct to mammography in women under 50 years of age: an initial study. J Magn Reson Imaging 2012;36:139-144 
  10. McDonald ES, Hammersley JA, Chou SH, Rahbar H, Scheel JR, Lee CI, et al. Performance of DWI as a rapid unenhanced technique for detecting mammographically occult breast cancer in elevated-risk women with dense breasts. AJR Am J Roentgenol 2016;207:205-216 
  11. Rotili A, Trimboli RM, Penco S, Pesapane F, Tantrige P, Cassano E, et al. Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection. Breast Cancer Res Treat 2020;180:111-120 
  12. Telegrafo M, Rella L, Stabile Ianora AA, Angelelli G, Moschetta M. Unenhanced breast MRI (STIR, T2-weighted TSE, DWIBS): an accurate and alternative strategy for detecting and differentiating breast lesions. Magn Reson Imaging 2015;33:951-955 
  13. Trimboli RM, Verardi N, Cartia F, Carbonaro LA, Sardanelli F. Breast cancer detection using double reading of unenhanced MRI including T1-weighted, T2-weighted STIR, and diffusion-weighted imaging: a proof of concept study. AJR Am J Roentgenol 2014;203:674-681 
  14. Yabuuchi H, Matsuo Y, Sunami S, Kamitani T, Kawanami S, Setoguchi T, et al. Detection of non-palpable breast cancer in asymptomatic women by using unenhanced diffusion-weighted and T2-weighted MR imaging: comparison with mammography and dynamic contrast-enhanced MR imaging. Eur Radiol 2011;21:11-17 
  15. Iima M, Partridge S, Le Bihan D. Diffusion MRI of the breast. 1st ed. Philadelphia: Elsevier, 2023:87 
  16. Shin HJ, Lee SH, Park VY, Yoon JH, Kang BJ, Yun B, et al. Diffusion-weighted magnetic resonance imaging for breast cancer screening in high-risk women: design and imaging protocol of a prospective multicenter study in Korea. J Breast Cancer 2021;24:218-228 
  17. Baltzer P, Mann RM, Iima M, Sigmund EE, Clauser P, Gilbert FJ, et al. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2020;30:1436-1450 
  18. Pesapane F, Rotili A, Penco S, Montesano M, Agazzi GM, Dominelli V, et al. Inter-reader agreement of diffusion-weighted magnetic resonance imaging for breast cancer detection: a multi-reader retrospective study. Cancers (Basel) 2021;13:1978 
  19. Malyarenko D, Fedorov A, Bell L, Prah M, Hectors S, Arlinghaus L, et al. Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 2018;5:011006 
  20. Ota R, Kataoka M, Iima M, Honda M, Kishimoto AO, Miyake KK, et al. Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image. Magn Reson Imaging 2023;98:132-139 
  21. Berg WA, Bandos AI, Zuley ML, Waheed UX. Training radiologists to interpret contrast-enhanced mammography: toward a standardized lexicon. Journal of Breast Imaging 2021;3:176-189 
  22. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-174 
  23. Baltzer PA, Benndorf M, Dietzel M, Gajda M, Camara O, Kaiser WA. Sensitivity and specificity of unenhanced MR mammography (DWI combined with T2-weighted TSE imaging, ueMRM) for the differentiation of mass lesions. Eur Radiol 2010;20:1101-1110 
  24. Kuroki-Suzuki S, Kuroki Y, Nasu K, Nawano S, Moriyama N, Okazaki M. Detecting breast cancer with non-contrast MR imaging: combining diffusion-weighted and STIR imaging. Magn Reson Med Sci 2007;6:21-27 
  25. Belli P, Bufi E, Bonatesta A, Patrolecco F, Padovano F, Giuliani M, et al. Unenhanced breast magnetic resonance imaging: detection of breast cancer. Eur Rev Med Pharmacol Sci 2016;20:4220-4229 
  26. Shin HJ, Chae EY, Choi WJ, Ha SM, Park JY, Shin KC, et al. Diagnostic performance of fused diffusion-weighted imaging using unenhanced or postcontrast T1-Weighted MR imaging in patients with breast cancer. Medicine (Baltimore) 2016;95:e3502 
  27. Mann RM, Kuhl CK, Moy L. Contrast-enhanced MRI for breast cancer screening. J Magn Reson Imaging 2019;50:377-390 
  28. Ha SM, Chang JM, Lee SH, Kim ES, Kim SY, Kim YS, et al. Detection of contralateral breast cancer using diffusion-weighted magnetic resonance imaging in women with newly diagnosed breast cancer: comparison with combined mammography and whole-breast ultrasound. Korean J Radiol 2021;22:867-879 
  29. Radovic N, Ivanac G, Divjak E, Biondic I, Bulum A, Brkljacic B. Evaluation of breast cancer morphology using diffusion-weighted and Dynamic Contrast-Enhanced MRI: intermethod and interobserver agreement. J Magn Reson Imaging 2019;49:1381-1390 
  30. Miglioretti DL, Gard CC, Carney PA, Onega TL, Buist DS, Sickles EA, et al. When radiologists perform best: the learning curve in screening mammogram interpretation. Radiology 2009;253:632-640 
  31. Avendano D, Marino MA, Leithner D, Thakur S, Bernard-Davila B, Martinez DF, et al. Limited role of DWI with apparent diffusion coefficient mapping in breast lesions presenting as non-mass enhancement on dynamic contrast-enhanced MRI. Breast Cancer Res 2019;21:136