Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations

유방영상 판독과 자료체계: 장점과 한계

  • Ji Soo Choi (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • 최지수 (성균관대학교 의과대학 삼성서울병원 영상의학과)
  • Received : 2022.10.23
  • Accepted : 2022.12.13
  • Published : 2023.01.01
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Abstract

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations.

유방영상 판독과 자료 체계(Breast Imaging Reporting and Data System; 이하 BI-RADS)는 유방영상 판독의 표준화 및 판독 질 관리를 위해 유방 병변의 특성을 기술하는 용어 사전(lexicon), 평가 범주(assessment category) 및 처치(management)를 위한 권고들을 제시한 시스템이다. 판독의는 BI-RADS에서 권장하는 표준화된 용어를 이용하여 검사 결과를 임상의에게 간결하고 재현 가능하게 전달할 수 있고, 평가 범주 판정을 통하여 검사에서 발견된 병변이 악성일 가능성을 추정된 확률로 제공하고 이에 따른 처치를 권고할 수 있다. 현재 사용 중인 BI-RADS 5판이 가지는 한계는 표준화된 용어 사전이 정립되지 않은 일부 영역들이 존재한다는 것과 MRI 판정 범주 3-4의 평가 기준이 유방촬영술이나 초음파의 기준에 비해 모호하다는 점이다. BI-RADS의 다음 개정판에는 이러한 한계들을 극복하기 위한 개선안이 포함될 것으로 예상한다.

Keywords

References

  1. Kopans DB, D'Orsi CJ, Adler DED. Breast imaging reporting and data system. Reston, VA: American College of Radiology 1993
  2. D'Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology 2013
  3. Eghtedari M, Chong A, Rakow-Penner R, Ojeda-Fournier H. Current status and future of BI-RADS in multimodality imaging, from the AJR special series on radiology reporting and data systems. AJR Am J Roentgenol 2021;216:860-873 https://doi.org/10.2214/AJR.20.24894
  4. Lam DL, Entezari P, Duggan C, Muyinda Z, Vasquez A, Huayanay J, et al. A phased approach to implementing the breast imaging reporting and data system (BI-RADS) in low-income and middle-income countries. Cancer 2020;126 Suppl 10:2424-2430 https://doi.org/10.1002/cncr.32864
  5. Scheel JR, Peacock S, Orem J, Bugeza S, Muyinda Z, Porter PL, et al. Improving breast ultrasound interpretation in Uganda using a condensed breast imaging reporting and data system. Acad Radiol 2016;23:1271-1277 https://doi.org/10.1016/j.acra.2016.05.018
  6. Taylor K, Britton P, O'Keeffe S, Wallis MG. Quantification of the UK 5-point breast imaging classification and mapping to BI-RADS to facilitate comparison with international literature. Br J Radiol 2011;84:1005-1010 https://doi.org/10.1259/bjr/48490964
  7. Orel SG, Kay N, Reynolds C, Sullivan DC. BI-RADS categorization as a predictor of malignancy. Radiology 1999;211:845-850 https://doi.org/10.1148/radiology.211.3.r99jn31845
  8. Liberman L, Abramson AF, Squires FB, Glassman JR, Morris EA, Dershaw DD. The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 1998;171:35-40 https://doi.org/10.2214/ajr.171.1.9648759
  9. Berg WA, Berg JM, Sickles EA, Burnside ES, Zuley ML, Rosenberg RD, et al. Cancer yield and patterns of follow-up for BI-RADS category 3 after screening mammography recall in the National Mammography Database. Radiology 2020;296:32-41 https://doi.org/10.1148/radiol.2020192641
  10. Lee SE, Lee JH, Han K, Kim EK, Kim MJ, Moon HJ, et al. BI-RADS category 3, 4, and 5 lesions identified at preoperative breast MRI in patients with breast cancer: implications for management. Eur Radiol 2020;30:2773-2781 https://doi.org/10.1007/s00330-019-06620-y
  11. Chikarmane SA, Birdwell RL, Poole PS, Sippo DA, Giess CS. Characteristics, malignancy rate, and follow-up of BI-RADS category 3 lesions identified at breast MR imaging: implications for MR image interpretation and management. Radiology 2016;280:707-715 https://doi.org/10.1148/radiol.2016151548
  12. Grimm LJ, Anderson AL, Baker JA, Johnson KS, Walsh R, Yoon SC, et al. Frequency of malignancy and imaging characteristics of probably benign lesions seen at breast MRI. AJR Am J Roentgenol 2015;205:442-447 https://doi.org/10.2214/AJR.14.13530
  13. Spick C, Bickel H, Polanec SH, Baltzer PA. Breast lesions classified as probably benign (BI-RADS 3) on magnetic resonance imaging: a systematic review and meta-analysis. Eur Radiol 2018;28:1919-1928 https://doi.org/10.1007/s00330-017-5127-y
  14. Hong S, Song SY, Park B, Suh M, Choi KS, Jung SE, et al. Effect of digital mammography for breast cancer screening: a comparative study of more than 8 million Korean women. Radiology 2020;294:247-255 https://doi.org/10.1148/radiol.2019190951
  15. Tran TXM, Kim S, Song H, Lee E, Park B. Association of longitudinal mammographic breast density changes with subsequent breast cancer risk. Radiology 2023;306:e220291
  16. Kim S, Tran TXM, Song H, Ryu S, Chang Y, Park B. Mammographic breast density, benign breast disease, and subsequent breast cancer risk in 3.9 million Korean women. Radiology 2022;304:534-541 https://doi.org/10.1148/radiol.212727
  17. Sickles EA, Miglioretti DL, Ballard-Barbash R, Geller BM, Leung JW, Rosenberg RD, et al. Performance benchmarks for diagnostic mammography. Radiology 2005;235:775-790 https://doi.org/10.1148/radiol.2353040738
  18. Lehman CD, Arao RF, Sprague BL, Lee JM, Buist DS, Kerlikowske K, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 2017;283:49-58 https://doi.org/10.1148/radiol.2016161174
  19. Miglioretti DL, Ichikawa L, Smith RA, Bassett LW, Feig SA, Monsees B, et al. Criteria for identifying radiologists with acceptable screening mammography interpretive performance on basis of multiple performance measures. AJR Am J Roentgenol 2015;204:W486-W491 https://doi.org/10.2214/AJR.13.12313
  20. Choe J, Chikarmane SA, Giess CS. Nonmass findings at breast US: definition, classifications, and differential diagnosis. Radiographics 2020;40:326-335 https://doi.org/10.1148/rg.2020190125
  21. Park KW, Park S, Shon I, Kim MJ, Han BK, Ko EY, et al. Non-mass lesions detected by breast US: stratification of cancer risk for clinical management. Eur Radiol 2021;31:1693-1706 https://doi.org/10.1007/s00330-020-07168-y
  22. Travieso-Aja MM, Maldonado-Saluzzi D, Naranjo-Santana P, Fernandez-Ruiz C, Severino-Rondon W, Rodriguez Rodriguez M, et al. Evaluation of the applicability of BI-RADS® MRI for the interpretation of contrast-enhanced digital mammography. Radiologia (Engl Ed) 2019;61:477-488 https://doi.org/10.1016/j.rxeng.2019.07.003
  23. Knogler T, Homolka P, Hoernig M, Leithner R, Langs G, Waitzbauer M, et al. Application of BI-RADS descriptors in contrast-enhanced dual-energy mammography: comparison with MRI. Breast Care (Basel) 2017;12:212-216 https://doi.org/10.1159/000478899
  24. Kamal RM, Helal MH, Mansour SM, Haggag MA, Nada OM, Farahat IG, et al. Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography? Br J Radiol 2016;89:20160157
  25. Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB. Digital breast tomosynthesis: state of the art. Radiology 2015;277:663-684 https://doi.org/10.1148/radiol.2015141303
  26. van Zelst JCM, Mann RM. Automated three-dimensional breast US for screening: technique, artifacts, and lesion characterization. Radiographics 2018;38:663-683 https://doi.org/10.1148/rg.2018170162
  27. Jiang J, Chen YQ, Xu YZ, Chen ML, Zhu YK, Guan WB, et al. Correlation between three-dimensional ultrasound features and pathological prognostic factors in breast cancer. Eur Radiol 2014;24:1186-1196 https://doi.org/10.1007/s00330-014-3135-8
  28. Tang G, An X, Xiang H, Liu L, Li A, Lin X. Automated breast ultrasound: interobserver agreement, diagnostic value, and associated clinical factors of coronal-plane image features. Korean J Radiol 2020;21:550-560 https://doi.org/10.3348/kjr.2019.0525
  29. Kaplan SS. Automated whole breast ultrasound. Radiol Clin North Am 2014;52:539-546 https://doi.org/10.1016/j.rcl.2014.01.002
  30. Grimm LJ, Rahbar H, Abdelmalak M, Hall AH, Ryser MD. Ductal carcinoma in situ: state-of-the-art review. Radiology 2022;302:246-255 https://doi.org/10.1148/radiol.211839
  31. University of Birmingham. LORIS: a phase III trial of surgery versus active monitoring for low risk ductal carcinoma in situ (DCIS). Available at. https://www.birmingham.ac.uk/Documents/college-mds/trials/crctu/Loris/LORIS-Trial-Protocol-Vn4.0-15.03.16.pdf. Accessed October 1, 2022
  32. Kuerer HM, Vrancken Peeters MTFD, Rea DW, Basik M, De Los Santos J, Heil J. Nonoperative management for invasive breast cancer after neoadjuvant systemic therapy: conceptual basis and fundamental international feasibility clinical trials. Ann Surg Oncol 2017;24:2855-2862 https://doi.org/10.1245/s10434-017-5926-z
  33. Tasoulis MK, Lee HB, Yang W, Pope R, Krishnamurthy S, Kim SY, et al. Accuracy of post-neoadjuvant chemotherapy image-guided breast biopsy to predict residual cancer. JAMA Surg 2020;155:e204103