Impact of Molecular Subtype Definitions on AI Classification of Breast Cancer at MRI

HomeRadiologyRecently Published PreviousNext Reviews and CommentaryEditorialImpact of Molecular Subtype Definitions on AI Classification of Breast Cancer at MRIMin Sun Bae Min Sun Bae Author AffiliationsFrom the Department of Radiology, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon 22332, Republic of Korea.Address correspondence to the author (email: [email protected]).Min Sun Bae Published Online:Jan 3 2023https://doi.org/10.1148/radiol.223041MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012;490(7418):61–70. Crossref, Medline, Google Scholar2. Grimm LJ, Johnson KS, Marcom PK, Baker JA, Soo MS. Can breast cancer molecular subtype help to select patients for preoperative MR imaging? Radiology 2015;274(2):352–358. Link, Google Scholar3. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJPanel members. Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011;22(8):1736–1747. Crossref, Medline, Google Scholar4. Sutton EJ, Dashevsky BZ, Oh JH, et al. Breast cancer molecular subtype classifier that incorporates MRI features. J Magn Reson Imaging 2016;44(1):122–129. Crossref, Medline, Google Scholar5. Bismeijer T, van der Velden BHM, Canisius S, et al. Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression. Radiology 2020;296(2):277–287. Link, Google Scholar6. Ji Y, Whitney HM, Li H, Peifang L, Giger ML, Zhang X. Differences in molecular subtype reference standards impact AI-based breast cancer classification with dynamic contrast-enhanced MRI. Radiology 2023. https://doi.org/10.1148/radiol.220984. Published online January 3, 2023. Google Scholar7. Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C. Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 2019;290(2):290–297. Link, Google Scholar8. Mahoney MC, Gatsonis C, Hanna L, DeMartini WB, Lehman C. Positive predictive value of BI-RADS MR imaging. Radiology 2012;264(1):51–58. Link, Google Scholar9. Uematsu T, Kasami M, Yuen S. Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology 2009;250(3):638–647. Link, Google Scholar10. Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 2018;287(3):732–747. Link, Google ScholarArticle HistoryReceived: Nov 24 2022Revision requested: Dec 5 2022Revision received: Dec 8 2022Accepted: Dec 12 2022Published online: Jan 03 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleDifferences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRIJan 3 2023RadiologyRecommended Articles RSNA Education Exhibits RSNA Case Collection Recently Published Metrics Altmetric Score PDF download