AI-driven Selection of Candidates for Supplemental Breast Cancer Screening

HomeRadiologyVol. 311, No. 1 PreviousNext Reviews and CommentaryEditorialAI-driven Selection of Candidates for Supplemental Breast Cancer ScreeningMyoung Kyoung Kim, Jung Min Chang Myoung Kyoung Kim, Jung Min Chang Author AffiliationsFrom the Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.K.K.); Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C.); and Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (J.M.C.).Address correspondence to J.M.C. (email: [email protected]).Myoung Kyoung KimJung Min Chang Published Online:Apr 9 2024https://doi.org/10.1148/radiol.240447See also the article by Liu et al in this issue.MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Berg WA, Zhang Z, Lehrer D, et al; ACRIN 6666 Investigators. 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(13):1394–1404. Crossref, Medline, Google Scholar2. Ohuchi N, Suzuki A, Sobue T, et al; J-START investigator groups. Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet 2016;387(10016):341–348. Crossref, Medline, Google Scholar3. Bakker MF, de Lange SV, Pijnappel RM, et al; DENSE Trial Study Group. Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 2019;381(22):2091–2102. Crossref, Medline, Google Scholar4. Monticciolo DL, Newell MS, Moy L, Lee CS, Destounis SV. Breast cancer screening for women at higher-than-average risk: updated recommendations from the ACR. J Am Coll Radiol 2023;20(9):902–914. Crossref, Medline, Google Scholar5. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 2019;292(1):60–66. Link, Google Scholar6. Yala A, Mikhael PG, Strand F, et al. Multi-institutional validation of a mammography-based breast cancer risk model. J Clin Oncol 2022;40(16):1732–1740. Crossref, Medline, Google Scholar7. Damiani C, Kalliatakis G, Sreenivas M, et al. Evaluation of an AI model to assess future breast cancer risk. Radiology 2023;307(5):e222679. Link, Google Scholar8. Arasu VA, Habel LA, Achacoso NS, et al. Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction: an observational study. Radiology 2023;307(5):e222733. Link, Google Scholar9. Lauritzen AD, von Euler-Chelpin MC, Lynge E, et al. Assessing breast cancer risk by combining AI for lesion detection and mammographic texture. Radiology 2023;308(2):e230227. Link, Google Scholar10. Liu Y, Sorkhei M, Dembrower K, Azizpour H, Strand F, Smith K. Use of an AI score combining cancer signs, masking, and risk to select patients for supplemental breast cancer screening. Radiology 2024;311(1):e232535. Google ScholarArticle HistoryReceived: Feb 13 2024Revision requested: Mar 8 2024Revision received: Mar 11 2024Accepted: Mar 12 2024Published online: Apr 09 2024 FiguresReferencesRelatedDetailsAccompanying This ArticleUse of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer ScreeningApr 9 2024RadiologyRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 311, No. 1 Metrics Altmetric Score PDF download