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Published on: September 16, 2022
Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An
Vignesh A Arasu1, Laurel A Habel1, Ninah S Achacoso1
1From the Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L., C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California, Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology, Southern California Permanente Medical Group, Orange County, Irvine, Calif (J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G., J.K.), University of California-San Francisco, San Francisco, Calif; Department of Medical Imaging Technology and Informatics, Southern California Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics, University of California-Davis, Davis, Calif (D.L.M.); The Technology Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals, Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health and Nash Family Department of Neuroscience (L.S.) and Department of Population Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu, Hawaii (H.C.Y.).
Abstract:
Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.
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