Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer risk models in the Kaiser Permanente Research Bank.
Authors
Affiliations (21)
Affiliations (21)
- Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, United States.
- Department of Radiology, Kaiser Permanente Northern California, Vallejo, CA, United States.
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, United States.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States.
- Department of Radiology, Southern California Permanente Medical Group, Anaheim, CA, United States.
- Department of Surgery, University of California, San Francisco, San Francisco, CA, United States.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.
- Department of Medical Imaging Technology and Informatics, Southern California Permanente Medical Group, Pasadena, CA, United States.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
- Department of Nuclear Medicine, The Permanente Medical Group, Oakland, CA, United States.
- KP Information Technology, Kaiser Foundation Health Plan Inc. and Kaiser Foundation Hospitals, Pasadena, CA, United States.
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States.
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, United States.
- Department of Radiology, Kaiser Permanente Northern California, San Rafael, CA, United States.
- Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Abstract
We compared performance across 3 breast cancer risk domains-clinical, polygenic, and mammography artificial intelligence-alone and in combination over a 10-year time horizon among women with a negative screening mammogram within a Kaiser Permanente Research Bank (KPRB) prospective cohort. The study included 82 957 women (61 962 non-Hispanic White, 7256 Asian, 3414 Black, and 5466 Latina) who enrolled in KPRB between 2003 to 2020. Women with a prior history of breast cancer or high/moderate-penetrant gene mutation were excluded. The negative screening mammogram (no clinically visible cancer) closest to enrollment was used to generate the Mirai mammography AI risk score. KPRB survey and electronic health record data were used to generate the Breast Cancer Surveillance Consortium version 3 (BCSCv3) clinical risk score. Genome-wide genotypes were used to compute the 313-SNP polygenic risk score, adjusted for genetic ancestry (PRS313adj). Risks of breast cancer (invasive or ductal carcinoma in situ) at 0 to 10 years after the mammogram were estimated using Cox models, with 5-fold cross-validation used to estimate the C-index. During 10 years of follow-up, 2471 women developed breast cancer. The C-index (95% CI) for the combined model with all 3 risk scores (0.70; 95% CI = 0.69 to 0.71) was significantly higher than for univariate models with only the BCSCv3 (0.62; 95% CI = 0.61 to 0.63), PRS313adj (0.61; 95% CI = 0.60 to 0.62), or Mirai (0.66; 95% CI = 0.65 to 0.67) risk score. Integrating mammographic AI and polygenic risk scores with clinical risk models significantly improved breast cancer risk discrimination, supporting use of combined models for personalized screening and prevention.