Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score
Presenter: Christopher Scott, MS Session: Radiomics and AI in Medical Imaging Time: 4/20/2026 2:00:00 PM → 4/20/2026 5:00:00 PM
Authors
Christopher G. Scott 1 , Peter Kraft 2 , Imon Banerjee 3 , Ramon Correa Medero 3 , Aaron D. Norman 1 , Fergus J. Couch 1 , Karla Kerlikowske 4 , Stacey J. Winham 1 , Celine M. Vachon 1 1 Mayo Clinic, Rochester, MN, 2 National Cancer Institute, Rockville, MD, 3 Mayo Clinic, Phoenix, AZ, 4 UCSF, San Francisco, CA
Abstract
Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles ( MIRAI and MIRAI+PRS 5-year risk model performance within the Mayo Clinic Biobank mammography cohort Model HR per SD (95% CI) C-index (95% CI) Obs/Exp Ratio (95% CI) Overall BC (Invasive + DCIS), N=250 MIRAI 5 yr Risk (log) 1.69 (1.55, 1.84) 0.71 (0.68, 0.74) 0.96 (0.84, 1.09) MIRAI+PRS 5 yr Risk (log) 1.97 (1.78, 2.19) 0.72 (0.69, 0.75) 0.93 (0.82, 1.05) Invasive BC, N=176 MIRAI 5 yr Risk (log) 1.68 (1.51, 1.86) 0.71 (0.67, 0.75) 0.68 (0.58, 0.78) MIRAI+PRS 5 yr Risk (log) 1.99 (1.76, 2.24) 0.73 (0.69, 0.76) 0.66 (0.56, 0.76)
Disclosure
C. G. Scott, None.. P. Kraft, None.. I. Banerjee, None.. R. Correa Medero, None.. F. J. Couch, None.. K. Kerlikowske, None.. S. J. Winham, None.. C. M. Vachon, None.
Cited in
Control: 4298 · Presentation Id: 2606 · Meeting 21436