Citations 1
Performance of clinical breast cancer risk prediction models vs a mammography-based artificial intelligence risk model
Kaul, M., Scott, C., Wadzinske, Alena , Banerjee, Imon, Hursh, Dan, Norman, Aaron, Wu, Fang-Fang, Medero, Ramon Correa, Couch, Fergus, Pruthi, Sandhya , Olson, JA, Kerlikowske, Karla, Vachon, Celine M, Scott, Christopher G. , Couch, Fergus J. , Norman, Aaron D. , Banerjee, Imon , Correa, Ramón , Couch, Fergus J. , Kaul, Medha , Scott, Christopher , Banerjee, Imon , Norman, Aaron D , Correa-Medero, Ramon Luis , Kontos, Despina , McCarthy, Anne Marie , Winham, Stacey J , Kerlikowske, Karla M , Vachon, Celine M. , Pruthi, Sandhya , Hursh, Dan, Winham, Stacey J. , Kerlikowske, Karla
JNCI: Journal of the National Cancer Institute | March 24, 2026 | Volume: 118 | Issue: 8 | Pages: 1485-1495
Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.