Validating Breast Cancer Risk Prediction Models in the Korean Cancer Prevention Study-II Biobank

Abstract Background: Risk prediction models may be useful for precision breast cancer screening. We aimed to evaluate the performance of breast cancer risk models developed in European-ancestry studies in a Korean population. Methods: We compared discrimination and calibration of three multivariable risk models in a cohort of 77,457 women from the Korean Cancer Prevention Study (KCPS)-II. The first incorporated U.S. breast cancer incidence and mortality rates, U.S. risk factor distributions, and RR estimates from European-ancestry studies. The second recalibrated the first by using Korean incidence and mortality rates and Korean risk factor distributions, while retaining the European-ancestry RR estimates. Finally, we derived a Korea-specific model incorporating the RR estimates from KCPS. Results: The U.S. European-ancestry breast cancer risk model was well calibrated among Korean women <50 years [expected/observed = 1.124 (0.989, 1.278)] but markedly overestimated the risk for those ≥50 years [E/O = 2.472 (2.005, 3.049)]. Recalibrating absolute risk estimates using Korean breast cancer rates and risk distributions markedly improved the calibration in women ≥50 [E/O = 1.018 (0.825, 1.255)]. The model incorporating Korean-based RRs had similar but not clearly improved performance relative to the recalibrated model. Conclusions: The poor performance of the U.S. European-ancestry breast cancer risk model among older Korean women highlights the importance of tailoring absolute risk models to specific populations. Recalibrating the model using Korean incidence and mortality rates and risk factor distributions greatly improved performance. Impact: The data will provide valuable information to plan and evaluate actions against breast cancer focused on primary prevention and early detection in Korean women.