Simple SummaryPersonalized breast cancer screening has the potential to improve the accuracy and effectiveness of breast cancer screening by tailoring the screening protocol to an individual’s risk factors. Currently, most women undergo mammograms at regular intervals based on age and family history, regardless of their risk factors. We examined the applicability of the Gail model, a well-known breast cancer prediction tool including several risk factors, in an Asian population. While the tool predicts the risks of developing breast cancer in the next 2, 5, 10, and 15 years to some extent, the age at which the disease occurs cannot be estimated. In addition, the tool performs better for longer prediction horizons (10 or 15 years), limiting its utility for risk prediction for two-year screening intervals.AbstractPersonalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model’s performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580–0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86–1.71; E/Oshort-term ranges:1.24–3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.