BACKGROUND: External validation of risk models is critical for risk stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development, comparative model validation, and to make projections for population risk stratification.
METHODS: Performance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 White non-Hispanic women (863 cases) aged 35-74 years. Risk projections in a target population of US White non-Hispanic women aged 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).
RESULTS: The best calibrated models were iCARE-Lit (expected to observed number of cases (E/O)=0.98 (95% confidence interval [CI]=0.87 to 1.11)) for women younger than 50 years; and iCARE-BPC3 (E/O=1.00 (0.93 to 1.09)) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify ∼500,000 women at moderate to high risk (>3% five-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this to approximately 3.5 million, and among them, approximately 153,000 invasive breast cancer cases are expected within five years.
CONCLUSIONS: iCARE models based on classical risk factors perform similarly or better than BCRAT or IBIS in White non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.