Comparative validation of breast cancer risk prediction models and projections for future risk stratification

Parichoy Pal Choudhury, Amber N Wilcox, Mark N Brook, Yan Zhang, Thomas Ahearn, Nick Orr, Penny Coulson, Minouk J Schoemaker, Michael E Jones, Mitchell H Gail, Anthony J Swerdlow, Nilanjan Chatterjee, Montserrat Garcia-Closas

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalJournal of the National Cancer Institute
Early online date04 Jun 2019
DOIs
Publication statusEarly online date - 04 Jun 2019

Bibliographical note

Published by Oxford University Press 2019.

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