Evaluating mammographic density's contribution to improve a breast cancer risk model with questionnaire-based and polygenic factors.

Abstract

Incorporation of mammographic density into breast cancer risk models may improve risk stratification for tailored screening and prevention. We evaluated the added value of Breast Imaging Reporting and Data System (BI-RADS) breast density to a validated model combining questionnaire-based risk factors and a 313-variant polygenic risk score (PRS), using the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation. Calibration and discrimination were assessed in three prospective cohorts of European-ancestry women (1468 cases, 19,104 controls): US-based Nurses' Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were stratified by age (<50, ≥50 years). Adding density modestly improved discrimination: among younger women, AUC increased from 65.6% (95% CI: 61.9-69.3%) to 67.0% (95% CI: 63.5-70.6%); among older women, from 65.5% (95% CI: 63.8-67.2%) to 66.1% (95% CI 64.4-67.8%). Among US women aged 50-70 years, 18.4% were identified at ≥3% 5-year risk with density included, capturing 42.4% of future cases; 7.9% were reclassified, identifying 2.8% more future cases. In Sweden, 10.3% were identified at elevated risk, capturing 29.4% of cases, with 5.3% reclassified and 4.4% more cases identified. Integrating density with established risk factors and PRS may enhance breast cancer risk stratification among European-ancestry women, supporting its potential for clinical utility.