Investigating the added value of incorporating mammographic density to an integrated breast cancer risk model with questionnaire-based risk factors and polygenic risk score
Authors: Mulder Charlotta V. , Yang Xin , Jee Yon Ho , Scott Christopher G. , Gao Chi , Cao Yu , Hurson Amber N. , Eriksson Mikael , Vachon Celine M. , Hall Per , Antoniou Antonis C. , Kraft Peter , Gierach Gretchen L. , Garcia-Closas Montserrat , Choudhury Parichoy Pal 
Published: December 2024

Abstract

Introduction
Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models.
Methods
We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 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 done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50–70 years in US and Sweden.
Results
For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5–70.6%) vs. 65.6% (95% CI: 61.9–69.3%) for models with and without density; and 66.1% (95% CI 64.4–67.8%) vs. 65.5% (95% CI: 63.8–67.2%) among older women. The model with density identified 18.4% of US women 50–70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases.
Conclusion
Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.