Combining Breast Cancer Risk Prediction Models

TL;DR

This paper explores methods to combine two breast cancer risk prediction models, BRCAPRO and BCRAT, to improve accuracy in risk stratification for better targeted screening and prevention.

Contribution

It introduces two novel approaches for integrating BRCAPRO and BCRAT, enhancing predictive performance over individual models.

Findings

Combination models outperform individual models in simulations.

Ensemble approach improves risk prediction accuracy.

Modified penetrance functions enhance model performance.

Abstract

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Numerous breast cancer risk prediction models have been developed, but they often give predictions with conflicting clinical implications. Integrating information from different models may improve the accuracy of risk predictions, which would be valuable for both clinicians and patients. BRCAPRO and BCRAT are two widely used models based on largely complementary sets of risk factors. BRCAPRO is a Bayesian model that uses detailed family history information to estimate the probability of carrying a BRCA1/2 mutation, as well as future risk of breast and ovarian cancer, based on mutation prevalence and penetrance (age-specific probability of developing cancer given genotype). BCRAT uses a relative hazard model based on first-degree family history and non-genetic risk factors. We consider two approaches for combining BRCAPRO and BCRAT: 1) modifying the penetrance functions in BRCAPRO using relative hazard estimates from BCRAT, and 2) training an ensemble model that takes as input BRCAPRO and BCRAT predictions. We show that the combination models achieve performance gains over BRCAPRO and BCRAT in simulations and data from the Cancer Genetics Network.