# A combined risk model shows viability for personalized breast cancer risk assessment in the Indonesian population: A case/control study

## TL;DR

This study shows that combining clinical and genetic risk factors improves breast cancer risk prediction in Indonesia.

## Contribution

The first evaluation of combined clinical and polygenic risk models for breast cancer in the Indonesian population.

## Key findings

- Clinical and polygenic risk models retained predictive accuracy in Indonesia with AUCs of 0.67.
- A combined risk model improved predictive accuracy with an AUC of 0.70.
- Combined models outperform single-factor approaches in this demographic.

## Abstract

Breast cancer remains a significant concern worldwide, with a rising incidence in Indonesia. This study aims to evaluate the applicability of risk-based screening approaches in the Indonesian demographic through a case-control study involving 305 women. We developed a personalized breast cancer risk assessment workflow that integrates multiple risk factors, including clinical (Gail) and polygenic (Mavaddat) risk predictions, into a consolidated risk category. By evaluating the area under the receiver operating characteristic curve (AUC) of each single-factor risk model, we demonstrated that they retained their predictive accuracy in the Indonesian context (AUC for clinical risk: 0.67 [0.61,0.74]; AUC for genetic risk: 0.67 [0.61,0.73]). Notably, our combined risk approach enhanced the AUC to 0.70 [0.64,0.76], highlighting the advantages of a multifaceted model. Our findings demonstrate for the first time the applicability of the Mavaddat and Gail models to Indonesian populations, and show that within this demographic, combined risk models provide a superior predictive framework compared to single-factor approaches.