A combined risk model shows viability for personalized breast cancer risk assessment in the Indonesian population: A case/control study - Bijak Rabbani; Sabrina Gabriel Tanu; Kevin Nathanael Ramanto; Jessica Audrienna; Eric Aria Fernandez; Fatma Aldila; Mar Gonzalez-Porta; Margareta Deidre Valeska; Jessline Haruman; Lorina Handayani Ulag; Yusuf Maulana; Kathleen Irena Junusmin; et al. (PLoS ONE, 2025)
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.