A combined risk model shows viability for personalized breast cancer risk assessment in the Indonesian population
Rabbani, B.; Tanu, S. G.; Ramanto, K. N.; Audrienna, J.; Sodiqi, F. A.; Fernandez, E. A.; Gonzalez-Porta, M.; Valeska, M. D.; Haruman, J.; Ulag, L. H.; Maulana, Y.; Junusmin, K. I.; Amelia, M.; Gabriella, G.; Soetyono, F.; Fajarrahman, A.; Maudani, S. S.; Agatha, F. A.; Wijaya, M.; Br Sormin, S. T.; Sani, L.; Ali, S.; Winata, A.; Salim, A.; Irwanto, A.; Haryono, S. J.
Show 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 demonstrate that they retain 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.
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