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

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

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...

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Main Authors: 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, Margareta Amelia, Gabriella Gabriella, Feilicia Soetyono, Aulian Fajarrahman, Salma Syahfani Maudina Hasan, Faustina Audrey Agatha, Marco Wijaya, Stevany Tiurma Br Sormin, Levana Sani, Astrid Irwanto, Samuel J Haryono, Soegianto Ali
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0321545
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Summary: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.
ISSN:1932-6203

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