Ashley Lewis, Yash Samir Khandwala, Tina Hernandez-Boussard, James Brooks
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引用次数: 0
Abstract
This study investigates the potential of multimodal data for prostate cancer (PCa) risk prediction using the All of Us (AoU) research program dataset. By integrating polygenic risk scores (PRSs) with diverse clinical, survey, and genomic data, we developed a model that identifies established PCa risk factors, such as age and family history, and a novel factor: recent healthcare visits are linked to reduced risk. The model's performance, notably the false positive rate, is improved compared to traditional methods, despite the lack of Prostate-Specific Antigen (PSA) data. The findings demonstrate that incorporating comprehensive multimodal data from AoU can enhance PCa risk prediction and provide a robust framework for future clinical applications.