SDoH-Aware Approach to Prostate Cancer Screening: Addressing Overdiagnosis of Prostate Cancer using PSA

Ashley Lewis, Yash Samir Khandwala, Tina Hernandez-Boussard, James Brooks
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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.
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具有 SDoH 意识的前列腺癌筛查方法:使用 PSA 解决前列腺癌过度诊断问题
本研究利用 "我们所有人"(AoU)研究计划数据集,探讨了多模态数据在前列腺癌(PCa)风险预测方面的潜力。通过将多基因风险评分(PRS)与各种临床、调查和基因组数据相结合,我们开发了一个模型,该模型可识别年龄和家族史等既有的 PCa 风险因素以及一个新因素:最近的医疗保健就诊与风险降低有关。尽管缺乏前列腺特异性抗原(PSA)数据,但与传统方法相比,该模型的性能(尤其是假阳性率)有所提高。研究结果表明,结合 AoU 的综合多模态数据可以提高 PCa 风险预测能力,并为未来的临床应用提供一个稳健的框架。
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