The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients’ risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.

目前的前列腺癌（PCa）筛查测试前列腺特异性抗原（PSA）对PCa具有较高的敏感性，但对高风险、有临床意义的PCa（csPCa）特异性较低，导致对非csPCa的过度诊断和过度治疗。早期识别 csPCa 同时避免对非 csPCa 男性进行不必要的活检具有挑战性。我们构建了一个优化的机器学习平台 (ClarityDX)，并展示了其在生成预测 csPCa 的模型方面的实用性。将 ClarityDX 平台与具有临床意义的 PCa 血液生物标志物以及来自 3448 名患者队列的临床生物标志物数据相结合，我们开发了一项测试来对患者的 csPCa 风险进行分层；称为 ClarityDX 前列腺。在预测验证队列中的高风险癌症时，ClarityDX Prostate 在 ≥ 25% 阈值下显示出 95% 的敏感性、35% 的特异性、54% 的阳性预测值和 91% 的阴性预测值。在此阈值下使用 ClarityDX Prostate 可以避免高达 35% 的不必要的前列腺活检。 ClarityDX Prostate 在预测 csPCa 风险方面表现出比单独使用 PSA 和经过测试的基于模型的风险计算器更高的准确性。使用此测试作为 PSA 水平升高的男性的反射测试可以帮助患者及其医疗保健提供者决定是否需要进行前列腺活检。