Multicentre Evaluation of an AI‐Assisted Urine Test for Clinically Significant Prostate Cancer in Men Undergoing Initial Biopsy

医学 前列腺癌 过度诊断 直肠检查 概化理论 活检 前列腺活检 PCA3系列 泌尿科 队列 尿 金标准(测试) 放射科 内科学 接收机工作特性 前列腺 曲线下面积 一致性 前列腺特异性抗原 肿瘤科 曲线下面积 诊断试验 尿检 试验预测值 尿潴留 液体活检 诊断准确性 回顾性队列研究 泌尿系统 癌症 胃肠病学 预测值 病理 病态的 训练集
作者
Shaoqin Jiang,Chao Yang,Zhangcheng Huang,Zebang Guo,Feiting Lu,xinwen nian,Z. K. Chen,P. W. Luo,Jiawei Jiang,Xu Gao,Miaoxiu Li,Fei Liu
出处
期刊:Journal of extracellular vesicles [Taylor & Francis]
卷期号:15 (4): e70233-e70233
标识
DOI:10.1002/jev2.70233
摘要

The Extracellular Vesicles Gene-based Prostate Score (EGPS), powered by DeepSeek, is an artificial intelligence (AI) diagnostic tool that enhances the detection of clinically significant prostate cancer (csPCa) using urinary EV-derived gene expression, without requiring digital rectal examination (DRE). To address overdiagnosis resulting from the limited specificity of prostate-specific antigen (PSA) and reduce unnecessary biopsies, this study evaluated the clinical utility and generalizability of EGPS in men undergoing initial biopsy with PSA levels ranging from 0 to 15 ng/mL. A total of 645 patients were retrospectively enrolled: 586 from three centres were divided into training (70%) and internal validation (30%) cohorts, and 59 from two centres served as the external validation cohort. EVs were isolated using the EXODUS platform, and gene expression was measured by RT-qPCR. Ten machine learning algorithms were evaluated for constructing the EGPS model with selected genes. Diagnostic efficacy was assessed by ROC analysis, DeLong tests, and decision curve analysis. An AI diagnostic system using DeepSeek was also developed. The EGPS model, incorporating AMACR, HOXB13, and PSGR, achieved AUCs of 0.838, 0.825, and 0.811 in the training, internal validation, and external validation cohorts, respectively, outperforming PSA. At a cut-off value of 0.22, the model demonstrated sensitivity above 95%, with a missed diagnosis rate of 3.81% in the training cohort and 0% in the validation cohorts. The model reduced unnecessary biopsies by 79 (23.37%), 27 (18.62%) and 9 (15.25%) cases across the three cohorts, thereby lowering biopsy-related risks. A DeepSeek-powered AI diagnostic system integrating EGPS was developed to support csPCa diagnosis and minimize unnecessary biopsies. EGPS, derived from multicentre Chinese cohorts, enables accurate, DRE-free, non-invasive prediction of csPCa in men with PSA levels of 0-15 ng/mL. When integrated into an AI system, EGPS supports early screening and personalized clinical decision-making by reducing unnecessary biopsies.
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