文章：

利用可解释人工智能提升主动监测患者前列腺癌进展预测潜力

Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence

原文发布日期：7 November 2025

DOI: 10.3390/cancers17223598

类型: Article

开放获取: 是

英文摘要：

Background: Approximately half of prostate cancer (PCa) patients present with low- or intermediate-risk disease eligible for active surveillance (AS). However, a substantial proportion of individuals experience pathological progression during follow-up. In this study, we developed predictive models for histopathological PCa progression in patients on AS.Methods: The dataset comprised patients with biopsy-confirmed PCa and a minimum follow-up of two years. All patients underwent regular surveillance, including prostate-specific antigen (PSA) measurements and MRI examinations. Each patient had three to six consecutive MRI scans available for analysis. Histopathological progression was defined as an upgrade to a higher grade group on repeat targeted biopsy. Predictive modeling integrated radiomic and clinical variables using machine learning (ML). SHapley Additive exPlanations (SHAP) was used for feature interpretation.Results: Three models were obtained: (1) a baseline model utilizing radiomic features from initial MRI scans combined with baseline PSA density (PSAd) (AUC = 0.793, sensitivity = 0.690, specificity = 0.830); (2) a delta model incorporating feature changes between latest and baseline available MRI scans with final PSAd (AUC = 0.913, sensitivity = 0.793, specificity = 0.936); and (3) a time series model analyzing the complete series of radiomic features and PSAd (AUC = 0.917, sensitivity = 0.828, specificity = 0.894).Conclusions: Our predictive models demonstrated strong performance in distinguishing progressors from non-progressors, suggesting that radiomic analysis combined with ML has significant potential to enhance PCa management. This approach could enable more personalized treatment strategies and improve clinical decision-making for patients undergoing AS.

摘要翻译： 

背景：约半数前列腺癌（PCa）患者确诊时为低危或中危疾病，符合主动监测（AS）条件。然而，在随访期间有相当比例患者出现病理进展。本研究旨在开发AS患者组织病理学进展的预测模型。 方法：数据集纳入经活检确诊且至少随访两年的PCa患者。所有患者均接受规律监测，包括前列腺特异性抗原（PSA）检测和磁共振成像（MRI）检查。每位患者拥有3-6次连续MRI扫描数据用于分析。组织病理学进展定义为重复靶向活检中升级至更高分级组别。预测模型通过机器学习（ML）整合影像组学特征与临床变量，并采用SHapley加性解释（SHAP）进行特征解析。 结果：获得三种预测模型：（1）基线模型：结合首次MRI影像组学特征与基线PSA密度（PSAd）（AUC=0.793，敏感度=0.690，特异度=0.830）；（2）动态模型：纳入最新与基线MRI特征变化值及最终PSAd（AUC=0.913，敏感度=0.793，特异度=0.936）；（3）时序模型：分析完整的影像组学特征序列及PSAd数据（AUC=0.917，敏感度=0.828，特异度=0.894）。 结论：本研究所建预测模型在区分进展与非进展患者方面表现优异，表明影像组学分析与机器学习相结合对优化PCa管理具有重要潜力。该方法有助于制定更个体化的治疗策略，改善AS患者的临床决策。

原文链接：

Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence