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提高使用可解释的人工智能的积极监测患者预测前列腺癌进展的潜力

Olga Vershinina^1,2, Nikita Sushentsev³, Alexey Zaikin^4,5

¹Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.

|November 27, 2025

概括

机器学习模型准确地预测了在积极监测 (AS) 的患者中前列腺癌 (PCa) 进展. 对MRI扫描和PSA密度的放射性分析提高了预测能力,有助于为PCa制定个性化治疗策略.

科学领域:

在瘤学瘤学.
医疗成像医学成像
机器学习机器学习

背景情况:

一半的前列腺癌 (PCa) 患者患有适合主动监测 (AS) 的低风险或中等风险疾病.
有相当数量的AS患者经历了病理进展.
预测这种进展对于有效的PCa管理至关重要.

研究的目的:

在接受AS的患者中,开发和验证对异位病理性PCa进展的预测模型.
整合来自MRI和临床数据的放射性特征,以提高预测能力.
改善AS患者的临床决策.

主要方法:

使用了活检确认的PCa患者的数据集,至少有两年的随访和多次MRI扫描.
定义了组织病理进展作为重复活检上的等级组升级.
采用机器学习 (ML) 模型,集成放射性和PSA密度 (PSAd) 变量.
应用了SHapley添加式解释 (SHAP) 来解释特征.

主要成果:

一个基线模型 (放射性+PSAd) 达到0.793.3的AUC.
一个三角形模型 (特征变化+PSAd) 改善了预测,AUC为0.913.
一个时间序列模型 (所有扫描 + PSAd) 显示出最高的性能,AUC为0.917.
所有模型都表现出强大的能力来区分进步者和非进步者.

结论:

放射性分析与ML结合,为预测AS患者PCa进展提供了强大的工具.
这些模型可以显著提高PCa管理和个性化治疗策略.
这些发现支持将高级成像和ML纳入AS的常规临床实践.

关键词:

由MRI衍生的放射学.积极监督是指积极的监督.可解释的人工智能机器学习是机器学习.进展的预测和预测.前列腺癌是前列腺癌.

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