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机器学习模型的开发和部署,用于对怀疑患有前列腺癌的患者进行前列腺MRI (ProMT-ML) 的 triage

Jesse Persily¹, Hersh Chandarana^1,2, Angela Tong²

¹Department of Urology, NYU Grossman School of Medicine, New York, New York, USA.

Journal of magnetic resonance imaging : JMRI

|November 4, 2025

概括

机器学习模型 (ProMT-ML) 现在可以个性化前列腺MRI的风险估计,改善前列腺癌评估的分组. 这些模型的性能明显优于PSA测试.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

获得前列腺MRI的机会受到资源限制和需要专家解释的限制.
前列腺癌的评估在很大程度上依赖于MRI,需要有效的分类方法.

研究的目的:

开发机器学习 (ML) 模型,称为ProMT-ML,用于前列腺MRI中的基于风险的分类.
通过人工智能驱动的风险分层来提高前列腺癌的评估.

主要方法:

开发了两种监督的ML模型 (模型A具有前列腺体积,模型B没有) 使用11,879个回顾性MRI扫描.
使用Boruta算法进行特征选择,并使用AUC与PSA比较模型性能.
在4,551个记录上前性验证模型.

主要成果:

这两种ProMT-ML模型在预测异常MRI (PI-RADS ≥3) 中显著优于PSA.
模型A的AUC为0.711,模型B为0.616,而PSA的AUC为0.593.
模型A显示了比PSA更好的特异性,同时保持了高灵敏度,大多数假阴性结果具有良性活检结果.

结论:

ProMT-ML模型提供了针对前列腺MRI异常的个性化风险估计,有助于测试分类.
这些人工智能工具可以优化资源配置,并支持前列腺癌诊断中的临床决策.

关键词:

机器学习是机器学习.预测模型预测模型前列腺核磁共振 (MRI) 的前列腺.前列腺癌是前列腺癌.风险分层的风险分层.选分类是为了分类.

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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...