开发一种定量多参数超声波和深度学习分类器,用于检测前列腺癌
Florian Delberghe1, Xueting Li2, Daniel L van den Kroonenberg3,4
1Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands. f.t.delberghe@tue.nl.
European radiology
|January 29, 2026
概括
一个新的深度学习分类器使用定量3D多参数超声波 (mpUS) 功能准确检测临床显著的前列腺癌 (csPCa). 这一进步为cspca诊断提供了一个具有成本效益和可访问的工具.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 前列腺癌 (PCa) 诊断越来越依赖于成像方式.
- 超声波 (美国) 是一种经济高效且易于获得的成像选项.
- 需要提高PCa检测的诊断准确性.
研究的目的:
- 开发和验证用于预测临床显著前列腺癌 (csPCa) 的深度学习分类器.
- 为了利用3D多参数超声波 (mpUS) 的定量特征来检测csPCa.
- 在外部数据集上评估分类器的概括性.
主要方法:
- 327名疑似PCa患者的多中心前队列接受了3D mpUS.
- 提取了定量mpUS特征,并用于训练3D深度学习分类器.
- 该分类器接受了培训,并对250名患者进行内部评估,并对77名患者进行外部评估,以3D组织学为参考标准.
主要成果:
- 深度学习分类器在内部评估集上实现了0.87的接收器运行特征曲线 (ROC AUC) 下的区域.
- 外部验证显示ROC AUC为0.88,表明强烈的概括性.
- 分类器使用定量mpUS特征准确检测了csPCa.
结论:
- 拟议的深度学习分类器使用定量3D mpUS特征准确检测csPCa.
- 分类器证明了对外部数据集的良好概括性.
- 3D mpUS显示承诺作为一个准确的,具有成本效益的工具,用于csPCa诊断.
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