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深度学习从T2加权的MRI中获得基于前列腺区域体积的生物标志物,以区分前列腺癌和良性前列腺增生症

Zelin Zhang¹, Qingsong Yang², Rakesh Shiradkar¹

¹Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.

Medical physics

|August 14, 2025

概括

机器学习使用MRI准确地区分良性前列腺增生症 (BPH) 和前列腺癌 (PCa). 前列腺区域体积比 (pZVR) 显示出非侵入性诊断的希望,区分BPH-PCa和BPH-Only病例.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
机器学习机器学习

背景情况:

良性前列腺增生 (BPH) 和前列腺癌 (PCa) 在MRI上呈现重叠的特征,使诊断复杂化.
精确区分仅BPH和BPH-PCa至关重要,以避免不必要的活检和过度诊断.
之前的研究表明,在BPH-PCa与BPH-Only患者中,前列腺外周区域 (PZ) 和过渡区域 (TZ) 体积之间存在不同的关联.

研究的目的:

开发和验证在T2加权 (T2W) MRI上PZ和TZ体积的机器学习衍生比率作为成像生物标志物.
使用这种新型生物标志物,区分BPH-PCa和BPH-Only病例.

主要方法:

一项对199名患者的回顾性研究 (106名BPH-Only,93名BPH-PCa) 接受了切除前的3特斯拉多参数MRI.
开发和培训一个3D条件生成对抗网络 (cGAN) 模型 (ProZonaNet),用于在T2WMRI上对前列腺TZ和PZ体积进行细分.
使用单变量和多变量分析计算前列腺区域体积比 (pZVR = TZ/PZ) 并评估其区分能力.

主要成果:

在一个独立的测试组中,ProZonaNet实现了92.5%的平均子相似系数 (DSC),表现优于现有模型.
计算的pZVR显示了与基本真相注释的高度一致 (仅BPH的一致性相关系数[CCC]为0.960,BPH-PCa的0.930).
综合pZVR模型,年龄和PSA,改善了区分BPH-PCa和BPH-Only的曲线下的面积 (AUC),从0.758到0.927.

结论:

使用ProZonaNet计算的前列腺区域体积比率 (pZVR) 在MRI上有效地区分了BPH与PCa.
这项研究表明了BPH-PCa.非侵入性诊断的可行性.
这些发现可能有助于区分PCa和BPH-Only等良性疾病,从而减少不必要的活检.

关键词:

核磁共振 (MRI) 多区域3D细分在CHANGAN中,我们可以看到.前列腺癌是前列腺癌.

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