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基于人工智能的检测MRI隐形的前列腺癌的nnU-Net

Jingcheng Lyu^1,2, Ruiyu Yue^1,2, Boyu Yang^1,2

¹Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.

The Canadian journal of urology

|November 12, 2025

概括

使用nn-Net的AI系统显示出在MRI扫描上看不见的前列腺癌检测的前景,有助于早期诊断未确定的发现 (PI-RADS得分≤3) 的患者. 这项技术可以改善前列腺癌的管理.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

当磁共振成像 (MRI) 发现不确切时,诊断前列腺癌可能是具有挑战性的 (前列腺成像报告和数据系统[PI-RADS]得分≤3).
临床上显著的前列腺癌可能会错过标准的MRI,需要先进的诊断工具.

研究的目的:

开发基于人工智能 (AI) 的图像识别系统,用于检测MRI隐形前列腺癌.
利用nnU-Net自适应神经网络,在具有挑战性的前列腺癌病例中提高诊断准确性.

主要方法:

对150名病理确诊的前列腺癌患者的回顾性分析,PI-RADS ≤3在MRI上.
用了1475个多参数MRI图像 (T2WI,DWI,ADC) 来进行AI模型训练.
采用nn-Net用于初始瘤细分和卷积神经网络用于图像识别,通过五倍交叉验证进行验证.

主要成果:

人工智能系统在检测MRI隐形前列腺癌方面实现了55.0%的平均子相似系数.
显示平均灵敏度为50.5%,高平均特异性为96.9%.
报告的平均虚假阳性和虚假阴性率分别为3.1%和49.5%.

结论:

一个基于人工智能的图像识别系统使用nn-Net成功开发.
该系统显示了提高前列腺癌的早期检测和管理的潜力,特别是在没有明确的MRI发现的情况下.
这种人工智能工具可以显著帮助临床医生诊断困难的前列腺癌病例.

关键词:

人工智能的人工智能是人工智能.磁共振成像技术的使用在 nnU-Net 网络上.前列腺癌是前列腺癌.

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