使用人工智能模型在前列腺癌和前列腺炎患者的MRI图像中检测损伤,并比较模型性能
Muhammed Kaya1, Osman Durdag2, Merve Solak1
1Faculty of Medicine, Recep Tayyip Erdoğan University, Rize, Türkiye.
Frontiers in urology
|February 4, 2026
概括
深度学习模型在使用MRI诊断前列腺癌 (P5) 和前列腺炎方面表现出高准确度,与专家放射科医生相似. 这些人工智能工具为改善前列腺癌检测提供了临床决策支持系统的潜力.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 双参数磁共振成像 (MR) 对前列腺癌和前列腺炎的诊断提出了挑战.
- 目前正在研究深度学习 (DL) 模型,以提高该领域的诊断准确性.
研究的目的:
- 评估DL模型在使用MRI图像提高前列腺癌和前列腺炎的诊断准确度方面的能力.
- 评估DL模型在协助放射科医生诊断前列腺癌方面的性能.
主要方法:
- 对153名被诊断为前列腺癌或前列腺炎的患者进行了回顾性分析.
- 检查T2A和明显扩散系数-扩散-加权成像 (ADC-DWI) 序列.
- 利用基于Faster区域的卷积神经网络 (Faster R-CNN) 进行初始病变检测,并训练了九种分类模型.
主要成果:
- 更快的R-CNN实现了P5前列腺癌 (96%在T2A中,90%在ADC-DWI中) 和前列腺炎 (99%在T2A中,97%在ADC-DWI中) 的高精度.
- 分类模型达到高达97%的准确性 (InceptionV3,T2A) 和99%的准确性 (DenseNet201,ADC-DWI).
- DL模型的性能与专家放射科医生相比,具有很大的一致性 (科恩的卡帕=0.86).
结论:
- 从MRI图像中区分前列腺炎和P5前列腺癌病变方面,DL模型显示出显著的希望.
- 基于人工智能的诊断工具可以作为有效的临床决策支持系统,帮助放射科医生诊断前列腺癌.
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