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对MRI进行深度学习分析,以评估直肠癌治疗和治疗

Heather M Selby¹, Ashley Y Son¹, Vipul R Sheth²

¹Stanford-Surgery Policy Improvement Research and Education Center (S-SPIRE Center), Department of Surgery, Stanford School of Medicine, Palo Alto, CA, United States.

Frontiers in oncology

|February 25, 2026

概括

深度学习模型在MRI上准确地细分了直肠瘤,有助于区分局部晚期直肠癌 (LARC) 的治疗反应. 放射性分析根据临床完整反应 (cCR) 确定了不同的患者组.

科学领域:

在瘤学瘤学.
放射学放射学是指放射学
人工智能的人工智能

背景情况:

局部晚期直肠癌 (LARC) 的新辅助疗法产生了有限的病理完整反应 (pCR) 率.
通过MRI进行临床完整反应 (cCR) 评估是主观的,需要客观的工具.
自动细分和放射性分析可以提高治疗反应评估.

研究的目的:

开发深度学习模型,用于在治疗前后的MRI上自动化直肠瘤细分.
确定放射性特征,以区分cCR和非cCR患者.
评估半监督学习的业绩,以对其进行细分.

主要方法:

从37名LARC患者的MRI进行了回顾性分析.
深度学习模型 (基线和半监督) 训练用于瘤细分.
从处理后的ADC地图中提取放射性特征,然后进行过和聚类.

主要成果:

半监督模型 (模型2) 显著改善了治疗前细分 (DSC 0.769),表现优于评价者之间的协议.
治疗后的细分显示了较低的一致性 (DSC 0.362),这是由于治疗引起的变化.
放射性聚类确定了两个不同的患者群体,与cCR和非cCR状态相关联.

结论:

深度学习可以实现可行的自动细分和放射性分析,用于直肠癌治疗反应.
半监督学习有效地解决了细分中的有限注释挑战.
放射性特征对差异化治疗反应有希望,需要进一步的多中心验证.

关键词:

这就是为什么MRI是MRI.完整的临床反应.深度学习是一种深度学习.在 nnU-Net 网络上.这是直肠癌.细分化细分化的细分化

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