通过基于MRI的可解释机器学习,在肝脏内胆管癌中进行围神经侵入的术前预测
Xiaoqi Zhou1, Meicheng Chen1, Danyang Xu1
1Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, PR China.
概括
使用MRI的深度学习 (DL) 模型准确地预测肝脏内胆管癌 (ICC) 中的围神经侵入 (PNI). 来自DL模型的可视化改善了潜在应用的临床解释性.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 肝脏内胆管癌 (ICC) 在手术前诊断方面存在挑战.
- 周围神经侵入 (PNI) 是ICC的一个关键预后因素.
- 准确的手术前预测PNI对于治疗规划至关重要.
研究的目的:
- 开发和评估放射学和深度学习 (DL) 模型,用于使用MRI在ICC中预测PNI.
- 为了比较单相和多相MRI模型的性能.
- 通过可视化技术提高DL模型的可解释性.
主要方法:
- 从两个中心对165名ICC患者进行了术前MRI的回顾性分析.
- 使用单相和多相MRI数据构建放射学和DL模型.
- 使用接收器操作特征曲线下的面积 (AUC) 和梯度加权类激活映射 (Grad-CAM) 评估模型性能.
主要成果:
- 深度学习 (DL) 模型与放射学模型相比,表现优越,特别是在外部测试组 (AUC高达0.844) 中.
- 多相DL模型显示高AUC值 (0.944内部,0.844外部).
- 肝胆阶段 (HBP) DL模型也表现出强的表现 (AUC 0.926内部,0.856外部).
结论:
- 基于MRI的深度学习 (DL) 模型对于ICC的手术前PNI预测是有效的.
- 通过像Grad-CAM这样的技术来增强模型的解释性,突出显示瘤边缘区域.
- 这些发现表明,对于改善ICC管理的潜在临床应用.
关键词:
深度学习是一种深度学习.在肝脏内发生的胆管癌 (cholangiocarcinoma).多相核磁共振成像 (MRI) 是一种多相核磁共振成像.周围神经的入侵.无线电学 (Radiomics) 是一种辐射学.更多相关视频
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