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基于加达酸增强的MRI的深度多实例学习模型,用于预测肝细胞癌的微血管入侵:一个多中心的回顾性研究

Yi Luo¹, Ge Zhang², Shuyuan Zhong¹

¹Department of Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology), 518020, Shenzhen, Guangdong, China.

|October 22, 2025

概括

深度学习模型现在可以在肝细胞癌 (HCC) 中非侵入性地预测微血管入侵 (MVI). 使用MRI数据的多模式深度学习方法显示出强大的预测能力,有助于为个性化HCC治疗做出临床决策.

科学领域:

肝胆道成像检查肝胆道成像检查
放射学中的深度学习.
瘤学成像分析分析

背景情况:

微血管入侵 (MVI) 对肝细胞癌 (HCC) 治疗决策至关重要.
在HCC中进行MVI的非侵入性术前预测仍然是一个临床挑战.

研究的目的:

评估深度学习 (DL) 模型用于HCC的非侵入性MVI预测.
为了比较2D,3D和2.5DDL模型的性能,使用不同的感兴趣区域 (ROI) 和输入维度.
开发和验证用于MVI预测的多式联运DL模型.

主要方法:

对来自三个机构的206名HCC患者进行了回顾性分析.
开发2D,3DDL和2.5D深度多实例学习 (MIL) 模型,使用肝胆相 (HBP) MRI.
构建一个多式预测模型,将T1WI-FS和T2WI-FS图像与最佳DL模型结合起来.
使用接收器操作特征 (ROC) 曲线和决策曲线评估模型性能.

主要成果:

使用所有轴向ROI图像的2.5DDL模型在单序模型中获得了最高的性能 (AUC0.802验证,0.759测试).
多模式MRI模型显示出强大的预测效果,AUC为0.954 (训练),0.857 (验证) 和0.788 (测试).

结论:

深度学习模型,特别是多模式方法,显示出在HCC中进行非侵入性MVI预测的巨大潜力.
开发的DL模型可以帮助临床决策制定个性化HCC治疗策略.

关键词:

深度学习是一种深度学习.加多克西酸是指加多克西酸.肝细胞癌是肝细胞癌.磁共振成像技术磁共振成像技术微血管侵袭是一种微血管侵袭.

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