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基于机器学习的放射学分析,用MRI和CT图像分析术前功能性肝脏储备

Ling Zhu¹, Feifei Wang¹, Xue Chen^1,2

¹Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.

BMC medical imaging

|July 17, 2023

概括

使用MRI和CT成像的机器学习模型可以准确地评估肝细胞癌患者的功能性肝储备. 这些非侵入性放射学模型显示出在手术前评估肝功能的前景.

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:

在肝癌手术前评估功能性肝储备时,印ocyanine绿色保留率在15分钟 (ICG-R15) 是至关重要的.
肝细胞癌 (HCC) 需要精确评估肝功能治疗规划.

研究的目的:

研究基于机器学习 (ML) 的放射学模型的疗效,使用Gd-EOB-DTPA增强的MRI和对比增强的CT来评估HCC患者的功能性肝储备.
为了将放射学特征与ICG-R15值相关联,以预测肝功能.

主要方法:

追溯分析190名HCC患者的CT和/或MRI数据.
从MRI和CT图像中提取放射性特征.
使用ICG-R15分类值 (10%,20%,30%) 的ML模型 (XGBoost,随机森林,SVM) 的开发和评估.
使用准确度 (ACC) 和接收器操作特征 (ROC) 曲线下的面积 (AUC) 的性能评估.

主要成果:

基于MRI的模型表现出高性能,XGBoost获得ICG-R15的AUC=0.917和ACC=0.882的10%,而随机森林获得ICG-R15的AUC=0.979和ACC=0.882的20%.
基于CT的模型也证明了有效性,XGBoost在ICG-R15中达到AUC=0.938和ACC=0.965的30%.
在MRI和CT分析中,XGBoost和Random Forest是各种ICG-R15值的最佳分类器.

结论:

基于MRI和CT的机器学习放射学模型都是评估HCC患者肝功能储备的宝贵的非侵入性工具.
这些模型可以帮助进行手术前评估,并有可能改善手术决策和患者的治疗结果.

关键词:

增强对比度的CTCT可以提高对比度.功能性肝脏储备功能性肝脏储备用Gd-EOB-DTPA增强的肝脏MRI进行.机器学习是机器学习.无线电学 (Radiomics) 是一种无线电学.

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