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血管表型的非侵入性成像分析改善了原发性肝癌的预后分层:一项多队列研究

Hongjie Xin¹, Ying Wang², Hongwei Xin³

¹Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China.

NPJ precision oncology

|December 31, 2025

概括

一个新的AI工具MTV-Net分析CT扫描,通过血管模式对原发性肝癌 (PLC) 进行分类,并预测复发风险,改善患者分层和结果.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

瘤血管微环境 (TVME) 在原发性肝癌 (PLC) 中至关重要,但缺乏评估其异质性的非侵入性方法.
了解TVME对于肝细胞癌 (HCC),肝内胆管癌 (ICC) 和组合类型的准确诊断和预后至关重要.

研究的目的:

开发一个计算框架 (MTV-Net) 来从CT图像中分析TVME.
创建成像生物标志物来分类PLC亚型并预测切割后复发风险.

主要方法:

从六个临床队列的CT图像中提取了定量血管特征.
一个多任务学习框架 (MTV-Net) 产生了两个生物标志物:TAVS (表型分类) 和TAVS (复发风险).
放射基因组学分析探索了生物标志物的生物基础.

主要成果:

在TAVS_PHE分类的PLC中",类似ICC"的表型显示出比"类似HCC"的表型更糟糕的生存率.
在所有PLC亚型中,TAVS_RE有效地分层化了复发风险,当被结合为MTV-Net-Clinic时,其表现优于现有的分期系统.
对于MTV-Net-Clinic的一致性指数在0.731到0.823.3之间.

结论:

根据TVME,MTV-Net提供了一个非侵入性的工具,可以从常规CT扫描中重新分类PLC.
该框架增强了预后分层,为肝癌患者提供了改善的临床决策.
细胞外矩阵重塑路径的丰富是开发生物标志物的基础.

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