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Ran Wei¹, Zelong Liu², Lingjie Ju³

¹Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Radiology. Imaging cancer

|September 5, 2025

概括

一个新的机器学习 (ML) 模型,CatBoost,准确地预测了肝细胞癌 (HCC) 经过过动脉化学栓塞 (TACE) 后的晚期进展 (ASP). 这种工具有助于识别可能受益于进一步治疗的高风险患者.

科学领域:

癌症学
医学成像
人工智能

背景情况:

肝细胞癌 (HCC) 是一种主要的肝癌.
转动脉化学栓塞 (TACE) 是一种常见的中期HCC治疗方法.
在TACE后预测晚期进展 (ASP) 对于患者的治疗至关重要.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测HCC患者TACE后的ASP.
将ML模型的性能与已建立的分期系统进行比较.
评估ML模型在TACE后治疗决策中的有用性.

主要方法:

一项多中心的回顾性研究包括2333名接受TACE的中期HCC患者.
用34个手术前临床和CT成像变量来训练6个ML模型.
使用AUC和一致性指数与现有的分阶段系统进行了比较.

主要成果:

在预测ASP方面,CatBoost模型获得了高AUC值 (0. 97训练,0. 94内部测试,0. 93外部测试).
与五个主要分阶段系统相比,CatBoost表现出更好的分辨能力 (P < .001).
该模型确定了高风险患者,他们从TACE后的全身治疗中显著受益 (P < .001).

结论:

与目前的分期系统相比,CatBoost ML模型在中期HCC中提供了TACE后的ASP预测.
这种模式有效地将患者分为风险群体,帮助制定个性化治疗策略.
这些发现支持使用ML来优化TACE后的HCC管理.

关键词:

高阶段的进展肝细胞癌肝脏机器学习癌症学风险分化通过动脉化学栓塞

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