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Updated: Jan 9, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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基于机器学习的生存分析对接受伦瓦替尼治疗不可切除的肝细胞癌的患者

Chien-Hung Lu1, Ching-Wen Chang2,3, San-Chi Chen4,5,6

  • 1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.

Journal of hepatocellular carcinoma
|December 1, 2025
PubMed
概括

机器学习模型准确地预测了用lenvatinib治疗的不可切除肝细胞癌 (HCC) 患者的整体存活率 (OS) 和无进展存活率 (PFS). 这些模型将患者分为风险组,帮助临床决策.

关键词:
考克斯比例危险模型渐变增强机器的渐变增强机器总体存活率 总体存活率没有进展的生存率.随机生存森林 随机生存森林生存模型的生存模型.

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