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基于机器学习的个性化预测,在射频废除后肝细胞癌复发

Masaya Sato^1,2, Ryosuke Tateishi², Makoto Moriyama²

¹Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Gastro hep advances

|August 12, 2024

概括

一个新的机器学习模型准确地预测了射频废除 (RFA) 后肝细胞癌 (HCC) 复发. 这个工具有助于个性化对接受RFA治疗HCC的患者的随访策略.

科学领域:

肝细胞癌研究肝细胞癌研究
机器学习在瘤学中
医疗数据分析医学数据分析

背景情况:

射频除 (RFA) 是肝细胞癌 (HCC) 的标准治疗方法.
在RFA后HCC复发是常见的,需要有效的风险预测.
个性化患者风险评估对于治疗后的管理至关重要.

研究的目的:

开发和验证用于预测HCC复发风险的机器学习 (ML) 模型.
为了确定HCC在RFA后复发的关键预测因素.
根据预测的复发风险,实现个性化后续策略.

主要方法:

利用了从1778名接受RFA治疗的先前未接受治疗的HCC患者的数据.
开发并比较了6个ML模型,包括DeepSurv和Cox比例危险模型.
通过哈雷尔的c指数和通过分割样本方法进行外部验证来评估模型性能.

主要成果:

渐变增强决策树 (GBDT) 模型在外部验证中显示出优异的预测性能 (c指数为0.67).
GBDT有效地将患者分为不同的风险组 (P < .001).
确定的主要预测因素是瘤数量,血清白蛋白和des-gamma-carboxyprothrombin水平.

结论:

开发了一种新的ML模型,GBDT,用于RFA后个性化HCC复发风险预测.
该模型促进了风险分层,以指导个性化后续护理.
这种方法支持为用RFA治疗的HCC患者量身定制的管理策略.

关键词:

在DeepSurv上进行深度调查.肝细胞癌肝细胞癌机器学习机器学习无线电频率废弃法废弃法

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