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基于机器学习算法的随机生存森林模型的构建,以预测成人肝细胞癌肝切除术后的早期复发

Ji Zhang¹, Qing Chen¹, Yu Zhang¹

¹Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

|December 25, 2024

概括

随机生存森林 (RSF) 模型准确地预测了手术后早期肝细胞癌 (HCC) 复发,超过了传统的考克斯比例危险 (CPH) 模型. 这种机器学习方法有助于将患者分为不同风险群体,以提供量身定制的后续护理.

科学领域:

肝细胞癌 (HCC) 研究研究
机器学习在瘤学中的应用
预测模型在医学中的预测模型.

背景情况:

肝细胞癌 (HCC) 在切除肝脏后经常在早期复发,导致患者的治疗结果不佳.
目前对HCC复发的预测模型经常使用考克斯比例危险 (CPH) 模型,该模型在复杂的临床数据方面存在局限性.

研究的目的:

评估随机生存森林 (RSF) 模型在预测早期HCC复发后手术后的有效性.
将RSF模型的性能与传统的CPH模型和Albumin-Bilirubin (ALBI) 等级模型进行比较.

主要方法:

一项回顾性队列研究包括541名患者,随机分为培训 (n=378) 和验证 (n=163) 组.
最小绝对收缩和选择运营商 (LASSO) 回归确定了风险因素,这些风险因素被用来构建RSF和CPH模型.
使用一致性指数 (C指数),接收器运行特征曲线下的面积 (AUC),决策曲线分析 (DCA) 和Brier分数来评估模型性能.

主要成果:

在培训和验证组中,RSF模型表现出卓越的预测能力,与CPH和ALBI模型相比,C指数和AUC值更高.
决策曲线分析表明RSF模型具有更大的临床实用性.
该RSF模型有效地将患者分为低风险,中风险和高风险组.

结论:

RSF模型是预测手术后早期HCC复发的高效工具.
它的性能优于CPH和ALBI模型,使其在临床决策和患者风险分层方面具有价值.

关键词:

早期的复发早期的复发这是一种肝切除术.肝细胞癌是肝细胞癌.机器学习机器学习

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