可解释的机器学习模型用于预测局部晚期胃癌患者的术后生存率
Zhijie Gong1,2, Liping Zhou3, Yinghao He1,2
1The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Cancer medicine
|November 21, 2025
概括
这项研究开发了一种可解释的机器学习模型,用于预测局部晚期胃癌 (LAGC) 患者的生存率. 随机生存森林模型确定了淋巴结比率,AJCC阶段和年龄作为关键预测因素,有助于个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 胃癌仍然是一个重大的全球健康挑战,特别是局部晚期胃癌 (LAGC).
- 准确预测术后存活率对于有效的治疗计划和LAGC患者管理至关重要.
- 现有的预后模型可能缺乏个性化医学所需的准确性和解释性.
研究的目的:
- 开发和验证一种可解释的机器学习模型,用于预测LAGC患者的术后存活率.
- 优化预测准确度,并确保个性化预后的临床适用性.
- 确定影响LAGC生存的关键预后因素.
主要方法:
- 利用了SEER数据库 (8616名LAGC患者) 和外部验证队列 (235名患者).
- 开发并比较了五种预测模型:CoxPH,RSF,XGBoost,GBM和DeepSurv.
- 使用C指数,AUROC,Brier分数,ROC曲线,校准曲线和DCA评估模型;使用SurvSHAP和SurvLIME进行解释.
主要成果:
- 随机生存森林 (RSF) 模型显示出优异的预测性能 (C指数0.732在验证中,0.723在外部验证中).
- 确定的主要预后因素包括淋巴结比率 (LNR),AJCC阶段和年龄.
- 为个性化预后可视化创建了一个交互式预测工具.
结论:
- 一个基于RSF的可解释模型准确地预测LAGC患者的术后存活率.
- LNR,AJCC阶段和年龄是重要的预后指标.
- 开发的交互工具增强了个性化治疗决策的临床实用性.
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