基于阳性淋巴结的日志概率的机器学习模型用于预测T1N+胃癌中的生存率
Yuchen Liu1,2, Hao Cui1,2, Zhen Yuan1,2
1School of Medicine, Nankai University, Tianjin, China.
Frontiers in oncology
|January 26, 2026
概括
阳性淋巴结的日志几率 (LODDS) 改善了早期胃癌 (EGC) 淋巴结转移的生存预测. 机器学习模型,特别是随机生存森林,与LODDS集成,为T1N+胃癌患者提供了更高的预后准确性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 早期胃癌 (EGC) 可以转移到淋巴结,影响预后.
- 淋巴结 (ELN) 检查不足使准确的分期变得复杂.
- 阳性淋巴结 (LODDS) 的日志概率被提出为一种新的预后因素.
研究的目的:
- 将LODDS与机器学习集成在一起,以改善T1N+胃癌 (GC) 的生存预测.
- 评估不同机器学习模型在预测整体存活 (OS) 方面的表现.
主要方法:
- 对SEER数据库和中国验证队列的回顾性分析.
- 拉索和多变量考克斯回归用于因子选择.
- 用于OS预测的CoxPH,RSF和XGBoost模型的开发和比较.
- 为了模型的可解释性,夏普利添加式解释 (SHAP).
主要成果:
- 在LODDS分期测试中,比N分期测试有更好的区别 (C指数为0.65与0.57).
- 随机生存森林 (RSF) 模型的表现优于CoxPH和XGBoost模型.
- RSF获得了高C指数 (0.79培训,0.80验证) 和有利的校准.
结论:
- LODDS是T1N+GC的独立预后因素.
- 将LODDS集成到机器学习模型中,特别是RSF,可以提高生存预测的准确性.
- 这种方法为T1N+GC的预后评估提供了有价值的工具.
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