超越TNM分期:对个性化结肠癌结果的机器学习
Joseph H Cotler1, Lauren M Janczewski1, Ronald J Weigel2
1American College of Surgeons Cancer Programs, National Cancer Database, Chicago, IL.
Surgery
|July 23, 2025
概括
结合临床数据的机器学习模型显著改善了超越传统TNM分期的结肠癌生存预测. 这提高了患者结果的准确性,以便更好地规划治疗.
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:
- 传统的TNM分期缺乏影响癌症存活率的关键患者数据.
- 机器学习为提高预测准确性提供了一个潜在的解决方案.
研究的目的:
- 开发和评估一个机器学习模型,以改善结肠癌存活率预测.
- 将机器学习模型的预测性能与传统的TNM分期进行比较.
主要方法:
- 对382,531例结肠癌病例 (2018-2021) 的回顾性分析.
- 两个模型的开发:单独使用TNM分期与TNM加上使用极端梯度增强的额外临床变量.
- 使用布里尔分数,哈雷尔一致性指数和曲线下的时间依赖区域进行预测效率的比较.
主要成果:
- 结合额外变量的机器学习模型显示出更高的预测准确性.
- 关键指标显示显著改善:布里尔得分从0.19降至0.14,哈雷尔一致性指数从0.73增加到0.83,时间依赖的AUC从0.75增加到0.87.
结论:
- 整合临床参数的机器学习模型提供了更准确的癌症生存预测.
- 这些先进的模型优于传统的TNM解剖分期来预测患者的结果.
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