机器学习预测癌症老年患者的死亡率:使用两个大型法国队列开发和外部验证老年癌症评分系统
Etienne Audureau1,2,3, Pierre Soubeyran4, Claudia Martinez-Tapia1
1INSERM, IMRBU955, Univ Paris Est Créteil, Créteil, France.
概括
一个新的老年癌症评分系统 (GCSS) 准确预测了老年癌症患者的死亡率. 这种使用法国队列数据开发的机器学习工具有助于预后和临床决策,以改善患者护理.
科学领域:
- 老年瘤学 老年瘤学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 老年癌症患者的预后是复杂的,因为目前的预测模型的异质性和局限性.
- 准确的预测对于这个人群的个性化治疗和护理规划至关重要.
研究的目的:
- 开发和外部验证老年癌症评分系统 (GCSS) 以改善死亡率预测.
- 改进年龄较大的癌症患者在一年内进行老年期后评估 (GA) 的个性化预后.
主要方法:
- 利用来自法国两个前性多中心队列 (ELCAPA和ONCODAGE) 的数据进行培训和验证.
- 比较了Cox回归,决策树 (DT) 和随机生存森林 (RSF) 模型,使用时间依赖的接收器操作员曲线 (tAUC) 下的面积.
- 包括瘤,老年因素和常规生物标志物作为候选预测因素.
主要成果:
- 随机生存森林 (RSF) 模型实现了最高的预测性能 (12 个月 tAUC: 0.87).
- 确定的主要预测因素包括瘤部位,转移状态,体重减轻,多药性,功能障碍,G-8分数和炎症标志物 (CRP/白蛋白).
- 老年癌症评分系统 (GCSS) 是基于RSF模型开发的.
结论:
- 采用机器学习方法的GCSS提供了对老年癌症患者的准确和外部验证的死亡率预测.
- GCSS有潜力提高临床决策和老年评估后的患者咨询.
- 建议在国际环境中进行进一步的验证,以确认GCSS的通用性.
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