预测肺癌患者免疫检查点抑制剂治疗的生存益处:使用真实数据的机器学习方法
Lingyun Pan1, Li Mu1, Haike Lei2
1Department of Pharmacy, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing, China.
International journal of clinical pharmacy
|October 29, 2024
概括
机器学习准确地预测了接受免疫检查点抑制剂 (ICI) 的患者的肺癌生存率. 关键预测因素包括D-二聚合物,性能状态和TNM阶段,改善治疗决策.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 免疫疗法在肺癌中的有效性有很大的差异,需要识别预测性生物标志物.
- 预测患者对免疫检查点抑制剂 (ICI) 的反应对于个性化肺癌治疗至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于预测ICI治疗的肺癌患者的整体存活率.
- 确定影响该患者队列生存结果的关键临床和实验室因素.
主要方法:
- 对1314名接受ICI治疗的肺癌患者的回顾性分析.
- 利用随机生存森林 (RSF) 进行特征选择和预测,并进行向后淘汰.
- 构建了一个可克斯比例危险 (CPH) 模型进行比较,并使用依赖时间的ROC和预测错误曲线进行验证.
主要成果:
- 在预测准确性方面,RSF模型的表现优于CPH模型 (C指数为0.91比0.68).
- 确定了生存的重要预测因素:D-二分体,卡诺夫斯基性能状态,白蛋白,手术,TNM阶段,血小板数和年龄.
- RSF模型实现了高AUC (0.95-0.98培训,0.90-0.95验证) 的1年,3年和5年的生存预测.
结论:
- 一个强大的机器学习模型被开发出来,用于预测接受ICI治疗的肺癌患者的生存结果.
- 这些发现支持在识别预后因素和指导肺癌治疗临床决策时使用机器学习.
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