使用真实世界患者数据预测免疫检查点抑制剂的有效性和毒性
Levente Lippenszky1, Kathleen F Mittendorf2, Zoltán Kiss1
1Science and Technology Organization-Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary/San Ramon, CA.
JCO clinical cancer informatics
|March 1, 2024
概括
机器学习模型使用电子健康记录预测免疫检查点抑制剂毒性和生存率. 这个框架提供了个性化的风险-收益概况,不需要额外的临床数据收集.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 数据科学数据科学数据科学
背景情况:
- 免疫检查点抑制剂 (ICI) 改善癌症的结果,但可能导致严重的毒性.
- 预测这些风险对于个性化治疗决策和临床试验设计至关重要.
研究的目的:
- 使用电子健康记录 (EHR) 数据开发一个机器学习 (ML) 框架.
- 在接受ICI的患者中预测肝炎,结肠炎,肺炎和1年整体存活率的风险.
主要方法:
- 利用了来自2200多名ICI治疗患者的现实世界EHR数据.
- 通过在60-365天内汇总实验室数据来设计特征.
- 开发并验证了随机森林分类器.
主要成果:
- 模型实现了0.729和0.755之间的AUC,用于预测肺炎,肝炎,结肠炎和1年生存率.
- 在不同结果特定的特征集中,表现强.
- 队列中主要有黑色素瘤,肺癌或尿生殖系统癌症.
结论:
- 这是第一个ML解决方案,使用常规EHR数据评估个人ICI风险-收益概况.
- 该框架不需要额外的数据收集,促进临床整合.
- 允许个性化风险评估,以改善治疗决策.
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