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可解释的机器学习来预测高级非小细胞肺癌治疗反应

Vinayak S Ahluwalia^1,2, Ravi B Parikh^3,4

¹Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

JCO clinical cancer informatics

|January 3, 2025

概括

与单独使用PD-L1相比,机器学习算法在高级非小细胞肺癌 (NSCLC) 中更好地预测治疗反应. 这种方法可以增强免疫瘤治疗的临床决策.

科学领域:

在瘤学瘤学.
生物标志物发现发现
机器学习在医学中的应用

背景情况:

免疫检查点抑制剂 (ICI) 是有效的癌症治疗方法.
PD-L1表达是高级非小细胞肺癌 (NSCLC) 免疫瘤学 (IO) 单疗法的标准生物标志物.
预测性生物标志物对于优化癌症治疗选择至关重要.

研究的目的:

评估机器学习 (ML) 算法是否可以超过PD-L1作为先进NSCLC第一线治疗的预测生物标志物.
评估ML算法对12个月无进展生存 (PFS) 和整体生存 (OS) 的预测性能.

主要方法:

利用了38 048名先进NSCLC患者的非识别电子健康记录数据库.
训练有素的二进制预测算法来预测12个月的PFS和12个月的OS.
评估了使用测试组AUC的算法,并通过Kaplan-Meier曲线和Cox模型比较了低风险和高风险患者组之间的生存结果.

主要成果:

经过12个月的PFS,ML算法实现了0.701的AUC,经过12个月的OS,则达到0.718.
通过ML识别的低风险患者与高风险患者相比,12个月的疾病进展 (HR=0.47) 和死亡率 (HR=0.31) 显著降低.
在IO单独治疗的ML识别的低风险患者中,进展率 (HR=0.53) 和死亡率 (HR=0.30) 降低.

结论:

ML算法提供了比PD-L1单独更准确的预测先进NSCLC的第一线治疗反应.
ML有潜力改善瘤学中的临床决策,超出单个生物标志物评估范围.

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