生活质量尺度机器学习方法用于预测晚期非小细胞肺癌患者的免疫治疗反应
Juanyan Shen1,2,3, Junliang Ma3, Shaolin Chen4
1Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Frontiers in immunology
|August 4, 2025
概括
患者报告的生活质量 (QoL) 亚型预测了先进的非小细胞肺癌 (aNSCLC) 治疗阿特佐利祖马布的结果. 使用机器学习识别QoL亚型可以指导免疫治疗决策,以改善患者的存活率.
科学领域:
- 在瘤学瘤学.
- 免疫治疗是一种免疫疗法.
- 机器学习 机器学习
背景情况:
- 免疫检查点抑制剂 (ICI) 改善了高级非小细胞肺癌 (aNSCLC) 的结果,但疾病进展仍然是一个挑战.
- 患者报告的生活质量 (QoL) 越来越被认为是临床结果的预测指标.
研究的目的:
- 调查aNSCLC患者的基线QoL和临床结果之间的关联,这些患者接受了阿特佐利祖马布治疗.
- 使用机器学习识别和验证预测治疗反应和生存的QoL亚型 (QoLS).
主要方法:
- 来自四项随机临床试验 (IMpower150,BIRCH,OAK,POPLAR) 的QOL数据分析,涉及使用阿特佐利祖马布治疗的aNSCLC患者.
- 在发现队列中识别QoL亚型 (QoLS) 和在外部验证队列中预测共识聚类.
主要成果:
- 确定了两个QoL亚型 (QoLS1和QoLS2). 与QoLS1相比,QoLS2与明显较差的整体存活率 (OS) 和无进展存活率 (PFS) 相关.
- 在外部验证队列中,QoLS2始终表现出不利的操作系统.
- 与化疗相比,QoLS1作为阿特佐利祖马布疗效的积极预测生物标志物,表明与化疗相比,生存益处更大,而QoLS2没有显著益处.
结论:
- 基线QOL的机器学习分析有效地预测了接受阿特佐利祖马布免疫治疗的aNSCLC患者的临床结果.
- QoL亚型可以作为预测生物标志物来个性化免疫疗法策略.
- 将 QoL 评估整合到治疗算法中可能会优化 aNSCLC 中的患者管理和存活率.
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