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人工智能算法预测对免疫检查点抑制剂的反应
Faisal Fa'ak1,2, Nicolas Coudray3,4, George Jour5
1Division of Medical Oncology, Washington University School of Medicine, St. Louis, Missouri.
概括
机器学习模型可以预测黑色素瘤患者对免疫检查点抑制剂 (ICI) 的反应. 新型瘤特征如上皮质组织学和低瘤-肌瘤比率与ICI治疗改善的生存结果有关.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 免疫治疗是一种免疫疗法.
背景情况:
- 免疫检查点抑制剂 (ICI) 已经改变了癌症护理,但患者的反应有很大的差异.
- 由于缺乏可概括的生物标志物,预测ICI反应和不良事件仍然具有挑战性.
- 之前的工作建立了一个监督机器学习 (ML) 模型,用于转移性黑色素瘤的ICI反应.
研究的目的:
- 验证和扩展监督ML算法的可通用性,用于预测较大的黑色素瘤队列中的ICI反应.
- 开发一种自我监督的ML模型,以识别与ICI治疗后患者存活相关的组织学特征.
- 在辅助性和转移性黑色素瘤环境中调查ICI反应和生存率.
主要方法:
- 从639名III/IV期黑色素瘤患者的治疗前治疗血素和素幻灯片的分析,这些患者接受了ICI (抗CTLA-4,抗PD-1或组合) 治疗.
- 在转移性黑色素瘤队列上测试监督ML算法的概括性.
- 开发一种自我监督的ML模型,以将组织学形态与无进展和整体存活相关联.
主要成果:
- 监督的ML算法在预测ICI治疗反应时实现了0.72的AUC.
- 一个深层卷积神经网络将患者分为高风险和低风险组,以无进展生存 (P < 0.0001).
- 在表皮质组织学,低瘤-肌瘤比率和ICI治疗后改善的生存率之间发现了新的关联.
结论:
- 开发的ML算法在预测转移性黑色素瘤的ICI治疗反应方面表现出普遍性.
- 这项研究首次确定了与接受ICI的患者的整体存活率相关的特定瘤组织学特征.
- 这些发现为将基于ML的生物标志物纳入临床实践为个性化黑色素瘤治疗铺平了道路.
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