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Cancers (Basel):使用机器学习模型研究接受PD-1或PD-L1抑制剂患者甲状腺相关不良事件的相关因素

2021-11-26 从医路漫漫 MedSci原创

人们积极研究肿瘤发生和转移的分子机制,尤其是T淋巴细胞,尤其是抗原性细胞毒作用,已经吸引了人们越来越多的兴趣来开发用于癌症治疗的免疫疗法。

背景及目的:人们积极研究肿瘤发生和转移的分子机制,尤其是T淋巴细胞,尤其是抗原性细胞毒作用,已经吸引了人们越来越多的兴趣来开发用于癌症治疗的免疫疗法。T细胞活化的各种负性调节因子可作为检查点分子,如细胞毒性T淋巴细胞相关蛋白4(CTLA-4)抑制剂、抗程序性细胞死亡1(PD-1)药物和抗程序性细胞死亡配体1(PDL1)药物免疫检查点抑制剂(ICIs)治疗靶点是通过下调免疫反应和抑制T细胞激活来调节免疫稳态和预防自身免疫。尽管ICIs因其良好的临床疗效而被广泛应用于免疫治疗,但它也可能引起自身免疫相关的不良事件。甲状腺相关的不良事件经常与抗程序性细胞死亡1 (PD-1)或抗程序性细胞死亡配体1 (PD-L1)药物相关。本研究旨在调查接受PD-1或PD-L1抑制剂的患者甲状腺功能障碍的相关因素,并开发各种机器学习方法来预测并发症。

方法:这项回顾性随访研究包括209例在2015年7月至2021年2月间接受ICIs治疗的患者。已诊断为甲状腺功能减退或甲状腺功能亢进。已开始使用甲状腺相关药物或资料不完整的患者被排除在外。在ICIs初始处方日期获取患者特征的基线值。使用电子医疗记录收集数据。收集患者性别、年龄、身高、体重、吸烟史、酒精史、合并症、同期用药、肿瘤类型、肿瘤分期、肿瘤小组绩效量表(ECOGPS)等数据。根据不良事件通用术语标准(CTCAE),甲状腺相关不良事件定义为2级或更高。CTCAE将2级甲状腺功能亢进定义为症状性、甲状腺抑制治疗和日常活动受限。通过内部验证的方法衡量每个机器学习模型的性能。在预测过程中,将整个数据集随机划分,进行模型开发和评估。将一个数据样本随机划分为5个子集后,选择一个子集进行模型验证,其余子集用于建立机器学习模型。这个五次交叉验证迭代重复了100次,以评估机器学习模型的预测能力。最终,本研究共纳入187例患者。采用Logistic回归分析方法探讨上述因素与不良事件的关系。使用各种机器学习方法预测甲状腺相关并发症。

结果:调整协变量后,我们发现吸烟史和高血压分别使甲状腺功能障碍的风险增加约3.7倍和4.1倍(95%置信区间(ci) 1.338-10.496和1.478-11.332,p= 0.012和0.007)。相反,服用阿片类药物的患者发生甲状腺相关并发症的风险比未服用阿片类药物的患者低约4.0倍(95% CI 1.464-11.111, p= 0.007)。在机器学习模型中,随机森林的预测效果最好,其接收方工作特征下的面积为0.770 (95%CI 0.648-0.883),精确Precision-recall曲线下的面积为0.510 (95%CI 0.357-0.666)。

表1 多变量分析确定接受免疫检查点抑制剂治疗的患者甲状腺相关不良事件的预测因素。

表2 Logistic回归模型、弹性网络模型、随机森林模型和支持向量机模型的AUC比较。

图1 用随机森林预测接受ICIS治疗的癌症患者甲状腺相关不良事件的前10个变量(按重要性估计)

图2 弹性网络(ENET)、Logistic回归(LR)、随机森林(RF)和支持向量机径向(SVM_R)预测性能的接收器操作特征曲线

表3 机器学习模型细节。

结论:本研究利用多种机器学习模型进行预测,发现吸烟史、高血压和阿片类药物等因素与接受PD-1/PD-L1抑制剂的癌症患者甲状腺相关不良事件相关。

原文出处:

Kim W,  Cho YA,  Kim DC,et al.Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models.Cancers (Basel) 2021 Oct 30;13(21)

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    2022-06-26 jklm09
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    2021-11-29 anminleiryan
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    2021-11-28 smartjoy
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