National Institute of Clinical Research, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
Shanghai Long For Health Data Technology Co.ltd, Shanghai, China.
Cancer Med. 2023 Feb;12(3):3744-3757. doi: 10.1002/cam4.5060. Epub 2022 Jul 24.
Few models exist to predict mortality in cancer patients receiving immunotherapy. Our aim was to build a machine learning-based risk stratification model for predicting mortality in atezolizumab-treated cancer patients.
Data from 2538 patients in eight atezolizumab-treated cancer clinical trials across three cancer types (non-small-cell lung cancer, bladder transitional cell carcinoma, and renal cell carcinoma) were included. The whole cohort was randomly split into development and validation cohorts in a 7:3 ratio. Machine-learning algorithms (extreme gradient boosting, random forest, logistic regression with lasso regularization, support vector machine, and K-nearest neighbor) were applied to develop prediction models. Model performance was mainly assessed by area under the receiver operating characteristic curve (AUC) value, calibration plot, and decision curve analysis. The probability of death risk was then stratified.
One thousand and three hundred and seventy-nine (54.33%) patients died. The random forest (RF) model was overall the best in terms of predictive performance, with the AUC of 0.844 (95% confidence interval [CI]: 0.826-0.862) in the development cohort and 0.786 (95% CI: 0.754-0.818) in the validation cohort for predicting mortality. Twelve baseline variables contributing to mortality prediction in the RF model were C-reactive protein, PD-L1 level, cancer type, prior liver metastasis, derived neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, hemoglobin, white blood cell count, number of metastatic sites, pulse rate, and Eastern Cooperative Oncology Group (ECOG) performance status. A total of 1782 (70.2%) patients were separated into the high-risk and 756 (29.8%) low-risk groups. Patients in the high-risk group were significantly more likely to die, experience disease progression, discontinue study, and discontinue treatment than patients in the low-risk group (all p values < 0.001). Risk groups were not associated with immune-related adverse events and grades 3-5 treatment-related adverse events (all p values > 0.05).
RF model has good performance in mortality prediction and risk stratification for cancer patients receiving atezolizumab monotherapy.
目前用于预测接受免疫治疗的癌症患者死亡率的模型较少。本研究旨在建立一个基于机器学习的风险分层模型，用于预测接受阿替利珠单抗治疗的癌症患者的死亡率。
纳入了来自三个癌种（非小细胞肺癌、膀胱癌和肾细胞癌）的 8 项阿替利珠单抗治疗癌症临床试验的 2538 例患者的数据。整个队列按照 7:3 的比例随机分为开发队列和验证队列。应用机器学习算法（极端梯度提升、随机森林、带有 LASSO 正则化的逻辑回归、支持向量机和 K-最近邻）来开发预测模型。主要通过接受者操作特征曲线下面积（AUC）值、校准图和决策曲线分析来评估模型性能。然后对死亡风险概率进行分层。
1379 例（54.33%）患者死亡。随机森林（RF）模型在预测性能方面总体表现最佳，其在开发队列中的 AUC 为 0.844（95%置信区间：0.826-0.862），在验证队列中的 AUC 为 0.786（95%置信区间：0.754-0.818）。RF 模型中预测死亡的 12 个基线变量为 C 反应蛋白、PD-L1 水平、癌症类型、既往肝转移、中性粒细胞与淋巴细胞比值、碱性磷酸酶、白蛋白、血红蛋白、白细胞计数、转移部位数量、脉搏率和东部肿瘤协作组（ECOG）表现状态。共有 1782 例（70.2%）患者被分为高危组和 756 例（29.8%）低危组。高危组患者死亡、疾病进展、退出研究和停药的可能性明显高于低危组（均 p 值<0.001）。风险组与免疫相关不良事件和 3-5 级治疗相关不良事件无相关性（均 p 值>0.05）。
RF 模型在预测接受阿替利珠单抗单药治疗的癌症患者死亡率和风险分层方面具有良好的性能。
