Pan Lingyun, Mu Li, Lei Haike, Miao Siwei, Hu Xiaogang, Tang Zongwei, Chen Wanyi, Wang Xiaoxiao
Department of Pharmacy, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing, China.
Chongqing Cancer Multi-Omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.
Int J Clin Pharm. 2024 Oct 29. doi: 10.1007/s11096-024-01818-7.
Due to the heterogeneity in the effectiveness of immunotherapy for lung cancer, identifying predictors is crucial.
This study aimed to develop a machine learning model to identify predictors of overall survival in lung cancer patients treated with immune checkpoint inhibitors (ICIs).
A retrospective analysis was performed on data from 1314 lung cancer patients at the Chongqing University Cancer Hospital from September 2018 to September 2022. We used the random survival forest (RSF) model to identify survival-influencing factors, using backward elimination for variable selection. A Cox proportional hazards (CPH) model was constructed using the most significant predictors. We assessed model performance and generalizability using time-dependent receiver operating characteristics (ROC) and predictive error curves.
The RSF model demonstrated better predictive accuracy than the CPH (IBS 0.17 vs. 0.17; C-index 0.91 vs. 0.68), with better discrimination and prediction performance. The influential variables identified included D-dimer, Karnofsky performance status, albumin, surgery, TNM stage, platelet count, and age. The RSF model, which incorporated these variables, achieved area under the curve (AUC) scores of 0.95, 0.94, and 0.98 for 1-, 3-, and 5-year survival predictions, respectively, in the training set. The validation set showed AUCs of 0.94, 0.90, and 0.95, respectively, exceeding the performance of the CPH model.
The study successfully developed a machine learning model that accurately predicted the survival benefits of ICI therapy in lung cancer patients, supporting clinical decision-making in lung cancer treatment.
由于肺癌免疫治疗效果存在异质性，确定预测指标至关重要。
本研究旨在开发一种机器学习模型，以识别接受免疫检查点抑制剂（ICI）治疗的肺癌患者总生存的预测指标。
对2018年9月至2022年9月重庆大学附属肿瘤医院1314例肺癌患者的数据进行回顾性分析。我们使用随机生存森林（RSF）模型识别影响生存的因素，并采用向后剔除法进行变量选择。使用最显著的预测指标构建Cox比例风险（CPH）模型。我们使用时间依赖的受试者工作特征（ROC）曲线和预测误差曲线评估模型性能和可推广性。
RSF模型显示出比CPH模型更好的预测准确性（IBS 0.17对0.17；C指数0.91对0.68），具有更好的区分度和预测性能。识别出的影响变量包括D-二聚体、卡诺夫斯基功能状态、白蛋白、手术、TNM分期、血小板计数和年龄。纳入这些变量的RSF模型在训练集中对1年、3年和5年生存预测的曲线下面积（AUC）得分分别为0.95、0.94和0.98。验证集的AUC分别为0.94、0.90和0.95，超过了CPH模型的性能。
本研究成功开发了一种机器学习模型，可准确预测ICI治疗对肺癌患者的生存获益，为肺癌治疗的临床决策提供支持。
