基于CoxPH模型和深度学习算法对肝内胆管癌根治性切除术后辅助化疗患者的生存分析

Survival analysis of patients with intrahepatic cholangiocarcinoma treated with adjuvant chemotherapy after radical resection based on CoxPH model and deep learning algorithm

目的建立肝内胆管癌根治性切除术后辅助化疗患者生存获益的预测模型。方法回顾性收集2010年1月至2018年12月于中国8家三级甲等医院行根治性切除术后辅助化疗的249例肝内胆管癌患者的临床和病理学资料。男性121例,女性128例;>60岁88例,≤60岁161例。通过单因素和多因素Cox回归分析进行特征选择,以总体生存时间和生存状态为结局指标,选择目标变量,并将患者分层为高风险组与低风险组,分析两组之间生存差异。利用筛选出的临床特征分别构建传统CoxPH模型和深度学习DeepSurv生存预测模型,依据一致性指数对模型性能进行评估。结果249例根治性切除术后辅助化疗患者中,影像学发现门静脉侵犯、癌胚抗原>5μg/L、淋巴细胞计数异常、肿瘤病理学分化低级别、阳性淋巴结>0枚是患者总体生存时间的独立不良预后因素(P值均<0.05)。高风险组患者辅助化疗的生存获益低于低风险组(P<0.05)。利用以上5个特征构建传统CoxPH模型并绘制列线图,同时构建深度学习DeepSurv生存预测模型,训练集的一致性指数分别为0.687、0.770,测试集的一致性指数分别为0.606、0.763。结论相较于传统Cox模型,深度学习DeepSurv模型能够更准确地预测肝内胆管癌患者根治性切除术后行辅助化疗患者在某时间点的生存概率,更精准判断辅助化疗的生存获益。

Objective To establish a predictive model for the survival benefit of patients with intrahepatic cholangiocarcinoma(ICC)who received adjuvant chemotherapy after radical resection.Methods The clinical and pathological data of 249 patients with ICC who underwent radical resection and adjuvant chemotherapy in 8 hospitals in China from January 2010 to December 2018 were retrospectively collected.There were 121 males and 128 females,with 88 cases>60 years and 161 cases≤60 years.Feature selection was performed by univariate and multivariate Cox regression analysis.Overall survival time and survival status were used as outcome indicators,then the target clinical features were selected.Patients were stratified into high-risk group and low-risk group,survival differences between the two groups were analyzed.Using the selected clinical features,the traditional CoxPH model and deep learning DeepSurv survival prediction model were constructed,and the performance of the models was evaluated according to the concordance index(C-index).Results Portal vein invasion,carcinoembryonic antigen>5μg/L,abnormal lymphocyte count,low grade tumor pathological differentiation and positive lymph nodes>0 were independent adverse prognostic factors for overall survival in 249 patients with adjuvant chemotherapy after radical resection(all P<0.05).The survival benefit of adjuvant chemotherapy in the high-risk group was significantly lower than that in the low-risk group(P<0.05).Using the above five features,the traditional CoxPH model and the deep learning DeepSurv survival prediction model were constructed.The C-index values of the training set were 0.687 and 0.770,and the C-index values of the test set were 0.606 and 0.763,respectively.Conclusion Compared to the traditional Cox model,the DeepSurv model can more accurately predict the survival probability of patients with ICC undergoing adjuvant chemotherapy at a certain time point and more accurately judge the survival benefit of adjuvant chemotherapy.