[基于CoxPH模型和深度学习算法的根治性切除术后辅助化疗的肝内胆管癌患者生存分析]。

J L Chen, X P Yu, Y Tang, C Chen, Y H Qiu, H Wu, T Q Song, Y He, X H Mao, W L Zhai, Z J Cheng, J D Li, Z M Geng, Z H Tang, Z W Quan
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引用次数: 0

摘要

研究目的建立肝内胆管癌(ICC)患者根治性切除术后接受辅助化疗的生存获益预测模型。方法:对 249 例肝内胆管癌患者的临床和病理数据进行分析:回顾性收集2010年1月至2018年12月在中国8家医院接受根治性切除术和辅助化疗的249例ICC患者的临床和病理资料。其中男性121例,女性128例;年龄大于60岁88例,小于60岁161例。通过单变量和多变量Cox回归分析进行特征选择。以总生存时间和生存状态作为结局指标,然后选择目标临床特征。将患者分为高危组和低危组,分析两组患者的生存率差异。利用选定的临床特征,构建传统的CoxPH模型和深度学习DeepSurv生存预测模型,并根据一致性指数(C-index)评估模型的性能。结果显示门静脉侵犯、癌胚抗原>5 μg/L、淋巴细胞计数异常、肿瘤病理分化低级和淋巴结阳性>0是249例根治性切除术后辅助化疗患者总生存期的独立不良预后因素(均为PPConclusion):与传统的Cox模型相比,DeepSurv模型能更准确地预测接受辅助化疗的ICC患者在某个时间点的生存概率,更准确地判断辅助化疗的生存获益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Survival analysis of patients with intrahepatic cholangiocarcinoma treated with adjuvant chemotherapy after radical resection based on CoxPH model and deep learning algorithm].

Objective: To establish a predictive model for 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 at 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 old and 161 cases≤60 years old. Feature selection was performed by univariate and multivariate Cox regression analysis. Overall survival time and survival status were used as outcome indicators,then 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 were evaluated according to concordance index(C-index). Results: Portal vein invasion, carcinoembryonic antigen>5 μg/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 with 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.

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