基于贝叶斯网络的进展期胆囊癌生存预测模型多中心临床研究  

The survival prediction model of advanced gallbladder cancer based on Bayesian network： a multi-institutional study

作　　者：汤朝晖[1] 耿智敏[2] 陈晨[2] 司书宾[3] 蔡志强[3] 宋天强[4] 巩鹏[5] 姜立 邱应和 何宇 翟文龙[9] 李升平[10] 张英才[11] 杨扬[11] Tang Zhaohui;Geng Zhimin;Chen Chen;Si Shubin;Cai Zhiqiang;Song Tianqiang;Gong Peng;Jiang Li;Qiu Yinghe;He Yu;Zhai Wenlong;Li Shengping;Zhang Yingcai;Yang Yang(Department of General Surgery, Shanghai Xin Hua Hospital Affiliated to School of Medicine, Shanghai Jiaotong University, Shanghai 200092, Chin)

机构地区：[1]上海交通大学医学院附属新华医院普通外科,200092 [2]西安交通大学第一附属医院肝胆外科 [3]西北工业大学机电学院 [4]天津医科大学附属肿瘤医院肝胆肿瘤科 [5]大连医科大学附属第一医院普通外科 [6]华中科技大学同济医学院附属同济医院普通外科 [7]海军军医大学东方肝胆外科医院胆道一科 [8]陆军军医大学第一附属医院全军肝胆外科研究所 [9]郑州大学第一附属医院肝胆胰外科 [10]中山大学肿瘤防治中心肝胆科 [11]中山大学附属第三医院肝脏外科

出　　处：《中华外科杂志》2018年第5期342-349,共8页Chinese Journal of Surgery

基　　金：国家自然科学基金资助项目（81572420,71631001,81772521）;陕西省重点研发计划（2017ZDXM-SF-055）;上海交通大学医学院附属新华医院院级临床研究培育基金项目（17CSK06）

摘　　要：目的探讨基于贝叶斯网络建立进展期胆囊癌患者根治性切除术后生存预测模型的临床价值。方法回顾性分析国内9家中心2010年1月至2015年12月收治的经根治性手术治疗的进展期胆囊癌患者临床资料，纳入生存时间、阳性淋巴结数目（NMLN）、T分期、病理学分级、切缘、黄疸、肝脏浸润、年龄、性别、肿瘤形态10个变量因素，运用Bayesia Lab软件建立模型，基于树增益朴素贝叶斯算法建立以生存时间为目标节点的中位生存时间预测模型。采用混淆矩阵和受试者工作特征（ROC）曲线及ROC曲线下面积评价模型预测效果的优劣。运用Bayesia Lab进行10个变量因素的先验统计分析和以生存时间为目标变量、剩余因素为属性变量的后验分析，基于后验分析结果开展多态Birnbaum重要度计算，给出各属性变量的重要度排序。排序结果筛选前4种因素建立胆囊癌生存概率预测表。使用Kaplan-Meier法绘制生存曲线，生存分析采用Log-rank检验。结果共316例患者纳入研究，其中男性109例，女性207例，男女比例为1.0∶1.9，年龄（62.0±10.8）岁。R0切除298例（94.3%），R1切除18例（5.7%）。T分期：T3期287例（90.8%），T4期29例（9.2%）。总体中位生存时间（MST）为23.77个月，1、3、5年累积生存率分别为67.4%、40.8%、32.0%。正确预测值分别为121例（MST≤23.77个月）和115例（MST〉23.77个月），模型预测精确度为74.86%。生存时间的先验概率为0.503 2（MST≤23.77个月）和0.496 8（MST〉23.77个月）。重要度排序结果表明，NMLN（0.366 6）、切缘（0.350 1）、T分期（0.319 2）和病理分级（0.258 9）是影响患者术后生存时间的前4位预后因素。将NMLN、切缘、T分期和病理学分级4个因素作为观测变量，得出不同状态下患者处于各个生存时间段的概率。在此基础上，设计一种基于NMLN、切缘、T分期、病理分级的生存预ObjectiveTo investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer（GBC）who underwent curative intent surgery.MethodsThe clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented na？ve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes（NMLN）, T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis（survival time as the target variable, the remaining factors as the attribute variables）was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test.ResultsA total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was （62.0±10.8）years.There was 298 cases（94.3%） R0 resection and 18 cases（5.7%） R1 resection.T staging： 287 cases（90.8%） T3 and 29 cases（9.2%） T4.The median survival time（MST） was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121（≤23.77 months） and 115（〉23.77 months） respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2（≤23.77 months） and 0.496 8（〉23.77

关 键 词：胆囊肿瘤 预后 生存预测模型 贝叶斯网络 

分 类 号：R735.8[医药卫生—肿瘤]