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、病理分级的生存预测评分系统，4～9分患者的中位生存时间分别为66.8、42.4、26.0、9.0、7.5、2.3个月，差异有统计学意义（P〈0.01）。结论基于贝叶斯网络建立的进展期胆囊癌生存预测模型具有较高的准确性，NMLN、切缘、T分期和病理学分级是影响患者术后生存时间的预后因素，基于NMLN、切缘、T分期及病理分级的生存预测评分系统可用于进展期胆囊癌患者的生存预测与治疗决策指导。
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 months）, the importance ranking showed that NMLN（0.366 6）, margin（0.350 1）, T stage（0.319 2） and pathological grade（0.258 9） were the top 4 prognosis factors influencing the postoperative MST.These four factors were taken as observation variables to get the probability of patients in different survival periods.Basing on these results, a survival prediction score system including NMLN, margin, T stage and pathological grade was designed, the median survival time（month） of 4-9 points were 66.8, 42.4, 26.0, 9.0, 7.5 and 2.3, respectively, there was a statistically significant difference in the different points（P〈0.01）.ConclusionsThe survival prediction model of GBC based on Bayesian network has high accuracy.NMLN, margin, T staging and pathological grade are the top 4 risk factors affecting the survival of patients with advanced GBC who underwent curative resection.The survival prediction score system based on these four factors could be used to predict the survival and to guide the decision making of patients with advanced GBC.
作者
汤朝晖
耿智敏
陈晨
司书宾
蔡志强
宋天强
巩鹏
姜立
邱应和
何宇
翟文龙
李升平
张英才
杨扬
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)
出处
《中华外科杂志》
CAS
CSCD
北大核心
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）
关键词
胆囊肿瘤
预后
生存预测模型
贝叶斯网络
Gallbladder neoplasm
Prognosis
Survival prediction model
Bayesian network
