The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study
10.3760/cma.j.issn.0529-5815.2018.05.005
- VernacularTitle: 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
- Author:
Zhaohui TANG
1
;
Zhimin GENG
;
Chen CHEN
;
Shubin SI
;
Zhiqiang CAI
;
Tianqiang SONG
;
Peng GONG
;
Li JIANG
;
Yinghe QIU
;
Yu HE
;
Wenlong ZHAI
;
Shengping LI
;
Yingcai ZHANG
;
Yang YANG
Author Information
1. Department of General Surgery, Shanghai Xin Hua Hospital Affiliated to School of Medicine, Shanghai Jiaotong University, Shanghai 200092, China
- Publication Type:Journal Article
- Keywords:
Gallbladder neoplasm;
Prognosis;
Survival prediction model;
Bayesian network
- From:
Chinese Journal of Surgery
2018;56(5):342-349
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery.
Methods:The 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 naïve 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.
Results:A 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).
Conclusions:The 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.
