Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma

作     者：Zhi-Min Geng Zhi-Qiang Cai Zhen Zhang Zhao-Hui Tang Feng Xue Chen Chen Dong Zhang Qi Li Rui Zhang Wen-Zhi Li Lin Wang Shu-Bin Si 

作者机构：Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061Shaanxi ProvinceChina Department of Industrial EngineeringSchool of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an 710072Shaanxi ProvinceChina Department of General SurgeryShanghai Xin Hua Hospital Affiliated to School of MedicineShanghai Jiaotong UniversityShanghai 200092China 

出 版 物：《World Journal of Gastroenterology》 (世界胃肠病学杂志（英文版）)

年 卷 期：2019年第25卷第37期

页      面：5655-5666页

核心收录：

学科分类：10[医学] 

基　　金：Supported by the National Natural Science Foundation of China,No.81572420 and No.71871181 the Key Research and Development Program of Shaanxi Province,No.2017ZDXM-SF-055 the Multicenter Clinical Research Project of School of Medicine,Shanghai Jiaotong University,No.DLY201807 

主　　题：Gallbladder carcinoma Bayesian network Surgery Adjuvant therapy Prediction model 

摘      要：BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain *** To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant *** Patients with curatively resected advanced gallbladder adenocarcinoma(T3 and T4)were selected from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.A survival prediction model based on Bayesian network(BN)was constructed using the tree-augmented na?ve Bayes algorithm,and composite importance measures were applied to rank the influence of factors on *** dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:*** confusion matrix and receiver operating characteristic curve were used to evaluate the model *** A total of 818 patients met the inclusion *** median survival time was 9.0 *** accuracy of BN model was 69.67%,and the area under the curve value for the testing dataset was 77.72%.Adjuvant radiation,adjuvant chemotherapy(CTx),T stage,scope of regional lymph node surgery,and radiation sequence were ranked as the top five prognostic factors.A survival prediction table was established based on T stage,N stage,adjuvant radiotherapy(XRT),and *** distribution of the survival time(9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)adjuvant radiationadjuvant chemotherapysurgery *** patients with node-positive disease,the larger benefit predicted by the model is adjuvant *** survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced