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 

机构地区：[1]Department of Hepatobiliary Surgery,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,Shaanxi Province,China [2]Department of Industrial Engineering,School of Mechanical Engineering,Northwestern Polytechnical University,Xi’an 710072,Shaanxi Province,China [3]Department of General Surgery,Shanghai Xin Hua Hospital Affiliated to School of Medicine,Shanghai Jiaotong University,Shanghai 200092,China

出　　处：《World Journal of Gastroenterology》2019年第37期5655-5666,共12页世界胃肠病学杂志（英文版）

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

摘　　要：BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS 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 survival.The 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 7:3.The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTS A total of 818 patients met the inclusion criteria.The median survival time was 9.0 mo.The 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 CTx.The distribution of the survival time(>9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)>adjuvant radiation>adjuvant chemotherapy>surgery alone.For patients with node-positive disease,the larger benefit predicted by the model is adjuvant chemoradiotherapy.The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P<0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P<0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients.Adjuvant chemoradiotherapy is expecBACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC) after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS 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 survival. The 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 7:3. The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTSA total of 818 patients met the inclusion criteria. The median survival time was9.0 mo. The 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 CTx. The distribution of the survival time(>9.0 mo) was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT) >adjuvant radiation > adjuvant chemotherapy > surgery alone. For patients with node-positive disease, the larger benefit predicted by the model is adjuvant chemoradiotherapy. The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank, surgery alone vs CTx, P < 0.001; surgery alone vs XRT, P = 0.014; surgery alone vs cXRT, P <0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC pati

关 键 词：GALLBLADDER CARCINOMA BAYESIAN network Surgery ADJUVANT therapy Prediction model 

分 类 号：R[医药卫生]