Retrospective Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 7, 2019; 25(37): 5655-5666
Published online Oct 7, 2019. doi: 10.3748/wjg.v25.i37.5655
Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
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
Zhi-Min Geng, Feng Xue, Chen Chen, Dong Zhang, Qi Li, Rui Zhang, Wen-Zhi Li, Lin Wang, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
Zhi-Qiang Cai, Zhen Zhang, Shu-Bin Si, Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi Province, China
Zhao-Hui Tang, Department of General Surgery, Shanghai Xin Hua Hospital Affiliated to School of Medicine, Shanghai Jiaotong University, Shanghai 200092, China
Author contributions: Geng ZM, Cai ZQ, Tang ZH, and Si SB designed the research; Xue F accessed the SEER database and acquired the data; Geng ZM, Cai ZQ, Zhang Z, Tang ZH, Chen C, Zhang D, Li Q, Zhang R, Li WZ, Wang L, and Si SB analyzed and interpreted the data; Geng ZM, Zhang Z, and Cai ZQ drafted the manuscript; Tang ZH and Si SB revised the manuscript critically; Geng ZM and Cai ZQ contributed equally to this work.
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; and the Multi-center Clinical Research Project of School of Medicine, Shanghai Jiaotong University, No. DLY201807.
Institutional review board statement: This study was reviewed and approved by the Institution Review Board of The First Affiliated Hospital of Xi'an Jiaotong University.
Informed consent statement: As this study is based on a publicly available database without identifying patient information, informed consent was not needed.
Conflict-of-interest statement: All authors declare no conflict of interest related to this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Shu-Bin Si, PhD, Dean, Professor, Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, Shaanxi Province, China. sisb@nwpu.edu.cn
Telephone: +86-13991363388
Received: July 4, 2019
Peer-review started: July 5, 2019
First decision: August 2, 2019
Revised: August 30, 2019
Accepted: September 9, 2019
Article in press: August 2, 2019
Published online: October 7, 2019
Processing time: 88 Days and 12.3 Hours
Core Tip

Core tip: A Bayesian network model was constructed to predict the survival time for patients with advanced gallbladder carcinoma (GBC) after curative resection from the Surveillance, Epidemiology, and End Results database, with a model accuracy of 69.67%, and the area under the curve for the testing dataset was 77.72%. Adjuvant radiation, chemotherapy, and T stage were ranked as the top three prognostic factors by importance measures. The prediction model supported the role of adjuvant therapy for advanced GBC patients after curative resection. Adjuvant chemoradiotherapy is expected to improve the survival more significantly for patients with node-positive disease.