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
ARTICLE HIGHLIGHTS
Research background

Gallbladder carcinoma (GBC) is the most common biliary tract cancer and the sixth most common gastrointestinal malignancy worldwide. Surgical resection is the only potentially curative treatment for GBC, and the outcome of patients with advanced disease is dismal. The factors affecting the prognosis and role of adjuvant therapy in advanced GBC after curative resection remain unclear.

Research motivation

In order to indentify the factors affecting the prognosis and role of adjuvant therapy in advanced GBC after curative resection, the establishment of an accurate survival prediction model for patients with advanced GBC is of great significance for the selection of individualized treatments to increase the survival time. We have previously applied a Bayesian network (BN) and importance measures to identify the significant factors of survival after surgery for patients with GBC. In the present study, we applied BN to build a model to predict the survival time for patients with advanced GBC following curative resection.

Research objectives

The objective of this study was to provide a survival prediction model and decision-making support to patients with advanced GBC after curative resection, as well as to identify the prognostic factors associated with survival and the role of adjuvant therapy.

Research methods

Patients with curatively resected advanced gallbladder adenocarcinoma (T3 and T4) were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. We constructed a survival prediction model based on the SEER database using the tree-augmented naïve Bayes algorithm, and composite importance measures were applied to rank the influence of prognostic factors on survival. The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.

Research results

A total of 818 patients met the inclusion criteria. The median survival time was 9.0 mo. The accuracy of the BN model was 69.67%, and the area under the curve value for the testing dataset was 77.72%. The importance measures showed that adjuvant radiation, adjuvant chemotherapy (CTx), T stage, scope Reg Ln Sur, and radiation sequence were ranked as the top 5 prognostic factors. A survival prediction table was then established based on T stage, N stage, adjuvant radiotherapy (XRT), and CTx. The prediction model showed that the survival time (>9.0 mo) was affected by different treatments with the order of adjuvant chemoradiotherapy > adjuvant radiation > adjuvant chemotherapy > surgery alone. For patients with node-positive disease, the larger benefit predicted by the model is adjuvant chemoradiotherapy, and the results were validated by the survival analysis further.

Research conclusions

A BN model was constructed to predict the survival time for patients with advanced GBC after curative resection from the SEER database, with a high model accuracy. The prediction model supported the role of adjuvant therapy for advanced GBC patients. For patients with node-negative disease, the model estimated the survival benefit from the addition of XRT and cXRT. For patients with node-positive disease, adjuvant chemoradiotherapy is expected to improve the survival significantly.

Research perspectives

The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients. We will improve the model accuracy based on more data. Large-volume, prospective, randomized, controlled clinical trials are needed to validate the prediction model in the future.