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

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 c...

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Autores principales: Geng, Zhi-Min, Cai, Zhi-Qiang, Zhang, Zhen, Tang, Zhao-Hui, Xue, Feng, Chen, Chen, Zhang, Dong, Li, Qi, Zhang, Rui, Li, Wen-Zhi, Wang, Lin, Si, Shu-Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785523/
https://www.ncbi.nlm.nih.gov/pubmed/31602165
http://dx.doi.org/10.3748/wjg.v25.i37.5655
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author Geng, Zhi-Min
Cai, Zhi-Qiang
Zhang, Zhen
Tang, Zhao-Hui
Xue, Feng
Chen, Chen
Zhang, Dong
Li, Qi
Zhang, Rui
Li, Wen-Zhi
Wang, Lin
Si, Shu-Bin
author_facet Geng, Zhi-Min
Cai, Zhi-Qiang
Zhang, Zhen
Tang, Zhao-Hui
Xue, Feng
Chen, Chen
Zhang, Dong
Li, Qi
Zhang, Rui
Li, Wen-Zhi
Wang, Lin
Si, Shu-Bin
author_sort Geng, Zhi-Min
collection PubMed
description 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 naïve 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 expected to improve the survival significantly for patients with node-positive disease.
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spelling pubmed-67855232019-10-10 Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma Geng, Zhi-Min Cai, Zhi-Qiang Zhang, Zhen Tang, Zhao-Hui Xue, Feng Chen, Chen Zhang, Dong Li, Qi Zhang, Rui Li, Wen-Zhi Wang, Lin Si, Shu-Bin World J Gastroenterol Retrospective Study 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 naïve 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 expected to improve the survival significantly for patients with node-positive disease. Baishideng Publishing Group Inc 2019-10-07 2019-10-07 /pmc/articles/PMC6785523/ /pubmed/31602165 http://dx.doi.org/10.3748/wjg.v25.i37.5655 Text en ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ 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.
spellingShingle Retrospective Study
Geng, Zhi-Min
Cai, Zhi-Qiang
Zhang, Zhen
Tang, Zhao-Hui
Xue, Feng
Chen, Chen
Zhang, Dong
Li, Qi
Zhang, Rui
Li, Wen-Zhi
Wang, Lin
Si, Shu-Bin
Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title_full Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title_fullStr Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title_full_unstemmed Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title_short Estimating survival benefit of adjuvant therapy based on a Bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
title_sort estimating survival benefit of adjuvant therapy based on a bayesian network prediction model in curatively resected advanced gallbladder adenocarcinoma
topic Retrospective Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785523/
https://www.ncbi.nlm.nih.gov/pubmed/31602165
http://dx.doi.org/10.3748/wjg.v25.i37.5655
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