Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network

Abstract The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was e...

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Autores principales:	Zhi-qiang Cai, Peng Guo, Shu-bin Si, Zhi-min Geng, Chen Chen, Long-long Cong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a05d16415cb9460581dfee937e8d0d9f
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spelling	oai:doaj.org-article:a05d16415cb9460581dfee937e8d0d9f2021-12-02T12:32:18ZAnalysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network10.1038/s41598-017-00491-32045-2322https://doaj.org/article/a05d16415cb9460581dfee937e8d0d9f2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00491-3https://doaj.org/toc/2045-2322Abstract The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was employed to establish and test a BN model using BayesiaLab software. A tree-augmented naïve Bayes method was also used to mine relationships between factors. Composite importance measures were applied to rank the influence of factors on survival time. The accuracy of BN model was 81.15%. For patients with long survival time (>6 months), the true-positive rate of the model was 77.78% and the false-positive rate was 15.25%. According to the built BN model, the sex, age, and pathological type were independent factors for survival of GBC patients. The N stage, liver infiltration, T stage, M stage, and surgical type were dependent variables for survival time prediction. Surgical type and TNM stages were identified as the most significant factors for the prognosis of GBC based on the analysis results of importance measures.Zhi-qiang CaiPeng GuoShu-bin SiZhi-min GengChen ChenLong-long CongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhi-qiang Cai Peng Guo Shu-bin Si Zhi-min Geng Chen Chen Long-long Cong Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
description	Abstract The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was employed to establish and test a BN model using BayesiaLab software. A tree-augmented naïve Bayes method was also used to mine relationships between factors. Composite importance measures were applied to rank the influence of factors on survival time. The accuracy of BN model was 81.15%. For patients with long survival time (>6 months), the true-positive rate of the model was 77.78% and the false-positive rate was 15.25%. According to the built BN model, the sex, age, and pathological type were independent factors for survival of GBC patients. The N stage, liver infiltration, T stage, M stage, and surgical type were dependent variables for survival time prediction. Surgical type and TNM stages were identified as the most significant factors for the prognosis of GBC based on the analysis results of importance measures.
format	article
author	Zhi-qiang Cai Peng Guo Shu-bin Si Zhi-min Geng Chen Chen Long-long Cong
author_facet	Zhi-qiang Cai Peng Guo Shu-bin Si Zhi-min Geng Chen Chen Long-long Cong
author_sort	Zhi-qiang Cai
title	Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
title_short	Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
title_full	Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
title_fullStr	Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
title_full_unstemmed	Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network
title_sort	analysis of prognostic factors for survival after surgery for gallbladder cancer based on a bayesian network
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a05d16415cb9460581dfee937e8d0d9f
work_keys_str_mv	AT zhiqiangcai analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork AT pengguo analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork AT shubinsi analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork AT zhimingeng analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork AT chenchen analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork AT longlongcong analysisofprognosticfactorsforsurvivalaftersurgeryforgallbladdercancerbasedonabayesiannetwork
_version_	1718394110536056832

Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network

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