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Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis

Predicting the prognosis of gallbladder carcinoma (GBC) has always been important for improving survival. The objective of this study was to determine the risk factors of survival for patients with GBC after surgery and to develop predictive nomograms for overall survival (OS) and cancer-specific su...

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Autores principales: He, Chaobin, Cai, Zhiyuan, Zhang, Yu, Lin, Xiaojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920125/
https://www.ncbi.nlm.nih.gov/pubmed/31921622
http://dx.doi.org/10.3389/fonc.2019.01329
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author He, Chaobin
Cai, Zhiyuan
Zhang, Yu
Lin, Xiaojun
author_facet He, Chaobin
Cai, Zhiyuan
Zhang, Yu
Lin, Xiaojun
author_sort He, Chaobin
collection PubMed
description Predicting the prognosis of gallbladder carcinoma (GBC) has always been important for improving survival. The objective of this study was to determine the risk factors of survival for patients with GBC after surgery and to develop predictive nomograms for overall survival (OS) and cancer-specific survival (CSS) using a large population-based cohort. We identified 2,762 patients with primary resectable GBC in the Surveillance, Epidemiology, and End Results (SEER) database for the period of 2004 to 2014 and another 152 patients with GBC after surgery from Sun Yat-sen University Cancer Center (SYSUCC) for the period of 1997 to 2017. The 1-, 2-, and 3-year cancer-specific mortalities were 37.2, 52.9, and 59.9%, while the competing mortalities were 5.8, 7.8, and 9.0%, respectively. Nomograms were developed to estimate OS and CSS, and these were validated by concordance indexes (C-indexes) and evaluated using receiver operating characteristic (ROC) curves. The C-indexes of the nomograms for OS and CSS prediction were 0.704 and 0.732, respectively. In addition, compared with the 8th Tumor-Node-Metastasis staging system, the newly established nomograms displayed higher areas under the ROC curves for OS and PFS prediction. The nomograms are well-validated and could thus aid individual clinical practice.
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spelling pubmed-69201252020-01-09 Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis He, Chaobin Cai, Zhiyuan Zhang, Yu Lin, Xiaojun Front Oncol Oncology Predicting the prognosis of gallbladder carcinoma (GBC) has always been important for improving survival. The objective of this study was to determine the risk factors of survival for patients with GBC after surgery and to develop predictive nomograms for overall survival (OS) and cancer-specific survival (CSS) using a large population-based cohort. We identified 2,762 patients with primary resectable GBC in the Surveillance, Epidemiology, and End Results (SEER) database for the period of 2004 to 2014 and another 152 patients with GBC after surgery from Sun Yat-sen University Cancer Center (SYSUCC) for the period of 1997 to 2017. The 1-, 2-, and 3-year cancer-specific mortalities were 37.2, 52.9, and 59.9%, while the competing mortalities were 5.8, 7.8, and 9.0%, respectively. Nomograms were developed to estimate OS and CSS, and these were validated by concordance indexes (C-indexes) and evaluated using receiver operating characteristic (ROC) curves. The C-indexes of the nomograms for OS and CSS prediction were 0.704 and 0.732, respectively. In addition, compared with the 8th Tumor-Node-Metastasis staging system, the newly established nomograms displayed higher areas under the ROC curves for OS and PFS prediction. The nomograms are well-validated and could thus aid individual clinical practice. Frontiers Media S.A. 2019-12-12 /pmc/articles/PMC6920125/ /pubmed/31921622 http://dx.doi.org/10.3389/fonc.2019.01329 Text en Copyright © 2019 He, Cai, Zhang and Lin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
He, Chaobin
Cai, Zhiyuan
Zhang, Yu
Lin, Xiaojun
Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title_full Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title_fullStr Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title_full_unstemmed Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title_short Prognostic Model to Predict Cancer-Specific Survival for Patients With Gallbladder Carcinoma After Surgery: A Population-Based Analysis
title_sort prognostic model to predict cancer-specific survival for patients with gallbladder carcinoma after surgery: a population-based analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920125/
https://www.ncbi.nlm.nih.gov/pubmed/31921622
http://dx.doi.org/10.3389/fonc.2019.01329
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AT zhangyu prognosticmodeltopredictcancerspecificsurvivalforpatientswithgallbladdercarcinomaaftersurgeryapopulationbasedanalysis
AT linxiaojun prognosticmodeltopredictcancerspecificsurvivalforpatientswithgallbladdercarcinomaaftersurgeryapopulationbasedanalysis