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The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database

Background: Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Methods: Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEER(*)...

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Autores principales: Li, Hongjian, He, Yingya, Huang, Lianfang, Luo, Hui, Zhu, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333664/
https://www.ncbi.nlm.nih.gov/pubmed/32676458
http://dx.doi.org/10.3389/fonc.2020.01051
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author Li, Hongjian
He, Yingya
Huang, Lianfang
Luo, Hui
Zhu, Xiao
author_facet Li, Hongjian
He, Yingya
Huang, Lianfang
Luo, Hui
Zhu, Xiao
author_sort Li, Hongjian
collection PubMed
description Background: Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Methods: Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEER(*)Stat software. After eliminating invalid and missing clinical information, 3,635 patients (total group) were finally identified and randomly divided into the training group (2,183 cases) and the verification group (1,452 cases). Cox proportional risk regression model was used in the training group, the verification group and the total group to analyze the prognostic factors of patients in the training group, and then the nomogram was constructed. C-indexes and calibration curves were used to evaluate the predictive value of nomogram by internal (training group data) and external validation (verification group data). Results: Cox proportional risk regression model in the training group showed that age, year of diagnosis, laterality, radiation, chemotherapy were all influential factors for prognosis of patients with glioblastoma (P < 0.05) and were all used to construct nomogram as well. The internal and external validation results of nomogram showed that the C-index of the training group was 0.729 [95% CI was (0.715, 0.743)], and the verification group was 0.734 [95% CI was (0.718, 0.750)]. The calibration curves of both groups showed good consistency. Conclusions: The proposed nomogram resulted in accurate prognostic prediction for patients with glioblastoma.
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spelling pubmed-73336642020-07-15 The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database Li, Hongjian He, Yingya Huang, Lianfang Luo, Hui Zhu, Xiao Front Oncol Oncology Background: Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Methods: Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEER(*)Stat software. After eliminating invalid and missing clinical information, 3,635 patients (total group) were finally identified and randomly divided into the training group (2,183 cases) and the verification group (1,452 cases). Cox proportional risk regression model was used in the training group, the verification group and the total group to analyze the prognostic factors of patients in the training group, and then the nomogram was constructed. C-indexes and calibration curves were used to evaluate the predictive value of nomogram by internal (training group data) and external validation (verification group data). Results: Cox proportional risk regression model in the training group showed that age, year of diagnosis, laterality, radiation, chemotherapy were all influential factors for prognosis of patients with glioblastoma (P < 0.05) and were all used to construct nomogram as well. The internal and external validation results of nomogram showed that the C-index of the training group was 0.729 [95% CI was (0.715, 0.743)], and the verification group was 0.734 [95% CI was (0.718, 0.750)]. The calibration curves of both groups showed good consistency. Conclusions: The proposed nomogram resulted in accurate prognostic prediction for patients with glioblastoma. Frontiers Media S.A. 2020-06-26 /pmc/articles/PMC7333664/ /pubmed/32676458 http://dx.doi.org/10.3389/fonc.2020.01051 Text en Copyright © 2020 Li, He, Huang, Luo and Zhu. 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
Li, Hongjian
He, Yingya
Huang, Lianfang
Luo, Hui
Zhu, Xiao
The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title_full The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title_fullStr The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title_full_unstemmed The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title_short The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database
title_sort nomogram model predicting overall survival and guiding clinical decision in patients with glioblastoma based on the seer database
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333664/
https://www.ncbi.nlm.nih.gov/pubmed/32676458
http://dx.doi.org/10.3389/fonc.2020.01051
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