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Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma
BACKGROUND: The purpose of this study was to develop a prognostic model for the survival of pediatric patients with rhabdomyosarcoma (RMS) using parameters that are measured during routine clinical management. METHODS: Demographic and clinical variables were evaluated in 1679 pediatric patients with...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162958/ https://www.ncbi.nlm.nih.gov/pubmed/25189734 http://dx.doi.org/10.1186/1471-2407-14-654 |
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author | Yang, Limin Takimoto, Tetsuya Fujimoto, Junichiro |
author_facet | Yang, Limin Takimoto, Tetsuya Fujimoto, Junichiro |
author_sort | Yang, Limin |
collection | PubMed |
description | BACKGROUND: The purpose of this study was to develop a prognostic model for the survival of pediatric patients with rhabdomyosarcoma (RMS) using parameters that are measured during routine clinical management. METHODS: Demographic and clinical variables were evaluated in 1679 pediatric patients with RMS registered in the Surveillance, Epidemiology, and End Results (SEER) program from 1990 to 2010. A multivariate Cox proportional hazards model was developed to predict median, 5-year and 10-year overall survival (OS). The Akaike information criterion technique was used for model selection. A nomogram was constructed using the reduced model after model selection, and was internally validated. RESULTS: Of the total 1679 patients, 543 died. The 5-year OS rate was 64.5% (95% confidence interval (CI), 62.1-67.1%) and the 10-year OS was 61.8% (95%CI, 59.2-64.5%) for the entire cohort. Multivariate analysis identified age at diagnosis, tumor size, histological type, tumor stage, surgery and radiotherapy as significantly associated with survival (p < 0.05). The bootstrap-corrected c-index for the model was 0.74. The calibration curve suggested that the model was well calibrated for all predictions. CONCLUSIONS: This study provided an objective analysis of all currently available data for pediatric RMS from the SEER cancer registry. A nomogram based on parameters that are measured on a routine basis was developed. The nomogram can be used to predict 5- and 10-year OS with reasonable accuracy. This information will be useful for estimating prognosis and in guiding treatment selection. |
format | Online Article Text |
id | pubmed-4162958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41629582014-09-14 Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma Yang, Limin Takimoto, Tetsuya Fujimoto, Junichiro BMC Cancer Research Article BACKGROUND: The purpose of this study was to develop a prognostic model for the survival of pediatric patients with rhabdomyosarcoma (RMS) using parameters that are measured during routine clinical management. METHODS: Demographic and clinical variables were evaluated in 1679 pediatric patients with RMS registered in the Surveillance, Epidemiology, and End Results (SEER) program from 1990 to 2010. A multivariate Cox proportional hazards model was developed to predict median, 5-year and 10-year overall survival (OS). The Akaike information criterion technique was used for model selection. A nomogram was constructed using the reduced model after model selection, and was internally validated. RESULTS: Of the total 1679 patients, 543 died. The 5-year OS rate was 64.5% (95% confidence interval (CI), 62.1-67.1%) and the 10-year OS was 61.8% (95%CI, 59.2-64.5%) for the entire cohort. Multivariate analysis identified age at diagnosis, tumor size, histological type, tumor stage, surgery and radiotherapy as significantly associated with survival (p < 0.05). The bootstrap-corrected c-index for the model was 0.74. The calibration curve suggested that the model was well calibrated for all predictions. CONCLUSIONS: This study provided an objective analysis of all currently available data for pediatric RMS from the SEER cancer registry. A nomogram based on parameters that are measured on a routine basis was developed. The nomogram can be used to predict 5- and 10-year OS with reasonable accuracy. This information will be useful for estimating prognosis and in guiding treatment selection. BioMed Central 2014-09-05 /pmc/articles/PMC4162958/ /pubmed/25189734 http://dx.doi.org/10.1186/1471-2407-14-654 Text en © Yang et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yang, Limin Takimoto, Tetsuya Fujimoto, Junichiro Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title | Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title_full | Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title_fullStr | Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title_full_unstemmed | Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title_short | Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
title_sort | prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162958/ https://www.ncbi.nlm.nih.gov/pubmed/25189734 http://dx.doi.org/10.1186/1471-2407-14-654 |
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