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Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study

BACKGROUND: Osteosarcoma is one of the most common bone tumors, with strong local aggressiveness and early metastasis. The aim of this study was to describe the epidemiological data and evaluate the prognostic factors for overall survival (OS) and cause-specific survival (CSS) in patients with non-m...

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Autores principales: Huang, Runzhi, Xian, Shuyuan, Shi, Tingting, Yan, Penghui, Hu, Peng, Yin, Huabin, Meng, Tong, Huang, Zongqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604676/
https://www.ncbi.nlm.nih.gov/pubmed/31231119
http://dx.doi.org/10.12659/MSM.915418
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author Huang, Runzhi
Xian, Shuyuan
Shi, Tingting
Yan, Penghui
Hu, Peng
Yin, Huabin
Meng, Tong
Huang, Zongqiang
author_facet Huang, Runzhi
Xian, Shuyuan
Shi, Tingting
Yan, Penghui
Hu, Peng
Yin, Huabin
Meng, Tong
Huang, Zongqiang
author_sort Huang, Runzhi
collection PubMed
description BACKGROUND: Osteosarcoma is one of the most common bone tumors, with strong local aggressiveness and early metastasis. The aim of this study was to describe the epidemiological data and evaluate the prognostic factors for overall survival (OS) and cause-specific survival (CSS) in patients with non-metastatic osteosarcoma. MATERIAL/METHODS: Patients histologically diagnosed with non-metastatic osteosarcoma between 2005 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis, machine learning, and Lasso regression were used to identify the prognostic factors for OS and CSS, and the accuracy of the nomograms was tested and compared with the American Joint Committee on Cancer (AJCC) staging systems. RESULTS: The entire cohort comprised 1000 patients with non-metastatic osteosarcoma. The multivariable analysis suggested that age, tumor size, grade, and American Joint Committee on Cancer (AJCC) T staging were independent prognostic factors for OS and CSS. Additionally, the nomograms based on these results could better predict probability of OS (Internal validation C-index, 0.7095) and CSS (0.7100) compared with the sixth (OS: 0.613; CSS: 0.628) and seventh edition AJCC staging systems (0.602, 0.613). CONCLUSIONS: Relatively young age and low histopathological grade were favorable factors for both OS and CSS. Nomograms based on multivariable models worked well in predicting the probability of death for patients with non-metastatic osteosarcoma.
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spelling pubmed-66046762019-07-17 Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study Huang, Runzhi Xian, Shuyuan Shi, Tingting Yan, Penghui Hu, Peng Yin, Huabin Meng, Tong Huang, Zongqiang Med Sci Monit Clinical Research BACKGROUND: Osteosarcoma is one of the most common bone tumors, with strong local aggressiveness and early metastasis. The aim of this study was to describe the epidemiological data and evaluate the prognostic factors for overall survival (OS) and cause-specific survival (CSS) in patients with non-metastatic osteosarcoma. MATERIAL/METHODS: Patients histologically diagnosed with non-metastatic osteosarcoma between 2005 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis, machine learning, and Lasso regression were used to identify the prognostic factors for OS and CSS, and the accuracy of the nomograms was tested and compared with the American Joint Committee on Cancer (AJCC) staging systems. RESULTS: The entire cohort comprised 1000 patients with non-metastatic osteosarcoma. The multivariable analysis suggested that age, tumor size, grade, and American Joint Committee on Cancer (AJCC) T staging were independent prognostic factors for OS and CSS. Additionally, the nomograms based on these results could better predict probability of OS (Internal validation C-index, 0.7095) and CSS (0.7100) compared with the sixth (OS: 0.613; CSS: 0.628) and seventh edition AJCC staging systems (0.602, 0.613). CONCLUSIONS: Relatively young age and low histopathological grade were favorable factors for both OS and CSS. Nomograms based on multivariable models worked well in predicting the probability of death for patients with non-metastatic osteosarcoma. International Scientific Literature, Inc. 2019-06-24 /pmc/articles/PMC6604676/ /pubmed/31231119 http://dx.doi.org/10.12659/MSM.915418 Text en © Med Sci Monit, 2019 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Clinical Research
Huang, Runzhi
Xian, Shuyuan
Shi, Tingting
Yan, Penghui
Hu, Peng
Yin, Huabin
Meng, Tong
Huang, Zongqiang
Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title_full Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title_fullStr Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title_full_unstemmed Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title_short Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study
title_sort evaluating and predicting the probability of death in patients with non-metastatic osteosarcoma: a population-based study
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604676/
https://www.ncbi.nlm.nih.gov/pubmed/31231119
http://dx.doi.org/10.12659/MSM.915418
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