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Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis

BACKGROUND: Understanding the epidemiology and prognostic factors of low-grade gliomas (LGGs) can help estimate the public health impact and optimize risk stratification and treatment strategies. METHODS: 3 337 patients diagnosed with LGGs were collected from the Surveillance, Epidemiology, and End...

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Autores principales: Cao, Junguo, Yan, Weijia, Zhan, Zhixin, Hong, Xinyu, Yan, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014976/
https://www.ncbi.nlm.nih.gov/pubmed/36937393
http://dx.doi.org/10.3389/fonc.2023.1079597
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author Cao, Junguo
Yan, Weijia
Zhan, Zhixin
Hong, Xinyu
Yan, Hong
author_facet Cao, Junguo
Yan, Weijia
Zhan, Zhixin
Hong, Xinyu
Yan, Hong
author_sort Cao, Junguo
collection PubMed
description BACKGROUND: Understanding the epidemiology and prognostic factors of low-grade gliomas (LGGs) can help estimate the public health impact and optimize risk stratification and treatment strategies. METHODS: 3 337 patients diagnosed with LGGs were collected from the Surveillance, Epidemiology, and End Results (SEER) dataset, 2004–2019. The incidence trends of LGGs were analyzed by patient demographics (sex, age, race, and ethnicity). In addition, a competing risk regression model was used to explore the prognostic factors of LGGs by patient demographics, tumor characteristics (histological subtypes, invasiveness, and size), treatment modality, and molecular markers (IDH mutation and 1p/19q codeletion). RESULTS: LGGs occurred more frequently in male, non-Hispanic, and White populations. The incidence rate of mixed gliomas was stable from 2004 to 2013 and decreased dramatically to nearly zero until 2019. The risk of death increased 1.99 times for every 20-year increase in patient age, and 60 years is a predictive cut-off age for risk stratification of LGGs. Male patients showed poorer LGG-specific survival. Among the different subtypes, astrocytoma has the worst prognosis, followed by mixed glioma and oligodendroglioma. Tumors with larger size (≥5 cm) and invasive behavior tended to have poorer survival. Patients who underwent gross total resection had better survival rates than those who underwent subtotal resection. Among the different treatment modalities, surgery alone had the best survival, followed by surgery + radiotherapy + chemotherapy, but chemotherapy alone had a higher death risk than no treatment. Furthermore, age, invasiveness, and molecular markers were the most robust prognostic factors. CONCLUSION: This study reviewed the incidence trends and identified several prognostic factors that help clinicians identify high-risk patients and determine the need for postoperative treatment according to guidelines.
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spelling pubmed-100149762023-03-16 Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis Cao, Junguo Yan, Weijia Zhan, Zhixin Hong, Xinyu Yan, Hong Front Oncol Oncology BACKGROUND: Understanding the epidemiology and prognostic factors of low-grade gliomas (LGGs) can help estimate the public health impact and optimize risk stratification and treatment strategies. METHODS: 3 337 patients diagnosed with LGGs were collected from the Surveillance, Epidemiology, and End Results (SEER) dataset, 2004–2019. The incidence trends of LGGs were analyzed by patient demographics (sex, age, race, and ethnicity). In addition, a competing risk regression model was used to explore the prognostic factors of LGGs by patient demographics, tumor characteristics (histological subtypes, invasiveness, and size), treatment modality, and molecular markers (IDH mutation and 1p/19q codeletion). RESULTS: LGGs occurred more frequently in male, non-Hispanic, and White populations. The incidence rate of mixed gliomas was stable from 2004 to 2013 and decreased dramatically to nearly zero until 2019. The risk of death increased 1.99 times for every 20-year increase in patient age, and 60 years is a predictive cut-off age for risk stratification of LGGs. Male patients showed poorer LGG-specific survival. Among the different subtypes, astrocytoma has the worst prognosis, followed by mixed glioma and oligodendroglioma. Tumors with larger size (≥5 cm) and invasive behavior tended to have poorer survival. Patients who underwent gross total resection had better survival rates than those who underwent subtotal resection. Among the different treatment modalities, surgery alone had the best survival, followed by surgery + radiotherapy + chemotherapy, but chemotherapy alone had a higher death risk than no treatment. Furthermore, age, invasiveness, and molecular markers were the most robust prognostic factors. CONCLUSION: This study reviewed the incidence trends and identified several prognostic factors that help clinicians identify high-risk patients and determine the need for postoperative treatment according to guidelines. Frontiers Media S.A. 2023-03-01 /pmc/articles/PMC10014976/ /pubmed/36937393 http://dx.doi.org/10.3389/fonc.2023.1079597 Text en Copyright © 2023 Cao, Yan, Zhan, Hong and Yan https://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
Cao, Junguo
Yan, Weijia
Zhan, Zhixin
Hong, Xinyu
Yan, Hong
Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title_full Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title_fullStr Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title_full_unstemmed Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title_short Epidemiology and risk stratification of low-grade gliomas in the United States, 2004-2019: A competing-risk regression model for survival analysis
title_sort epidemiology and risk stratification of low-grade gliomas in the united states, 2004-2019: a competing-risk regression model for survival analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014976/
https://www.ncbi.nlm.nih.gov/pubmed/36937393
http://dx.doi.org/10.3389/fonc.2023.1079597
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