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Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events
OBJECTIVES: The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients. MATERIALS AND METHODS: Three thousand eight hundred and six cases of LCSBM were extracted...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047387/ https://www.ncbi.nlm.nih.gov/pubmed/35477385 http://dx.doi.org/10.1186/s12890-022-01936-w |
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author | Yuan, Jiaying Cheng, Zhiyuan Feng, Jian Xu, Chang Wang, Yi Zou, Zixiu Li, Qiang Guo, Shicheng Jin, Li Jiang, Gengxi Shang, Yan Wu, Junjie |
author_facet | Yuan, Jiaying Cheng, Zhiyuan Feng, Jian Xu, Chang Wang, Yi Zou, Zixiu Li, Qiang Guo, Shicheng Jin, Li Jiang, Gengxi Shang, Yan Wu, Junjie |
author_sort | Yuan, Jiaying |
collection | PubMed |
description | OBJECTIVES: The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients. MATERIALS AND METHODS: Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram. RESULTS AND CONCLUSION: The younger patients (18–65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75–0.87), the married (HR = 0.80; 95% CI 0.75–0.86), the White (HR = 0.90, 95% CI 0.84–0.95) and primary site (HR = 0.45, 95% CI 0.39–0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40–1.62), advanced grade (HR = 1.19, 95% CI 1.12–1.25) and advanced T stage (HR = 1.09, 95% CI 1.05–1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622–0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction. |
format | Online Article Text |
id | pubmed-9047387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90473872022-04-29 Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events Yuan, Jiaying Cheng, Zhiyuan Feng, Jian Xu, Chang Wang, Yi Zou, Zixiu Li, Qiang Guo, Shicheng Jin, Li Jiang, Gengxi Shang, Yan Wu, Junjie BMC Pulm Med Research OBJECTIVES: The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients. MATERIALS AND METHODS: Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram. RESULTS AND CONCLUSION: The younger patients (18–65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75–0.87), the married (HR = 0.80; 95% CI 0.75–0.86), the White (HR = 0.90, 95% CI 0.84–0.95) and primary site (HR = 0.45, 95% CI 0.39–0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40–1.62), advanced grade (HR = 1.19, 95% CI 1.12–1.25) and advanced T stage (HR = 1.09, 95% CI 1.05–1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622–0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction. BioMed Central 2022-04-27 /pmc/articles/PMC9047387/ /pubmed/35477385 http://dx.doi.org/10.1186/s12890-022-01936-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yuan, Jiaying Cheng, Zhiyuan Feng, Jian Xu, Chang Wang, Yi Zou, Zixiu Li, Qiang Guo, Shicheng Jin, Li Jiang, Gengxi Shang, Yan Wu, Junjie Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title | Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title_full | Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title_fullStr | Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title_full_unstemmed | Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title_short | Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
title_sort | prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047387/ https://www.ncbi.nlm.nih.gov/pubmed/35477385 http://dx.doi.org/10.1186/s12890-022-01936-w |
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