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A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study

BACKGROUND: Breast cancer liver metastasis (BCLM) is a severe condition often resulting in early death. The identification of prognostic factors and the construction of accurate predictive models can guide clinical decision-making. METHODS: A large sample of data from the Surveillance, Epidemiology,...

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Autores principales: Liu, Shaochun, Jia, Yingxue, Chai, Jiaying, Ge, Han, Huang, Runze, Li, Anlong, Cheng, Huaidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510350/
https://www.ncbi.nlm.nih.gov/pubmed/37724916
http://dx.doi.org/10.1177/10732748231202851
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author Liu, Shaochun
Jia, Yingxue
Chai, Jiaying
Ge, Han
Huang, Runze
Li, Anlong
Cheng, Huaidong
author_facet Liu, Shaochun
Jia, Yingxue
Chai, Jiaying
Ge, Han
Huang, Runze
Li, Anlong
Cheng, Huaidong
author_sort Liu, Shaochun
collection PubMed
description BACKGROUND: Breast cancer liver metastasis (BCLM) is a severe condition often resulting in early death. The identification of prognostic factors and the construction of accurate predictive models can guide clinical decision-making. METHODS: A large sample of data from the Surveillance, Epidemiology, and End Results (SEER) database was analyzed, including 3711 patients diagnosed with de novo BCLM between 2010 and 2015. Predictive models were developed using histograms, and stepwise regression addressed variable collinearity. Internal validation was performed, and results were compared to similar studies. RESULTS: In this study of 3711 BCLM patients, 2571 didn't have early death. Out of the 1164 who died early, 1086 had cancer-specific early death. Prognostic factors for early death, including age, race, tumor size, and lymph node involvement, were identified. A nomogram based on these factors was constructed, accurately predicting early all-cause and cancer-specific death. CONCLUSIONS: Valuable insights into the prognosis of BCLM patients were provided, and important prognostic factors for early death were identified. The developed nomogram can assist clinicians in identifying high-risk patients for early death and inform treatment decisions.
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spelling pubmed-105103502023-09-21 A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study Liu, Shaochun Jia, Yingxue Chai, Jiaying Ge, Han Huang, Runze Li, Anlong Cheng, Huaidong Cancer Control Original Research Article BACKGROUND: Breast cancer liver metastasis (BCLM) is a severe condition often resulting in early death. The identification of prognostic factors and the construction of accurate predictive models can guide clinical decision-making. METHODS: A large sample of data from the Surveillance, Epidemiology, and End Results (SEER) database was analyzed, including 3711 patients diagnosed with de novo BCLM between 2010 and 2015. Predictive models were developed using histograms, and stepwise regression addressed variable collinearity. Internal validation was performed, and results were compared to similar studies. RESULTS: In this study of 3711 BCLM patients, 2571 didn't have early death. Out of the 1164 who died early, 1086 had cancer-specific early death. Prognostic factors for early death, including age, race, tumor size, and lymph node involvement, were identified. A nomogram based on these factors was constructed, accurately predicting early all-cause and cancer-specific death. CONCLUSIONS: Valuable insights into the prognosis of BCLM patients were provided, and important prognostic factors for early death were identified. The developed nomogram can assist clinicians in identifying high-risk patients for early death and inform treatment decisions. SAGE Publications 2023-09-19 /pmc/articles/PMC10510350/ /pubmed/37724916 http://dx.doi.org/10.1177/10732748231202851 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Article
Liu, Shaochun
Jia, Yingxue
Chai, Jiaying
Ge, Han
Huang, Runze
Li, Anlong
Cheng, Huaidong
A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title_full A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title_fullStr A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title_full_unstemmed A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title_short A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study
title_sort predictive model for the early death of breast cancer with synchronous liver metastases: a population-based study
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510350/
https://www.ncbi.nlm.nih.gov/pubmed/37724916
http://dx.doi.org/10.1177/10732748231202851
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