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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,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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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. |
format | Online Article Text |
id | pubmed-10510350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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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