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Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer
OBJECTIVE: By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931069/ https://www.ncbi.nlm.nih.gov/pubmed/36816930 http://dx.doi.org/10.3389/fonc.2023.1071076 |
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author | Geng, Anqi Xiao, Jingjing Dong, Bingyao Yuan, Shifang |
author_facet | Geng, Anqi Xiao, Jingjing Dong, Bingyao Yuan, Shifang |
author_sort | Geng, Anqi |
collection | PubMed |
description | OBJECTIVE: By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients. METHOD: The Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts. RESULTS: Cox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits. CONCLUSIONS: The constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals. |
format | Online Article Text |
id | pubmed-9931069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99310692023-02-16 Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer Geng, Anqi Xiao, Jingjing Dong, Bingyao Yuan, Shifang Front Oncol Oncology OBJECTIVE: By identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients. METHOD: The Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts. RESULTS: Cox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P<0.05). With this premise, the nomogram model was constructed and evaluated. The C-index value of the nomogram model was 0.830 in the training group and 0.914 in the validation group. Moreover, both the calibration and ROC curves for the proposed model exhibited reliable performance, and the clinical decision curve analysis showed that the proposed model can bring clinical benefits. CONCLUSIONS: The constructed nomogram can accurately predict individual survival probabilities and may serve as a clinical decision support tool for clinicians to optimize treatment in individuals. Frontiers Media S.A. 2023-02-01 /pmc/articles/PMC9931069/ /pubmed/36816930 http://dx.doi.org/10.3389/fonc.2023.1071076 Text en Copyright © 2023 Geng, Xiao, Dong and Yuan 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 Geng, Anqi Xiao, Jingjing Dong, Bingyao Yuan, Shifang Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title_full | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title_fullStr | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title_full_unstemmed | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title_short | Analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
title_sort | analysis of prognostic factors and construction of prognostic models for triple-positive breast cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931069/ https://www.ncbi.nlm.nih.gov/pubmed/36816930 http://dx.doi.org/10.3389/fonc.2023.1071076 |
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