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Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis

PURPOSE: The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients. METHODS: The pathological f...

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Autores principales: Lei, Huizi, Yuan, Pei, Guo, Changyuan, Ying, Jianming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036576/
https://www.ncbi.nlm.nih.gov/pubmed/36969057
http://dx.doi.org/10.3389/fonc.2023.1096589
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author Lei, Huizi
Yuan, Pei
Guo, Changyuan
Ying, Jianming
author_facet Lei, Huizi
Yuan, Pei
Guo, Changyuan
Ying, Jianming
author_sort Lei, Huizi
collection PubMed
description PURPOSE: The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients. METHODS: The pathological features of 2,561 breast cancer patients were retrospectively reviewed, and the patients were divided into training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram. RESULTS: According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755. CONCLUSION: A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases.
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spelling pubmed-100365762023-03-25 Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis Lei, Huizi Yuan, Pei Guo, Changyuan Ying, Jianming Front Oncol Oncology PURPOSE: The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients. METHODS: The pathological features of 2,561 breast cancer patients were retrospectively reviewed, and the patients were divided into training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram. RESULTS: According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755. CONCLUSION: A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases. Frontiers Media S.A. 2023-03-10 /pmc/articles/PMC10036576/ /pubmed/36969057 http://dx.doi.org/10.3389/fonc.2023.1096589 Text en Copyright © 2023 Lei, Yuan, Guo and Ying 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
Lei, Huizi
Yuan, Pei
Guo, Changyuan
Ying, Jianming
Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title_full Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title_fullStr Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title_full_unstemmed Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title_short Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis
title_sort development and validation of nomograms for predicting axillary non-sln metastases in breast cancer patients: a retrospective analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036576/
https://www.ncbi.nlm.nih.gov/pubmed/36969057
http://dx.doi.org/10.3389/fonc.2023.1096589
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