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Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer

Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate...

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Detalles Bibliográficos
Autores principales: Wang, Xiaomin, Yi, Xiaoping, Zhang, Qian, Wang, Xiaoxiao, Zhang, Hanghao, Peng, Shuai, Wang, Kuansong, Liao, Liqiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351098/
https://www.ncbi.nlm.nih.gov/pubmed/36724018
http://dx.doi.org/10.17305/bb.2022.8564
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author Wang, Xiaomin
Yi, Xiaoping
Zhang, Qian
Wang, Xiaoxiao
Zhang, Hanghao
Peng, Shuai
Wang, Kuansong
Liao, Liqiu
author_facet Wang, Xiaomin
Yi, Xiaoping
Zhang, Qian
Wang, Xiaoxiao
Zhang, Hanghao
Peng, Shuai
Wang, Kuansong
Liao, Liqiu
author_sort Wang, Xiaomin
collection PubMed
description Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGrade signatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in the training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.
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spelling pubmed-103510982023-08-01 Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer Wang, Xiaomin Yi, Xiaoping Zhang, Qian Wang, Xiaoxiao Zhang, Hanghao Peng, Shuai Wang, Kuansong Liao, Liqiu Biomol Biomed Research Article Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGrade signatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in the training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM. Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2023-08-01 2023-08-01 /pmc/articles/PMC10351098/ /pubmed/36724018 http://dx.doi.org/10.17305/bb.2022.8564 Text en © 2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Wang, Xiaomin
Yi, Xiaoping
Zhang, Qian
Wang, Xiaoxiao
Zhang, Hanghao
Peng, Shuai
Wang, Kuansong
Liao, Liqiu
Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title_full Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title_fullStr Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title_full_unstemmed Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title_short Incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
title_sort incorporating ultrasound-based lymph node staging significantly improves the performance of a clinical nomogram for predicting preoperative axillary lymph node metastasis in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351098/
https://www.ncbi.nlm.nih.gov/pubmed/36724018
http://dx.doi.org/10.17305/bb.2022.8564
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