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Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer

BACKGROUND: This study was aimed to establish the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors. METHODS: A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a tra...

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Autores principales: Geng, Sheng-Kai, Fu, Shao-Mei, Zhang, Hong-Wei, Fu, Yi-Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764558/
https://www.ncbi.nlm.nih.gov/pubmed/36536344
http://dx.doi.org/10.1186/s12885-022-10436-3
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author Geng, Sheng-Kai
Fu, Shao-Mei
Zhang, Hong-Wei
Fu, Yi-Peng
author_facet Geng, Sheng-Kai
Fu, Shao-Mei
Zhang, Hong-Wei
Fu, Yi-Peng
author_sort Geng, Sheng-Kai
collection PubMed
description BACKGROUND: This study was aimed to establish the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors. METHODS: A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a training group and a validation group. Univariate and multivariate ordered logistic regression were used to determine the predictive ability of each variable. A nomogram was performed based on the factors selected from logistic regression results. Receiver operating characteristic curve (ROC) analysis, calibration plots and decision curve analysis (DCA) were used to evaluate the discriminative ability and accuracy of the models. RESULTS: Logistic regression analysis demonstrated that CEA, CA125, CA153, tumor size, vascular-invasion, calcification, and tumor grade were independent prognostic factors for positive ALNs. Integrating all the predictive factors, a nomogram was successfully developed and validated. The C-indexes of the nomogram for prediction of no ALN metastasis, positive ALN, and four and more ALN metastasis were 0.826, 0.706, and 0.855 in training group and 0.836, 0.731, and 0.897 in validation group. Furthermore, calibration plots and DCA demonstrated a satisfactory performance of our nomogram. CONCLUSION: We successfully construct and validate the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors.
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spelling pubmed-97645582022-12-21 Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer Geng, Sheng-Kai Fu, Shao-Mei Zhang, Hong-Wei Fu, Yi-Peng BMC Cancer Research BACKGROUND: This study was aimed to establish the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors. METHODS: A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a training group and a validation group. Univariate and multivariate ordered logistic regression were used to determine the predictive ability of each variable. A nomogram was performed based on the factors selected from logistic regression results. Receiver operating characteristic curve (ROC) analysis, calibration plots and decision curve analysis (DCA) were used to evaluate the discriminative ability and accuracy of the models. RESULTS: Logistic regression analysis demonstrated that CEA, CA125, CA153, tumor size, vascular-invasion, calcification, and tumor grade were independent prognostic factors for positive ALNs. Integrating all the predictive factors, a nomogram was successfully developed and validated. The C-indexes of the nomogram for prediction of no ALN metastasis, positive ALN, and four and more ALN metastasis were 0.826, 0.706, and 0.855 in training group and 0.836, 0.731, and 0.897 in validation group. Furthermore, calibration plots and DCA demonstrated a satisfactory performance of our nomogram. CONCLUSION: We successfully construct and validate the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors. BioMed Central 2022-12-19 /pmc/articles/PMC9764558/ /pubmed/36536344 http://dx.doi.org/10.1186/s12885-022-10436-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Geng, Sheng-Kai
Fu, Shao-Mei
Zhang, Hong-Wei
Fu, Yi-Peng
Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title_full Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title_fullStr Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title_full_unstemmed Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title_short Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
title_sort predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764558/
https://www.ncbi.nlm.nih.gov/pubmed/36536344
http://dx.doi.org/10.1186/s12885-022-10436-3
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