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A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients
Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1–2 positive sentinel lymph nodes having breas...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736885/ https://www.ncbi.nlm.nih.gov/pubmed/33318591 http://dx.doi.org/10.1038/s41598-020-79016-4 |
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author | Zeng, De Lin, Hao-Yu Zhang, Yu-Ling Wu, Jun-Dong Lin, Kun Xu, Ya Chen, Chun-Fa |
author_facet | Zeng, De Lin, Hao-Yu Zhang, Yu-Ling Wu, Jun-Dong Lin, Kun Xu, Ya Chen, Chun-Fa |
author_sort | Zeng, De |
collection | PubMed |
description | Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1–2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0–2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0–2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829–0.921, P < 0.001) in the modeling group and 0.758 (95% confidence interval 0.702–0.807, P < 0.001) in the validation group. Decision curve analysis demonstrated that the model was clinically useful. The NBR model demonstrated adequate discriminative ability and clinical utility for predicting 0–2 ALN metastases. |
format | Online Article Text |
id | pubmed-7736885 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77368852020-12-15 A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients Zeng, De Lin, Hao-Yu Zhang, Yu-Ling Wu, Jun-Dong Lin, Kun Xu, Ya Chen, Chun-Fa Sci Rep Article Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1–2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0–2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0–2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829–0.921, P < 0.001) in the modeling group and 0.758 (95% confidence interval 0.702–0.807, P < 0.001) in the validation group. Decision curve analysis demonstrated that the model was clinically useful. The NBR model demonstrated adequate discriminative ability and clinical utility for predicting 0–2 ALN metastases. Nature Publishing Group UK 2020-12-14 /pmc/articles/PMC7736885/ /pubmed/33318591 http://dx.doi.org/10.1038/s41598-020-79016-4 Text en © The Author(s) 2020 Open Access This 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/. |
spellingShingle | Article Zeng, De Lin, Hao-Yu Zhang, Yu-Ling Wu, Jun-Dong Lin, Kun Xu, Ya Chen, Chun-Fa A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title | A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title_full | A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title_fullStr | A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title_full_unstemmed | A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title_short | A negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
title_sort | negative binomial regression model for risk estimation of 0–2 axillary lymph node metastases in breast cancer patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736885/ https://www.ncbi.nlm.nih.gov/pubmed/33318591 http://dx.doi.org/10.1038/s41598-020-79016-4 |
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