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Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis
PURPOSE: Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in brea...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374610/ https://www.ncbi.nlm.nih.gov/pubmed/35864309 http://dx.doi.org/10.1007/s10549-022-06672-7 |
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author | Ngai, Victoria Tai, Justina Cheh Juan Taj, Saima Khanfar, Heba Sfakianakis, Elefterios Bakalis, Athanasios Baker, Rose Ahmed, Muneer |
author_facet | Ngai, Victoria Tai, Justina Cheh Juan Taj, Saima Khanfar, Heba Sfakianakis, Elefterios Bakalis, Athanasios Baker, Rose Ahmed, Muneer |
author_sort | Ngai, Victoria |
collection | PubMed |
description | PURPOSE: Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in breast-conserving surgery for patients with low-risk biological cancers, axillary management has become increasingly conservative. This study aims to identify and assess the clinical predictive value of variables that could play a role in the quantification of axillary burden, including the accuracy of quantifying abnormal axillary nodes on ultrasound. METHODS: A retrospective analysis was conducted of hospital data for female breast cancer patients receiving an ANC at our centre between January 2018 and January 2020. The reference standard for axillary burden was surgical histology following SNB and ANC, allowing categorisation of the patients under ‘low axillary burden’ (2 or fewer pathological macrometastases) or ‘high axillary burden’ (> 2). After exploratory univariate analysis, multivariate logistic regression was conducted to determine relationships between the outcome category and candidate predictor variables: patient age at diagnosis, tumour focality, tumour size on ultrasound and number of abnormal lymph nodes on axillary ultrasound. RESULTS: One hundred and thirty-five patients were included in the analysis. Logistic regression showed that the number of abnormal lymph nodes on axillary ultrasound was the strongest predictor of axillary burden and statistically significant (P = 0.044), with a sensitivity of 66.7% and specificity of 86.8% (P = 0.011). CONCLUSION: Identifying the number of abnormal lymph nodes on preoperative ultrasound can help to quantify axillary nodal burden and identify patients with high axillary burden, and should be documented as standard in axillary ultrasound reports of patients with breast cancer. |
format | Online Article Text |
id | pubmed-9374610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-93746102022-08-14 Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis Ngai, Victoria Tai, Justina Cheh Juan Taj, Saima Khanfar, Heba Sfakianakis, Elefterios Bakalis, Athanasios Baker, Rose Ahmed, Muneer Breast Cancer Res Treat Epidemiology PURPOSE: Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in breast-conserving surgery for patients with low-risk biological cancers, axillary management has become increasingly conservative. This study aims to identify and assess the clinical predictive value of variables that could play a role in the quantification of axillary burden, including the accuracy of quantifying abnormal axillary nodes on ultrasound. METHODS: A retrospective analysis was conducted of hospital data for female breast cancer patients receiving an ANC at our centre between January 2018 and January 2020. The reference standard for axillary burden was surgical histology following SNB and ANC, allowing categorisation of the patients under ‘low axillary burden’ (2 or fewer pathological macrometastases) or ‘high axillary burden’ (> 2). After exploratory univariate analysis, multivariate logistic regression was conducted to determine relationships between the outcome category and candidate predictor variables: patient age at diagnosis, tumour focality, tumour size on ultrasound and number of abnormal lymph nodes on axillary ultrasound. RESULTS: One hundred and thirty-five patients were included in the analysis. Logistic regression showed that the number of abnormal lymph nodes on axillary ultrasound was the strongest predictor of axillary burden and statistically significant (P = 0.044), with a sensitivity of 66.7% and specificity of 86.8% (P = 0.011). CONCLUSION: Identifying the number of abnormal lymph nodes on preoperative ultrasound can help to quantify axillary nodal burden and identify patients with high axillary burden, and should be documented as standard in axillary ultrasound reports of patients with breast cancer. Springer US 2022-07-21 2022 /pmc/articles/PMC9374610/ /pubmed/35864309 http://dx.doi.org/10.1007/s10549-022-06672-7 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/) . |
spellingShingle | Epidemiology Ngai, Victoria Tai, Justina Cheh Juan Taj, Saima Khanfar, Heba Sfakianakis, Elefterios Bakalis, Athanasios Baker, Rose Ahmed, Muneer Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title | Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title_full | Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title_fullStr | Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title_full_unstemmed | Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title_short | Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
title_sort | non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374610/ https://www.ncbi.nlm.nih.gov/pubmed/35864309 http://dx.doi.org/10.1007/s10549-022-06672-7 |
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