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Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool
BACKGROUND: This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer. METHODS: Data of 911 SLN positive breast cancer patients were us...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771524/ https://www.ncbi.nlm.nih.gov/pubmed/31338839 http://dx.doi.org/10.1002/jso.25644 |
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author | van den Hoven, Ingrid van Klaveren, David Verheuvel, Nicole C. van la Parra, Raquel F. D. Voogd, Adri C. de Roos, Wilfred K. Bosscha, Koop Heuts, Esther M. Tjan‐Heijnen, Vivianne C. G. Roumen, Rudi M. H. Steyerberg, Ewout W. |
author_facet | van den Hoven, Ingrid van Klaveren, David Verheuvel, Nicole C. van la Parra, Raquel F. D. Voogd, Adri C. de Roos, Wilfred K. Bosscha, Koop Heuts, Esther M. Tjan‐Heijnen, Vivianne C. G. Roumen, Rudi M. H. Steyerberg, Ewout W. |
author_sort | van den Hoven, Ingrid |
collection | PubMed |
description | BACKGROUND: This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer. METHODS: Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer. RESULTS: Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had ≥3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve = 0.82; 95% confidence interval = 0.74‐0.90) and good calibration over the full range of predicted probabilities. CONCLUSION: This new and validated model predicts the extent of nodal involvement in node‐positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment. |
format | Online Article Text |
id | pubmed-6771524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67715242019-10-03 Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool van den Hoven, Ingrid van Klaveren, David Verheuvel, Nicole C. van la Parra, Raquel F. D. Voogd, Adri C. de Roos, Wilfred K. Bosscha, Koop Heuts, Esther M. Tjan‐Heijnen, Vivianne C. G. Roumen, Rudi M. H. Steyerberg, Ewout W. J Surg Oncol Research Articles BACKGROUND: This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer. METHODS: Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer. RESULTS: Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had ≥3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve = 0.82; 95% confidence interval = 0.74‐0.90) and good calibration over the full range of predicted probabilities. CONCLUSION: This new and validated model predicts the extent of nodal involvement in node‐positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment. John Wiley and Sons Inc. 2019-07-23 2019-09-15 /pmc/articles/PMC6771524/ /pubmed/31338839 http://dx.doi.org/10.1002/jso.25644 Text en © 2019 The Authors. Journal of Surgical Oncology Published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles van den Hoven, Ingrid van Klaveren, David Verheuvel, Nicole C. van la Parra, Raquel F. D. Voogd, Adri C. de Roos, Wilfred K. Bosscha, Koop Heuts, Esther M. Tjan‐Heijnen, Vivianne C. G. Roumen, Rudi M. H. Steyerberg, Ewout W. Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title | Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title_full | Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title_fullStr | Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title_full_unstemmed | Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title_short | Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool |
title_sort | predicting the extent of nodal involvement for node positive breast cancer patients: development and validation of a novel tool |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771524/ https://www.ncbi.nlm.nih.gov/pubmed/31338839 http://dx.doi.org/10.1002/jso.25644 |
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