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Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data
BACKGROUND: Positive margins after breast-conserving surgery (BCS) and subsequent second surgery are associated with increased costs and patient discomfort. The aim of this study was to develop a prediction model for positive margins based on risk factors available before surgery. METHODS: Patients...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493005/ https://www.ncbi.nlm.nih.gov/pubmed/34611702 http://dx.doi.org/10.1093/bjsopen/zrab092 |
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author | Ellbrant, J Gulis, K Plasgård, E Svensjö, T Bendahl, P O Rydén, L |
author_facet | Ellbrant, J Gulis, K Plasgård, E Svensjö, T Bendahl, P O Rydén, L |
author_sort | Ellbrant, J |
collection | PubMed |
description | BACKGROUND: Positive margins after breast-conserving surgery (BCS) and subsequent second surgery are associated with increased costs and patient discomfort. The aim of this study was to develop a prediction model for positive margins based on risk factors available before surgery. METHODS: Patients undergoing BCS for in situ or invasive cancer between 2015 and 2016 at site A formed a development cohort; those operated during 2017 in site A and B formed two validation cohorts. MRI was not used routinely. Preoperative radiographic and tumour characteristics and method of operation were collected from patient charts. Multivariable logistic regression was used to develop a prediction model for positive margins including variables with discriminatory capacity identified in a univariable model. The discrimination and calibration of the prediction model was assessed in the validation cohorts, and a nomogram developed. RESULTS: There were 432 patients in the development cohort, and 190 and 157 in site A and B validation cohorts respectively. Positive margins were identified in 77 patients (17.8 per cent) in the development cohort. A non-linear transformation of mammographic tumour size and six variables (visible on mammography, ductal carcinoma in situ, lobular invasive cancer, distance from nipple–areola complex, calcification, and type of surgery) were included in the final prediction model, which had an area under the curve of 0.80 (95 per cent c.i. 0.75 to 0.85). The discrimination and calibration of the prediction model was assessed in the validation cohorts, and a nomogram developed. CONCLUSION: The prediction model showed good ability to predict positive margins after BCS and might, after further validation, be used before surgery in centres without the routine use of preoperative MRI. Presented in part to the San Antonio Breast Cancer Symposium, San Antonio, Texas, USA, December 2018 and the Swedish Surgical Society Annual Meeting, Helsingborg, Sweden, August 2018. |
format | Online Article Text |
id | pubmed-8493005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84930052021-10-06 Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data Ellbrant, J Gulis, K Plasgård, E Svensjö, T Bendahl, P O Rydén, L BJS Open Original Article BACKGROUND: Positive margins after breast-conserving surgery (BCS) and subsequent second surgery are associated with increased costs and patient discomfort. The aim of this study was to develop a prediction model for positive margins based on risk factors available before surgery. METHODS: Patients undergoing BCS for in situ or invasive cancer between 2015 and 2016 at site A formed a development cohort; those operated during 2017 in site A and B formed two validation cohorts. MRI was not used routinely. Preoperative radiographic and tumour characteristics and method of operation were collected from patient charts. Multivariable logistic regression was used to develop a prediction model for positive margins including variables with discriminatory capacity identified in a univariable model. The discrimination and calibration of the prediction model was assessed in the validation cohorts, and a nomogram developed. RESULTS: There were 432 patients in the development cohort, and 190 and 157 in site A and B validation cohorts respectively. Positive margins were identified in 77 patients (17.8 per cent) in the development cohort. A non-linear transformation of mammographic tumour size and six variables (visible on mammography, ductal carcinoma in situ, lobular invasive cancer, distance from nipple–areola complex, calcification, and type of surgery) were included in the final prediction model, which had an area under the curve of 0.80 (95 per cent c.i. 0.75 to 0.85). The discrimination and calibration of the prediction model was assessed in the validation cohorts, and a nomogram developed. CONCLUSION: The prediction model showed good ability to predict positive margins after BCS and might, after further validation, be used before surgery in centres without the routine use of preoperative MRI. Presented in part to the San Antonio Breast Cancer Symposium, San Antonio, Texas, USA, December 2018 and the Swedish Surgical Society Annual Meeting, Helsingborg, Sweden, August 2018. Oxford University Press 2021-10-06 /pmc/articles/PMC8493005/ /pubmed/34611702 http://dx.doi.org/10.1093/bjsopen/zrab092 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Ellbrant, J Gulis, K Plasgård, E Svensjö, T Bendahl, P O Rydén, L Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title | Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title_full | Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title_fullStr | Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title_full_unstemmed | Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title_short | Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
title_sort | validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493005/ https://www.ncbi.nlm.nih.gov/pubmed/34611702 http://dx.doi.org/10.1093/bjsopen/zrab092 |
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