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Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data

Ellbrant, J. LU ; Gulis, K. LU orcid ; Plasgård, E. ; Svensjö, T. ; Bendahl, P. O. LU and Rydén, L. LU orcid (2021) In BJS Open 5(5).
Abstract
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... (More)
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. (Less)
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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BJS Open
volume
5
issue
5
publisher
Wiley
external identifiers
  • scopus:85117805747
  • pmid:34611702
ISSN
2474-9842
DOI
10.1093/bjsopen/zrab092
language
English
LU publication?
yes
id
491ceab3-cd0f-41d2-b2ce-5f81dd3888e0
date added to LUP
2021-11-23 12:19:52
date last changed
2024-06-15 21:21:27
@article{491ceab3-cd0f-41d2-b2ce-5f81dd3888e0,
  abstract     = {{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.}},
  author       = {{Ellbrant, J. and Gulis, K. and Plasgård, E. and Svensjö, T. and Bendahl, P. O. and Rydén, L.}},
  issn         = {{2474-9842}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{5}},
  publisher    = {{Wiley}},
  series       = {{BJS Open}},
  title        = {{Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data}},
  url          = {{http://dx.doi.org/10.1093/bjsopen/zrab092}},
  doi          = {{10.1093/bjsopen/zrab092}},
  volume       = {{5}},
  year         = {{2021}},
}