ArticleBJS open2021
Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data.
Article in BJS open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clinicopathological and imaging factors of surgical margin status and prognosis in breast-conserving therapy.Scientific reports · 2026Article
- Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer.NPJ digital medicine · 2025Article
- External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery.BMC research notes · 2025Article
- Clinicopathological Predictors of Positive Resection Margins in Breast-Conserving Surgery.Annals of surgical oncology · 2024Observational
- An intraoperative nomogram for predicting secondary margin positivity in breast conserving surgery utilizing frozen section analysis.Frontiers in oncology · 2024Article
- Blood flow on ultrasound imaging is a predictor of lump margin status in breast-conserving patients: a retrospective matching study.European journal of medical research · 2023Article
- Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery.Frontiers in oncology · 2022Article
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6 authors.
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Abstract
backgroundPositive 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.
methodsPatients 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.
resultsThere 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.
conclusionThe 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.
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