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Validated prediction model for positive resection margins in breast-conserving surgery based exclusively on preoperative data
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- Ellbrant, J. (author)
- Lund University,Lunds universitet,Anestesiologi och intensivvård,Forskargrupper vid Lunds universitet,Anaesthesiology and Intensive Care Medicine,Lund University Research Groups,Skåne University Hospital
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- Gulis, K. (author)
- Lund University,Lunds universitet,Kirurgi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Surgery (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Central Hospital Kristianstad
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- Plasgård, E. (author)
- Skåne University Hospital
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- (creator_code:org_t)
- 2021-10-06
- 2021
- English.
- In: BJS Open. - : Oxford University Press (OUP). - 2474-9842. ; 5:5
- Journal article (peer-reviewed)
Abstract
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kirurgi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Surgery (hsv//eng)
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- Ellbrant, J.
- Gulis, K.
- Plasgård, E.
- Svensjö, T.
- Bendahl, P. O.
- Rydén, L.
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