title: Prediction of positive resection margins in patients with non-palpable breast cancer.
abstract: BACKGROUND
  In patients undergoing breast conserving surgery for non-palpable breast cancer, obtaining tumour free resection margins is important to prevent reexcision and local recurrence. We developed a model to predict positive resection margins in patients undergoing breast conserving surgery for non-palpable invasive breast cancer.
  
  
  METHODS
  A total of 576 patients with non-palpable invasive breast cancer underwent breast conserving surgery in five hospitals in the Netherlands. A prediction model for positive resection margins was developed using multivariate logistic regression. Calibration and discrimination of the model were assessed and the model was internally validated by bootstrapping.
  
  
  RESULTS
  Positive resection margins were present in 69/576 (12%) patients. Factors independently associated with positive resection margins included mammographic microcalcifications (OR 2.14, 1.22-3.77), tumour size (OR 1.75, 1.20-2.56), presence of DCIS (OR 2.61, 1.41-4.82), Bloom and Richardson grade 2/3 (OR 1.82, 1.05-3.14), and caudal location of the lesion (OR 2.4, 1.35-4.27). The model was well calibrated and moderately able to discriminate between patients with positive versus negative resection margins (AUC 0.70, 95% CI, 0.63-0.77, and 0.69 after internal validation).
  
  
  CONCLUSIONS
  The presented prediction model is moderately able to differentiate between women with high versus low risk of positive margins, and may be useful for surgical planning and preoperative patient counselling.
authors.affiliation: Department of Radiology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands. Electronic address: m.barentsz@umcutrecht.nl.
authors.forename: M W
authors.initials: MW
authors.lastname: Barentsz
authors.affiliation: Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: E L
authors.initials: EL
authors.lastname: Postma
authors.affiliation: Department of Surgery, Diakonessenhuis Utrecht, PO Box 80250, 3508 TG Utrecht, The Netherlands.
authors.forename: T
authors.initials: T
authors.lastname: van Dalen
authors.affiliation: Department of Radiology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: M A A J
authors.initials: MA
authors.lastname: van den Bosch
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, MD 3, 16 Medical Drive, Singapore 117597, Singapore.
authors.forename: H
authors.initials: H
authors.lastname: Miao
authors.affiliation: Department of Surgery, Amphia Hospital Breda, Molengracht 21, 4818 CK Breda, The Netherlands.
authors.forename: P D
authors.initials: PD
authors.lastname: Gobardhan
authors.affiliation: Department of Radiology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: L E
authors.initials: LE
authors.lastname: van den Hout
authors.affiliation: Department of Radiology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: R M
authors.initials: RM
authors.lastname: Pijnappel
authors.affiliation: Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: A J
authors.initials: AJ
authors.lastname: Witkamp
authors.affiliation: Department of Pathology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: P J
authors.initials: PJ
authors.lastname: van Diest
authors.affiliation: Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: R
authors.initials: R
authors.lastname: van Hillegersberg
authors.affiliation: Imaging Division, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
authors.forename: H M
authors.initials: HM
authors.lastname: Verkooijen
mesh_terms: D000368:Aged; D001941:Breast Diseases / Q000150:complications / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000150:complications / Q000473:pathology / Q000601:surgery*; D002114:Calcinosis / Q000150:complications / Q000000981:diagnostic imaging*; D018270:Carcinoma, Ductal, Breast / Q000150:complications / Q000473:pathology / Q000601:surgery*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000150:complications / Q000473:pathology / Q000601:surgery*; D018275:Carcinoma, Lobular / Q000150:complications / Q000473:pathology / Q000601:surgery*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D015412:Mastectomy, Segmental; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D060787:Neoplasm Grading; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D018365:Neoplasm, Residual; D009426:Netherlands; D047368:Tumor Burden
keywords: Breast cancer; Non-palpable lesions; Prediction model; Tumour margins