title: A model to predict the risk of upgrade to malignancy at surgery in atypical breast lesions discovered on percutaneous biopsy specimens.
abstract: BACKGROUND
  When any atypical feature is identified on a percutaneous biopsy specimen of a suspicious breast lesion, surgical excision is mandatory, leading to unnecessary surgeries in 70-90% of the cases. The purpose of this study was to develop a model to predict the presence of cancer at surgery that would be applicable to all atypical lesions.
  
  
  METHODS
  We collected complete clinical, radiological, and double-reading histological data concerning all patients with a diagnosis of a pure atypical lesion on image-guided biopsy performed at the One-Stop Breast Care Unit between 2004 and 2011.
  
  
  RESULTS
  Among the 204 eligible patients, 49 cancers (24%) had been diagnosed at definitive surgery (20 ductal carcinoma in situ, 20 invasive ductal, and 9 invasive lobular carcinoma). The univariate analysis retrieved age (p=0.03), the focus size in mm (p=0.02), the number of biopsy cores (p=0.02), the disappearance of radiological anomalies after biopsy (p=0.05), the mean number of atypical foci (p=0.05) and the percentage of atypical lobules and ducts for lobular neoplasia (p=0.04) as factors associated with cancer at surgery, whereas neither Ki67 nor ALDH1 expression was significantly correlated. The final most informative nomogram comprised information on patient age, the disappearance of radiological anomalies after biopsy and a focus size>15 mm. For the optimal threshold (risk of cancer=21%), sensitivity, specificity, positive predictive value, and negative predictive value were 78, 66, 36, and 90%, respectively.
  
  
  CONCLUSIONS
  After validation, this model could help to identify a subset of patients with premalignant disease who could be spared surgery.
authors.affiliation: Department of Surgery, Institut Gustave Roussy and University Paris Sud, Villejuif Cedex, France. catherine.uzan@igr.fr
authors.forename: Catherine
authors.initials: C
authors.lastname: Uzan
authors.forename: Chafika
authors.initials: C
authors.lastname: Mazouni
authors.forename: Malek
authors.initials: M
authors.lastname: Ferchiou
authors.forename: Laura
authors.initials: L
authors.lastname: Ciolovan
authors.forename: Corinne
authors.initials: C
authors.lastname: Balleyguier
authors.forename: Marie-Christine
authors.initials: MC
authors.lastname: Mathieu
authors.forename: Philippe
authors.initials: P
authors.lastname: Vielh
authors.forename: Suzette
authors.initials: S
authors.lastname: Delaloge
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy; D001943:Breast Neoplasms / Q000209:etiology* / Q000473:pathology; 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; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011379:Prognosis; D012372:ROC Curve