title: Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics.
abstract: OBJECTIVE
  To predict the probability of malignancy for MRI-detected breast lesions with a multivariate model incorporating patient and lesion characteristics.
  
  
  METHODS
  Retrospective review of 2565 breast MR examinations from 1/03-11/06. BI-RADS 3, 4 and 5 lesions initially detected on MRI for new cancer or high-risk screening were included and outcomes determined by imaging, biopsy or tumor registry linkage. Variables were indication for MRI, age, lesion size, BI-RADS lesion type and kinetics. Associations with malignancy were assessed using generalized estimating equations and lesion probabilities of malignancy were calculated.
  
  
  RESULTS
  855 lesions (155 malignant, 700 benign) were included. Strongest associations with malignancy were for kinetics (washout versus persistent; OR 4.2, 95% CI 2.5-7.1) and clinical indication (new cancer versus high-risk screening; OR 3.0, 95% CI 1.7-5.1). Also significant were age > = 50 years, size > = 10 mm and lesion-type mass. The most predictive model (AUC 0.70) incorporated indication, size and kinetics. The highest probability of malignancy (41.1%) was for lesions on MRI for new cancer, > = 10 mm with washout. The lowest (1.2%) was for lesions on high-risk screening, <10 mm with persistent kinetics.
  
  
  CONCLUSIONS
  A multivariate model shows promise as a decision support tool in predicting malignancy for MRI-detected breast lesions.
authors.affiliation: Department of Radiology, University of Washington Medical Center, 1959 NE Pacific, Seattle, WA 98195, USA. wdemarti@uw.edu
authors.forename: Wendy B
authors.initials: WB
authors.lastname: Demartini
authors.forename: Brenda F
authors.initials: BF
authors.lastname: Kurland
authors.forename: Robert L
authors.initials: RL
authors.lastname: Gutierrez
authors.forename: C Craig
authors.initials: CC
authors.lastname: Blackmore
authors.forename: Sue
authors.initials: S
authors.lastname: Peacock
authors.forename: Constance D
authors.initials: CD
authors.lastname: Lehman
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media; C064925:gadodiamide; D019786:Gadolinium DTPA