title: Computer-aided classification of BI-RADS category 3 breast lesions.
abstract: OBJECTIVE
  To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation.
  
  
  METHODS
  A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least two of four radiologists. The CAC system automatically extracted from the digitized mammograms quantitative features that characterized the lesions. The system then used a classification scheme to score the lesions by the likelihood of their malignancy on the basis of these features. The classification scheme was trained with 646 pathologically proved cases (323 malignant), and the results were tested with receiver operating characteristic (ROC) analysis by using the jackknife method. Sensitivity, specificity, positive predictive value, and accuracy were calculated. Category 3 lesions were stratified among BI-RADS categories 2-5 according to CAC-assigned lesion score, and this classification was compared with the results of pathologic analysis.
  
  
  RESULTS
  Jackknife analysis of CAC results in the training data set yielded a sensitivity of 94%, specificity of 78%, positive predictive value of 81%, and area under the ROC curve of 0.90. Of the 42 malignant lesions that had been classified at conventional interpretation as probably benign, nine were assigned by the CAC system to BI-RADS category 4, and 29 were assigned to category 5. The CAC system correctly upgraded the BI-RADS classification of these 38 lesions (sensitivity, 90%) and incorrectly upgraded the classification of only 20 benign lesions (specificity, 69%).
  
  
  CONCLUSIONS
  The CAC system scored 38 of the 42 malignant lesions initially assigned to BI-RADS category 3 as BI-RADS category 4 or 5, and thus correctly upgraded the category in 90% of these lesions.
authors.affiliation: Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, USA. sbuchbinder@siuh.edu
authors.forename: Shalom S
authors.initials: SS
authors.lastname: Buchbinder
authors.forename: Isaac S
authors.initials: IS
authors.lastname: Leichter
authors.forename: Richard B
authors.initials: RB
authors.lastname: Lederman
authors.forename: Boris
authors.initials: B
authors.lastname: Novak
authors.forename: Philippe N
authors.initials: PN
authors.lastname: Bamberger
authors.forename: Miryam
authors.initials: M
authors.lastname: Sklair-Levy
authors.forename: Gail
authors.initials: G
authors.lastname: Yarmish
authors.forename: Scott I
authors.initials: SI
authors.lastname: Fields
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D005260:Female; D005348:Fibrocystic Breast Disease / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D011230:Precancerous Conditions / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted; D011873:Radiology Information Systems; D012189:Retrospective Studies; D012680:Sensitivity and Specificity