title: Automated computerized classification of malignant and benign masses on digitized mammograms.
abstract: OBJECTIVE
  To develop a method for differentiating malignant from benign masses in which a computer automatically extracts lesion features and merges them into an estimated likelihood of malignancy.
  
  
  METHODS
  Ninety-five mammograms depicting masses in 65 patients were digitized. Various features related to the margin and density of each mass were extracted automatically from the neighborhoods of the computer-identified mass regions. Selected features were merged into an estimated likelihood of malignancy by, using three different automated classifiers. The performance of the three classifiers in distinguishing between benign and malignant masses was evaluated by receiver operating characteristic analysis and compared with the performance of an experienced mammographer and that of five less experienced mammographers.
  
  
  RESULTS
  Our computer classification scheme yielded an area under the receiver operating characteristic curve (Az) value of 0.94, which was similar to that for an experienced mammographer (Az = 0.91) and was statistically significantly higher than the average performance of the radiologists with less mammographic experience (Az = 0.81) (P = .013). With the database used, the computer scheme achieved, at 100% sensitivity, a positive predictive value of 83%, which was 12% higher than that for the performance of the experienced mammographer and 21% higher than that for the average performance of the less experienced mammographers (P < .0001).
  
  
  CONCLUSIONS
  Automated computerized classification schemes may be useful in helping radiologists distinguish between benign and malignant masses and thus reducing the number of unnecessary biopsies.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
authors.forename: Z
authors.initials: Z
authors.lastname: Huo
authors.forename: M L
authors.initials: ML
authors.lastname: Giger
authors.forename: C J
authors.initials: CJ
authors.lastname: Vyborny
authors.forename: D E
authors.initials: DE
authors.lastname: Wolverton
authors.forename: R A
authors.initials: RA
authors.lastname: Schmidt
authors.forename: K
authors.initials: K
authors.lastname: Doi
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008327:Mammography; D011237:Predictive Value of Tests; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity