title: Expert learning system network for diagnosis of breast calcifications.
abstract: Breast calcification diagnosis was studied by using clinical findings and computerized image processing of a mammogram in a network of trained expert learning systems (Outcome Advisor [OA]). The system was tested with records not used for training and performance was compared with radiologist. The network was 72% accurate in classifying clusters of calcifications as malignant or benign over a set of test cases radiologists had considered "hard-to-diagnose calcifications," and referred for biopsy. The radiologists had decided to conduct biopsy by selecting an equal number of positive and negative cases for the test group; thus the radiologists' performance with respect to categories of benign versus malignant was constrained to be 50/50. Statistical analysis shows only a 2% probability that the observed accuracy of 72% was a chance performance in recognizing whether a cluster is benign or malignant. The feasibility of developing a network of OAs for diagnosing breast cancer integrating digital image processing of mammograms is promising.
authors.affiliation: Department of Electrical Engineering, University of Cincinnati, OH 45204.
authors.forename: E A
authors.initials: EA
authors.lastname: Patrick
authors.forename: M
authors.initials: M
authors.lastname: Moskowitz
authors.forename: V T
authors.initials: VT
authors.lastname: Mansukhani
authors.forename: E I
authors.initials: EI
authors.lastname: Gruenstein
mesh_terms: D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005103:Expert Systems; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted; D012984:Software