title: Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
abstract: The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637.
authors.forename: Y
authors.initials: Y
authors.lastname: Wu
authors.forename: M L
authors.initials: ML
authors.lastname: Giger
authors.forename: K
authors.initials: K
authors.lastname: Doi
authors.forename: C J
authors.initials: CJ
authors.lastname: Vyborny
authors.forename: R A
authors.initials: RA
authors.lastname: Schmidt
authors.forename: C E
authors.initials: CE
authors.lastname: Metz
mesh_terms: D005260:Female; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer; D015588:Observer Variation; D011237:Predictive Value of Tests; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D011871:Radiology; D012680:Sensitivity and Specificity