Differences between computer-aided diagnosis of breast masses and that of calcifications

• Published in: Radiology Vol. 223; no. 2; p. 489
• Main Authors: Markey, Mia K, Lo, Joseph Y, Floyd, Jr, Carey E
• Format: Journal Article
• Language: English
• Published: United States 01.05.2002
• Subjects: Adult; Aged; Aged, 80 and over; Biopsy; Breast Neoplasms - diagnostic imaging; Calcinosis - diagnostic imaging; Diagnosis, Computer-Assisted; Diagnosis, Differential; Discriminant Analysis; Female; Humans; Mammography; Middle Aged; Neural Networks (Computer); ROC Curve; Sensitivity and Specificity
• ISSN: 0033-8419
• DOI: 10.1148/radiol.2232011257

To compare the performance of a computer-aided diagnosis (CAD) system for diagnosis of previously detected lesions, based on radiologist-extracted findings on masses and calcifications. A feed-forward, back-propagation artificial neural network (BP-ANN) was trained in a round-robin (leave-one-out) manner to predict biopsy outcome from mammographic findings (according to the Breast Imaging Reporting and Data System) and patient age. The BP-ANN was trained by using a large (>1,000 cases) heterogeneous data set containing masses and microcalcifications. The performances of the BP-ANN on masses and microcalcifications were compared with use of receiver operating characteristic analysis and a z test for uncorrelated samples. The BP-ANN performed significantly better on masses than microcalcifications in terms of both the area under the receiver operating characteristic curve and the partial receiver operating characteristic area index. A similar difference in performance was observed with a second model (linear discriminant analysis) and also with a second data set from a similar institution. Masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis.