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


Journal Article

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: Masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis.

Full Text

Duke Authors

Cited Authors

  • Markey, MK; Lo, JY; Floyd, CE

Published Date

  • May 2002

Published In

Volume / Issue

  • 223 / 2

Start / End Page

  • 489 - 493

PubMed ID

  • 11997558

Pubmed Central ID

  • 11997558

International Standard Serial Number (ISSN)

  • 0033-8419

Digital Object Identifier (DOI)

  • 10.1148/radiol.2232011257


  • eng

Conference Location

  • United States