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Differences between Computer-aided Diagnosis of Breast Masses and That of Calcifications¹

¹ From the Departments of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke University Medical Center, DUMC 3302, Durham, NC 27710. Received July 23, 2001; revision requested September 4; revision received October 12; accepted December 10. Supported in part by U.S. Public Health Service grants R29-CA75547, R21-CA092573, and R21-CA81309 awarded by the National Cancer Institute; Whitaker Foundation grants RG-97-0322 and SO-97-0035; U.S. Army Medical Research and Materiel Command grant DAMD17-99-1-9174 awarded by the U.S. Army; and Susan G. Komen Breast Cancer Foundation grants 9803 and BCTR2000730A. Address correspondence to M.K.M. (e-mail: markey@duke.edu).

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.

© RSNA, 2002

Index terms: Breast neoplasms, 00.31, 00.32 • Breast neoplasms, calcification, 00.81 • Breast neoplasms, diagnosis, 00.129 • Computers, diagnostic aid • Computers, neural network

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