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Differences between computer-aided diagnosis of breast masses and that of calcifications
Mia K Markey1, Joseph Y Lo, Carey E Floyd
1Department of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke University Medical Center, DUMC 3302, Durham, NC 27710, USA. 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.