Differences between computer-aided diagnosis of breast masses and that of calcifications.
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.
Duke Scholars
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Related Subject Headings
- Sensitivity and Specificity
- ROC Curve
- Nuclear Medicine & Medical Imaging
- Neural Networks, Computer
- Middle Aged
- Mammography
- Humans
- Female
- Discriminant Analysis
- Diagnosis, Differential
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Sensitivity and Specificity
- ROC Curve
- Nuclear Medicine & Medical Imaging
- Neural Networks, Computer
- Middle Aged
- Mammography
- Humans
- Female
- Discriminant Analysis
- Diagnosis, Differential