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Papers Published

1. MK Markey, JY Lo, CE Floyd, Differences between computer-aided diagnosis of breast masses and that of calcifications., Radiology, United States, vol. 223 no. 2 (May, 2002), pp. 489-93 .
   (last updated on 2006/02/06)

   Abstract:
   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.

   Keywords:
   Adult • Aged • Aged, 80 and over • Biopsy • Breast Neoplasms • Calcinosis • Diagnosis, Computer-Assisted* • Diagnosis, Differential • Discriminant Analysis • Female • Humans • Mammography • Middle Aged • Neural Networks (Computer) • ROC Curve • Sensitivity and Specificity • radiography*