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


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.


CONCLUSIONS
Masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis.
Markey
Mia K
MK
Department of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke University Medical Center, DUMC 3302, Durham, NC 27710, USA. markey@duke.edu
Lo
Joseph Y
JY
Floyd
Carey E
CE
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity