Mia K. Markey, BS Joseph Y. Lo, PhD Carey E. Floyd, Jr, PhD Index terms: Breast neoplasms, 00.31, 00.32 Breast neoplasms, calcification, 00.81 Breast neoplasms, diagnosis, 00.129 Computers, diagnostic aid Computers, neural network Published online before print 10.1148/radiol.2232011257 Radiology 2002; 223:489 – 493 Abbreviations: A z = area under ROC curve BI-RADS = Breast Imaging Reporting and Data System BP-ANN = back-propagation artificial neural network CAD = computer-aided diagnosis LDA = linear discriminant analysis PPV = positive predictive value ROC = receiver operating characteristic 1 From the Departments of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke Uni- versity Medical Center, DUMC 3302, Durham, NC 27710. Received July 23, 2001; revision requested September 4; revision received October 12; ac- cepted 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 Re- search 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 corre- spondence to M.K.M. (e-mail: markey @duke.edu). © RSNA, 2002 Author contributions: Guarantor of integrity of entire study, M.K.M.; study concepts and design, M.K.M., J.Y.L., C.E.F.; literature re- search, M.K.M., J.Y.L.; experimental studies, M.K.M., J.Y.L., C.E.F.; data ac- quisition and analysis/interpretation, M.K.M., J.Y.L., C.E.F.; statistical analy- sis, M.K.M., J.Y.L., C.E.F.; manuscript preparation, definition of intellectual content, editing, revision/review, and final version approval, M.K.M., J.Y.L., C.E.F. Differences between Computer-aided Diagnosis of Breast Masses and That of Calcifications 1 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 microcalcifica- tions. 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 microcalci- fications 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 Among American women, breast cancer is the most common cancer and is the second leading cause of cancer deaths (1). Women in the United States have about a 1 in 8 lifetime risk of developing invasive breast cancer (2,3). Mammographic screening has been shown to reduce the mortality of breast cancer by as much as 30% (4,5). However, mammography has a low positive predictive value (PPV). Approximately 35% or less of women who undergo biopsy for histopathologic diagnosis of breast cancer are found to have malig- nancies (6). One goal of the application of computer-aided diagnosis (CAD) to mammog- raphy is to reduce the false-positive rate. Avoiding benign biopsies spares women unnec- essary discomfort, anxiety, and expense. CAD of breast cancer is the application of computational techniques to the problem of interpreting breast images, usually mammograms (7–9). There are two major topics in breast cancer CAD: detection of mammographic lesions and diagnosis of cancer from identified lesions. In the detection task, the goal is to assist a radiologist in the identifi- cation, and often the localization, of lesion-containing regions of mammograms. In the diagnosis task, the goal is to assist a radiologist in determining whether an identified breast lesion is an indication of cancer. This study focused on the diagnosis of breast lesions that had already been identified by radiologists as suspicious enough to warrant biopsy. In other words, these cases are generally considered indeterminate and more challenging, and any reduction in the number of benign biopsies represents an improvement over the status quo, provided high sensitivity is maintained. Most breast biopsy is performed on lesions that manifest mammographically as either a mass or a cluster of microcalcifications (10). CAD systems for detection generally perform better on calcifications than on masses, as shown in two review articles (8,11) and a recent 489