Mia K Markey1, Joseph Y Lo, Carey E Floyd. 1. Department of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke University Medical Center, DUMC 3302, Durham, NC 27710, USA. markey@duke.edu
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. Copyright RSNA, 2002
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. Copyright RSNA, 2002
Authors: Berkman Sahiner; Heang-Ping Chan; Lubomir M Hadjiiski; Marilyn A Roubidoux; Chintana Paramagul; Janet E Bailey; Alexis V Nees; Caroline E Blane; Dorit D Adler; Stephanie K Patterson; Katherine A Klein; Renee W Pinsky; Mark A Helvie Journal: Acad Radiol Date: 2009-04-17 Impact factor: 3.173
Authors: Jagpreet Chhatwal; Oguzhan Alagoz; Mary J Lindstrom; Charles E Kahn; Katherine A Shaffer; Elizabeth S Burnside Journal: AJR Am J Roentgenol Date: 2009-04 Impact factor: 3.959