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Classification of Breast Lesions with Multimodality Computer-aided Diagnosis: Observer Study Results on an Independent Clinical Data Set¹

Karla Horsch, PhD, Maryellen L. Giger, PhD, Carl J. Vyborny, MD, PhD, Li Lan, MS, Ellen B. Mendelson, MD and R. Edward Hendrick, PhD

¹ From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637. From the 2002 RSNA Annual Meeting. Received February 23, 2005; revision requested April 21; revision received June 22; accepted July 18; final version accepted September 1. Supported in part by grants from the U.S. Army Medical Research and Material Command (DAMD 17-98-18194) and USPHS grants CA89452, RR11459, and T32 CA09649. Address correspondence to M.L.G. (e-mail: m-giger{at}uchicago.edu).

Purpose: To evaluate a computer-aided diagnosis multimodality intelligent workstation as an aid to radiologists in the interpretation of mammograms and breast sonograms.

Materials and Methods: An institutional review board approved the protocol for an observer study with signed consent, as well as the retrospective use of the mammograms, sonograms, and clinical data with waiver of consent. The HIPAA-compliant observer study was conducted with five breast radiologists and five breast imaging fellows, all of whom gave confidence ratings and patient management decisions, both without and with the computer aid, for 97 lesions that were unknown to both the observers and the computer. The performance of each observer without and with the computer aid was quantified by using four performance measures: area under the receiver operating characteristic curve (A_z) value, partial A_z value, sensitivity, and specificity. The statistical significance of the differences in the performance measures without and with the computer aid was determined by using a two-tailed t test for paired data.

Results: Use of the computer aid resulted in an improvement of the average performance of the 10 observers, as measured by means of a statistically significant increase in A_z value (0.87–0.92; P < .001), partial A_z value (0.47–0.68; P < .001), and sensitivity (0.88–0.93; P = .005). A statistically significant difference was not found in the specificity without and with the computer aid (0.66–0.69; P = .20).

Conclusion: Use of multimodality intelligent workstations can improve the performance of radiologists in the task of differentiating malignant and benign lesions at mammography and sonography.

© RSNA, 2006

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