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Improvement in Radiologists’ Characterization of Malignant and Benign Breast Masses on Serial Mammograms with Computer-aided Diagnosis: An ROC Study¹

¹ From the Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0904 (L.H., H.P.C., B.S., M.A.H., M.A.R., C.B., C.P., J.B., K.K., M.F., S.P., D.A., A.N., J.S.); and Center for Devices and Radiological Health, U.S. Food and Drug Administration, Rockville, Md (N.P.). From the 2002 RSNA scientific assembly. Received March 17, 2003; revision requested June 13; final revision received January 9, 2004; accepted February 4. Supported by USAMRMC grants DAMD17–98-1–8211, DAMD17–02-1–0489, and DAMD17–02-1–0214. Address correspondence to L.H. (e-mail: lhadjisk@umich.edu).

PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists’ characterization of masses on serial mammograms.

MATERIALS AND METHODS: Two hundred fifty-three temporal image pairs (138 malignant and 115 benign) obtained from 96 patients who had masses on serial mammograms were evaluated. The temporal pairs were formed by matching masses of the same view from two different examinations. Eight radiologists and two breast imaging fellows assessed the temporal pairs with and without computer aid. The classification of accuracy was quantified by using the area under receiver operating characteristic curve (A_z). The statistical significance of the difference in A_z between the different reading conditions was estimated with the Dorfman-Berbaum-Metz method for analysis of multireader multicase data and with the Student paired t test for analysis of observer-specific paired data.

RESULTS: The average A_z for radiologists’ estimates of the likelihood of malignancy was 0.79 without CAD and improved to 0.84 with CAD. The improvement was statistically significant (P = .005). The corresponding average partial area index was 0.25 without CAD and improved to 0.37 with CAD. The improvement was also statistically significant (P = .005). On the basis of Breast Imaging Reporting and Data System assessments, it was estimated that with CAD, each radiologist, on average, reduced 0.7% (0.8 of 115) of unnecessary biopsies and correctly recommended 5.7% (7.8 of 138) of additional biopsies.

CONCLUSION: CAD based on analysis of interval changes can significantly increase radiologists’ accuracy in classification of masses and thereby may be useful in improving correct biopsy recommendations.

© RSNA, 2004

Index terms: Breast neoplasms, diagnosis, 00.31, 00.32 • Computers, diagnostic aid • Diagnostic radiology, observer performance

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