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Breast Masses: Computer-aided Diagnosis with Serial Mammograms¹

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., B.S., M.A.H., H.P.C., M.A.R., C.P., C.B., J.B., K.K., M.F., S.K.P., D.A., A.V.N., J.S.); and Center for Devices and Radiological Health, U.S. Food and Drug Administration, Rockville, Md (N.P.). Received December 10, 2004; revision requested February 3, 2005; revision received May 5; accepted June 13; final version accepted September 12. Supported by USAMRMC grants DAMD17-98-1-8211, DAMD17-02-1-0489, and DAMD17–02-1-0214 and USPHS grant CA95153. Address correspondence to L.H. (e-mail: lhadjisk{at}umich.edu).

Purpose: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign.

Materials and Methods: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study. Ninety temporal pairs of two-view serial mammograms (depicting 47 malignant and 43 benign biopsy-proved masses) were obtained from 68 patient files and were digitized. Biopsy was the reference standard. Eight Mammography Quality Standards Act of 1992–accredited radiologists and two breast imaging fellows assessed digitized two-view temporal pairs (in preselected regions of interest only) by estimating likelihood of malignancy and Breast Imaging Reporting and Data System (BI-RADS) category without and with CAD. Observers' rating data were analyzed with Dorfman-Berbaum-Metz (DBM) multireader multicase method. Statistical significance of differences was estimated with the DBM method and Student two-tailed paired t test.

Results: Average area under the receiver operating characteristic curve for likelihood of malignancy across the 10 observers was 0.83 (range, 0.74–0.88) without CAD and improved to 0.87 (range, 0.80–0.92) with CAD (P < .05). The average partial area index above a sensitivity of 0.90 for likelihood of malignancy was 0.35 (range, 0.13–0.54) without CAD and 0.49 (range, 0.18–0.73) with CAD—a nonsignificant improvement (P = .11). For BI-RADS assessment, it was estimated that with CAD, six radiologists would correctly recommend additional biopsies for malignant masses (range, 4.3%–10.6%) and five would correctly recommend reduction of biopsy (ie, fewer biopsies) for benign masses (range, 2.3%–9.3%). However, five radiologists would incorrectly recommend additional biopsy for benign masses (range, 2.3%–14.0%), and one would incorrectly recommend reduction of biopsy (4.3%).

Conclusion: CAD involving interval change analysis of preselected regions of interest can significantly improve radiologists' accuracy in classifying masses on digitized screen-film mammograms as malignant or benign.

© RSNA, 2006