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Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy¹

¹ From the Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0904. From the 2003 RSNA Annual Meeting. Received August 31, 2005; revision requested November 3; revision received March 9, 2006; accepted April 4; final version accepted June 6. Supported in part by U.S. Army Medical Research Materiel Command grant DAMD17-01-1-0328 and by U.S. Public Health Service grants CA095153 and CA091713. Address correspondence to B.S. (e-mail: berki{at}umich.edu).

Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard.

Materials and Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25–86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve.

Results: Without CAD, the five radiologists had an average area under the ROC curve (A_z) of 0.83 (range, 0.81–0.87). With CAD, the average A_z increased significantly (P = .006) to 0.90 (range, 0.86–0.93). When a 2% likelihood of malignancy was used as the threshold for biopsy recommendation, the average sensitivity of radiologists increased from 96% to 98% with CAD, while the average specificity for this data set decreased from 22% to 19%. If a biopsy recommendation threshold could be chosen such that sensitivity would be maintained at 96%, specificity would increase to 45% with CAD.

Conclusion: Use of a computer algorithm may improve radiologists' accuracy in distinguishing malignant from benign breast masses on 3D US volumetric images.

Supplemental material: http://radiology.rsnajnls.org/cgi/content/full/2423051464/DC1

© RSNA, 2007

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