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Breast Mass Lesions: Computer-aided Diagnosis Models with Mammographic and Sonographic Descriptors¹

¹ From the Department of Biomedical Engineering (J.L.J., J.Y.L.) and Duke Advanced Imaging Labs, Department of Radiology (J.L.J., J.Y.L., J.A.B.), Duke University Medical Center, 2424 Erwin Rd, Suite 302, Durham, NC 27705. Received April 23, 2006; revision requested June 23; revision received July 24; accepted August 29; final version accepted November 15. Supported by U.S. Army Breast Cancer Research Program W81XWH-05-1-0292 and DAMD17-02-1-0373, and NIH/NCI R01 CA95061 and R21 CA93461. Address correspondence to J.L.J. (e-mail: jonathan.jesneck{at}duke.edu).

Purpose: To retrospectively develop and evaluate computer-aided diagnosis (CAD) models that include both mammographic and sonographic descriptors.

Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study. A waiver of informed consent was obtained. Mammographic and sonographic examinations were performed in 737 patients (age range, 17–87 years), which yielded 803 breast mass lesions (296 malignant, 507 benign). Radiologist-interpreted features from mammograms and sonograms were used as input features for linear discriminant analysis (LDA) and artificial neural network (ANN) models to differentiate benign from malignant lesions. An LDA with all the features was compared with an LDA with only stepwise-selected features. Classification performances were quantified by using receiver operating characteristic (ROC) analysis and were evaluated in a train, validate, and retest scheme. On the retest set, both LDAs were compared with radiologist assessment score of malignancy.

Results: Both the LDA and ANN achieved high classification performance with cross validation (area under the ROC curve [A_z] = 0.92 ± 0.01 [standard deviation] and _0.90A_z = 0.54 ± 0.08 for LDA, A_z = 0.92 ± 0.01 and _0.90A_z = 0.55 ± 0.08 for ANN). Results of both models generalized well to the retest set, with no significant performance differences between the validate and retest sets (P > .1). On the retest set, there were no significant performance differences between LDA with all features and LDA with only the stepwise-selected features (P > .3) and between either LDA and radiologist assessment score (P > .2).

Conclusion: Results showed that combining mammographic and sonographic descriptors in a CAD model can result in high classification and generalization performance. On the retest set, LDA performance matched radiologist classification performance.

© RSNA, 2007