Hypothesis: Using 3-dimensional (3-D) over 2-dimensional (2-D) ultrasonographic (US) images of the breast represents a potentially significant advantage for computer-aided diagnosis (CAD).
Background: Although conventional 2-D US images of the breast are increasingly used in surgical clinical practice, 3-D US imaging of the breast, a newly introduced technique, can offer more information than 2-D US images do.
Design: This study deals with a CAD method for use with the proposed 3-D US images of the breast and compares its performance with conventional 2-D US versions.
Methods: The test databases included 3-D US images of 107 benign and 54 malignant breast tumors for a total of 161 US images. All solid nodules at US belong to categories above C3 (ie, probably benign). The 3-D US imaging was performed using a scanner (Voluson 530; Kretz Technik, Zipf, Austria). New 3-D autocorrelation coefficients extended from the traditional 2-D autocorrelations were developed to extract the texture characteristics of the 3-D US images. The extracted texture features of the 3-D US images were used to classify the tumor as benign or malignant using the neural network.
Results: At the receiver operating characteristic analysis, 3-D and 2-D autocorrelation calculating schemes yielded Az values (ie, area under the receiver operating characteristic curve) of 0.97 and 0.85 in distinguishing between benign and malignant lesions, respectively. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are statistically significantly improved using 3-D instead of 2-D US images for CAD.
Conclusions: The proposed system (for 3-D and 2-D CAD) is expected to be a useful computer-aided diagnostic tool for classifying benign and malignant tumors on ultrasonograms and can provide a second reading to help reduce misdiagnosis. Findings from this study suggest that using 3-D over 2-D US images for CAD represents a potentially significant advantage.