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Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks¹

¹ From the Institute of Biomedical Engineering, National Taiwan University, 1, Section 1, Jen-Ai Rd, Taipei 100, Taiwan (C.M.C., K.C.H., G.S.H.); Department of Radiology, Division of Ultrasound, Taipei Veterans General Hospital and National Yang Ming University, Taiwan (Y.H.C., C.M.T., H.J.C.); and Department of Radiology, Division of Ultrasound, Taipei Veterans General Hospital (S.Y.C.). Received November 19, 2001; revision requested January 28, 2002; revision received May 17; accepted June 27. Supported by National Science Council grant NSC90-2213-E-002-103, Taiwan. Address correspondence to C.M.C. (e-mail: ming@lotus.mc.ntu.edu.tw).

PURPOSE: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions.

MATERIALS AND METHODS: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests.

RESULTS: The area under the receiver operating characteristic curve (A_z) was 0.952 ± 0.014 for the first set, 0.982 ± 0.004 for the first set as the training set and the second set as the prediction set, 0.954 ± 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 ± 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A_z value.

CONCLUSION: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.

© RSNA, 2003

Index terms: Breast neoplasms, diagnosis, 00.30 • Breast neoplasms, US, 00.1298 • Computers, diagnostic aid • Computers, neural network • Images, analysis

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