Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks
Resource
Radiology 226 (2): 504-514
Journal
Radiology
Journal Volume
226
Journal Issue
2
Pages
504-514
Date Issued
2003
Date
2003
Author(s)
Chou, Yi-Hong
Han, Ko-Chung
Hung, Guo-Shian
Tiu, Chui-Mei
Chiou, Hong-Jen
Chiou, See-Ying
Abstract
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,) 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 seta 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, 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.
Subjects
Breast neoplasms, diagnosis; Breast neoplasms, US; Computers, diagnostic aid; Computers, neural network; Images, analysis
SDGs
Other Subjects
adolescent; adult; aged; algorithm; area under the curve; article; artificial neural network; breast cancer; breast lesion; computer aided diagnosis; controlled study; diagnostic procedure; echography; female; human; major clinical study; morphology; priority journal; Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Breast Neoplasms; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Humans; Middle Aged; Neural Networks (Computer); Reproducibility of Results; ROC Curve; Ultrasonography, Mammary
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