ASIA unversity:Item 310904400/8686
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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8686


    Title: Analysis of Elastographic and B-mode Features at Sonoelastography for Breast Tumor Classification
    Authors: Moon, Woo Kyung;Huang, Chiun-Sheng;Shen, Wei-Chih;Takada, Etsuo;Chang, Ruey-Feng;Joe, Juliwati;Nakajima, Michiko;Kobayashi, Masayuki
    Contributors: Department of Computer Science and Information Engineering
    Keywords: coustic waves;Electric network analysis;Oncology;Tumors;Ultrasonics;Average gradient;BI-RADS;Breast tumor;Elastography;Gradient variance;Malignant tumors;Manual segmentation;Mean ages;Neural network analysis;Receiver operating characteristic curve analysis;ROC curves;Solid breast mass;Sonoelastography;Statistical analysis;Strain ratios;T-test
    Date: 2009-11
    Issue Date: 2010-04-07 13:27:21 (UTC+0)
    Publisher: Asia University
    Abstract: The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24-75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student's t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features- mean (0.87), median (0.86) and mode (0.83)-were significantly higher than the Az values for the six B-mode features (0.54-0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors. (E-mail: [email protected]). © 2009 World Federation for Ultrasound in Medicine & Biology.
    Relation: Ultrasound in Medicine and Biology 35(11):1794-1802
    Appears in Collections:[Department of Computer Science and Information Engineering] Journal Artical

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