NDLI: Quantitative analysis of peri-tumor tissue elasticity based on shear-wave elastography for breast tumor classification

Content Provider	IEEE Xplore Digital Library
Author	Yang Xiao Jie Zeng Ming Qian Rongqin Zheng Hairong Zheng
Copyright Year	2013
Description	Author affiliation: Paul C. Lauterbur Res. Center for Biomed. imaging, Shenzhen Inst. of Adv. Technol., Shenzhen, China (Yang Xiao; Hairong Zheng) \|\| Dept. of Med. Ultrasonics, Sun Yat-Sen Univ., Guangzhou, China (Jie Zeng; Rongqin Zheng)
Abstract	For shear-wave elastography (SWE) images, the most common site of tumor-associated stiffness is generally in the surrounding stroma rather than the tumor itself. The aim of this study is to assess the value of the peri-tumor tissue elasticity in the classification of breast tumors. SWE images of 106 breast tumors (65 benign, 41 malignant) were collected from 82 consecutive patients. By applying the image processing method, 5 elastographic features of the peri-tumor area (elasticity modulus mean, maximum, standard deviation, hardness degree and elasticity ratio) were computed to represent peri-tumor tissue elasticity. B-mode Breast Imaging Reporting and Data System (BI-RADS) were used for comparing the diagnostic performances between the grayscale US and color SWE images. Histopathologic results were used as the reference standard. The t-test, point biserial correlation coefficient and receiver operating characteristic (ROC) curve analysis were performed for statistical analysis. As a result, the Az values (area under ROC curve) were 0.92, 0.95, 0.94, 0.91, and 0.98 for the classifiers using the five elastographic features respectively, and 0.91 for BI-RADS assessment. The results showed that the peri-tumor tissue elasticity could provide valuable information for breast tumor classification.
Starting Page	1128
Ending Page	1131
File Size	761373
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609704
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Elasticity Image color analysis Cancer Breast tumors Image segmentation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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