Chung-Ming LoYi-Chen LaiYi-Hong ChouRUEY-FENG CHANG2018-09-102018-09-10201501692607http://scholars.lib.ntu.edu.tw/handle/123456789/391972https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947611836&doi=10.1016%2fj.cmpb.2015.09.004&partnerID=40&md5=9afcb00057e3647a7914091e9fb4662fBackground and objectives: A computer-aided diagnosis (CAD) system based on the quantified color distributions in shear-wave elastography (SWE) was developed to evaluate the malignancies of breast tumors. Methods: For 57 benign and 31 malignant tumors, 18 SWE features were extracted from regions of interest (ROI), including the tumor and peritumoral areas. In the ROI, a histogram in each color channel was described using moments such as the mean, variance, skewness, and kurtosis. Moreover, three color channels were combined as a vector to evaluate tissue elasticity. The SWE features were then combined in a logistic regression classifier for breast tumor classification. Results: The performance of the CAD system achieved an accuracy of 81%. Combining the CAD system with a BI-RADS assessment obtained an Az improvement from 0.77 to 0.89 (p-value <0.05). Conclusions: The combination of the proposed CAD system based on SWE features and the BI-RADS assessment would provide a promising diagnostic suggestion. ? 2015 Elsevier Ireland Ltd.Breast cancer; Computer-aided diagnosis; Histogram moment; Shear-wave elastography; Vector quantification[SDGs]SDG3Color; Graphic methods; Higher order statistics; Logistic regression; Medical imaging; Shear flow; Shear waves; Tumors; Breast Cancer; Breast tumor classifications; Computer Aided Diagnosis(CAD); Histogram moments; Logistic regression classifier; Regions of interest; Shear wave elastography; Shear wave imaging; Computer aided diagnosis; adult; aged; Article; benign tumor; breast cancer; breast lesion; cancer diagnosis; clinical feature; color; diagnostic accuracy; diagnostic test accuracy study; disease classification; elasticity; elastography; histogram; human; major clinical study; quantitative analysis; sensitivity and specificity; shear wave elastography; breast; breast tumor; classification; computer assisted diagnosis; echomammography; elastography; female; pathology; procedures; Breast; Breast Neoplasms; Diagnosis, Computer-Assisted; Elasticity Imaging Techniques; Female; Humans; Image Interpretation, Computer-Assisted; Ultrasonography, Mammaryjournal article10.1016/j.cmpb.2015.09.004264216962-s2.0-84947611836