Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5485961 | Ultrasound in Medicine & Biology | 2017 | 12 Pages |
Abstract
A radiomics approach to sonoelastography, called “sonoelastomics,” is proposed for classification of benign and malignant breast tumors. From sonoelastograms of breast tumors, a high-throughput 364-dimensional feature set was calculated consisting of shape features, intensity statistics, gray-level co-occurrence matrix texture features and contourlet texture features, which quantified the shape, hardness and hardness heterogeneity of a tumor. The high-throughput features were then selected for feature reduction using hierarchical clustering and three-feature selection metrics. For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, an accuracy of 88.0%, a sensitivity of 85.7% and a specificity of 89.3% in a validation set via the leave-one-out cross-validation, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features. The sonoelastomic features are valuable in breast tumor differentiation.
Keywords
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Acoustics and Ultrasonics
Authors
Qi Zhang, Yang Xiao, Jingfeng Suo, Jun Shi, Jinhua Yu, Yi Guo, Yuanyuan Wang, Hairong Zheng,