title: Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography.
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.
authors.affiliation: Institute of Biomedical Engineering, Shanghai University, Shanghai, China. Electronic address: zhangq@shu.edu.cn.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.affiliation: Institute of Biomedical Engineering, Shanghai University, Shanghai, China.
authors.forename: Jingfeng
authors.initials: J
authors.lastname: Suo
authors.affiliation: Institute of Biomedical Engineering, Shanghai University, Shanghai, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Shi
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, China.
authors.forename: Jinhua
authors.initials: J
authors.lastname: Yu
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
mesh_terms: D001940:Breast / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*
keywords: Breast tumor; Classification; Feature selection; Hierarchical clustering; Radiomics; Sonoelastography