کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5485961 1399443 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم آکوستیک و فرا صوت
پیش نمایش صفحه اول مقاله
Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ultrasound in Medicine & Biology - Volume 43, Issue 5, May 2017, Pages 1058-1069
نویسندگان
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