Multi-class discrimination of lymphadenopathy by using dual-modal ultrasound radiomics with elastography and B-mode ultrasound.
10.7507/1001-5515.201807015
- Author:
Jie SHI
1
,
2
;
Jianwei JIANG
3
;
Wanying CHANG
3
;
Man CHEN
4
;
Qi ZHANG
1
,
5
Author Information
1. The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, P.R.China
2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, P.R.China.
3. Department of Medical Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, P.R.China.
4. Department of Medical Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, P.R.China.maggiech1221@126.com.
5. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, P.R.China.zhangq@t.shu.edu.cn.
- Publication Type:Journal Article
- Keywords:
dual-modal;
feature selection;
lymph node;
multi-class classification;
radiomics
- MeSH:
Elasticity Imaging Techniques;
Humans;
Lymph Nodes;
Lymphadenopathy;
Retrospective Studies;
Ultrasonography
- From:
Journal of Biomedical Engineering
2019;36(6):957-963
- CountryChina
- Language:Chinese
-
Abstract:
The purpose of our study is to evaluate the diagnostic performance of radiomics in multi-class discrimination of lymphadenopathy based on elastography and B-mode dual-modal ultrasound images. We retrospectively analyzed a total of 251 lymph nodes (89 benign lymph nodes, 70 lymphoma and 92 metastatic lymph nodes) from 248 patients, which were examined by both elastography and B-mode sonography. Firstly, radiomic features were extracted from multimodal ultrasound images, including shape features, intensity statistics features and gray-level co-occurrence matrix texture features. Secondly, three feature selection methods based on information theory were used on the radiomic features to select different subsets of radiomic features, consisting of conditional infomax feature extraction, conditional mutual information maximization, and double input symmetric relevance. Thirdly, the support vector machine classifier was performed for diagnosis of lymphadenopathy on each radiomic subsets. Finally, we fused the results from different modalities and different radiomic feature subsets with Adaboost to improve the performance of lymph node classification. The results showed that the accuracy and overall 1 score with five-fold cross-validation were 76.09%±1.41% and 75.88%±4.32%, respectively. Moreover, when considering on benign lymph nodes, lymphoma or metastatic lymph nodes respectively, the area under the receiver operating characteristic curve of multi-class classification were 0.77, 0.93 and 0.84, respectively. This study indicates that radiomic features derived from multimodal ultrasound images are benefit for diagnosis of lymphadenopathy. It is expected to be useful in clinical differentiation of lymph node diseases.
