Feasibility of ultrasound radiomics-based models for classification of hepatic echinococcosis
10.16250/j.32.1374.2022202
- VernacularTitle:基于超声影像组学建立肝棘球蚴病分型模型的 可行性研究
- Author:
Xu-hui ZHANG
1
,
2
;
La-mu SUOLANG
2
,
3
;
Jia-jun QIU
4
;
Jing-wen JIANG
4
;
Jin YIN
4
;
Jun-ren WANG
4
;
Yi-fei WANG
1
;
Yong-zhong LI
1
;
Di-ming CAI
1
Author Information
1. Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
2. Co-first authors
3. Tibet Autonomous Region Center for Disease Control and Prevention, China
4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, China
- Publication Type:Journal Article
- Keywords:
Hepatic echinococcosis;
Classification;
Ultrasonographic image;
Radiomics;
Machine learning
- From:
Chinese Journal of Schistosomiasis Control
2022;34(5):500-506
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the feasibility of establishment of ultrasound radiomics-based models for classification of hepatic echinococcosis, so as to provide insights into precision ultrasound diagnosis of hepatic echinococcosis. Methods The ultrasonographic images were retrospectively collected from 200 patients with hepatic echinococcosis in Shiqu County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province in October 2014, and the regions of interest were plotted in ultrasonographic images of hepatic echinococcosis lesions. The ultrasound radiomics features of hepatic echinococcosis were extracted with 25 methods, and screened using pre-selection and the least absolute shrinkage and selection operator. Then, all ultrasonographic images were randomly assigned into the training and independent test sets according to the type of lesions at a ratio of 7:3. Machine learning models for classification of hepatic echinococcosis were created based on two classifiers, including kernel logistic regression (KLR) and medium Gaussian support vector machine (MGSVM). The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity and areas under the curves (AUC) of the created machine learning models for classification of hepatic echinococcosis were calculated. Results A total of 5 005 ultrasound radiomics features were extracted from 200 patients with hepatic echinococcosis using 25 methods, and 36 optimal radiomics features were screened through feature selection, based on which two machine learning models were created, including KLR and MGSVM. ROC curve analysis showed that MGS-VM presented a higher efficacy for hepatic echinococcosis classification than KLR in the training set, with a sensitivity of 0.82, a specificity of 0.78 and AUC of 0.88, while KLR presented a higher efficacy for hepatic echinococcosis classification than MGSVM in the independent test set, with a sensitivity of 0.82, a specificity of 0.72 and AUC of 0.86, respectively. Conclusions Ultrasound radiomics-based machine learning models are feasible for hepatic echinococcosis classification.
