Computerized Tomography Radiomics Features Analysis for Evaluation of Perihematomal Edema in Basal Ganglia Hemorrhage. Issue 8 (November 2019)

• Record Type: Journal Article
• Authors: Yao, Xiang; Liao, Lishang; Han, Yuxiao; Wei, Ting; Wu, Hai; Wang, Yiying; Li, Yanfei; Zhang, Xinyuan; Ren, Ke
• Is Part Of: Journal of craniofacial surgery. Volume 30:Issue 8(2019)
• Publication Date: 2019-11
• Subjects: Artificial intelligence -- basal ganglia -- computed tomography -- edema -- hemorrhage -- radiomics; Facial bones -- Surgery -- Periodicals; Skull -- Surgery -- Periodicals; Face -- Surgery -- Periodicals; Surgery, Plastic -- Periodicals; 617.52
• DOI: 10.1097/SCS.0000000000005765
• Languages: English
• ISSNs: 1049-2275

Abstract

Abstract : Abstract: To evaluate the edema area around basal ganglia hemorrhage by the application of computerized tomography (CT)-based radiomics as a prognostic factor and improve the diagnosis efficacy, a total of 120 patients with basal ganglia hemorrhage were analyzed retrospectively. The texture analysis software Mazda 3.3 was used to preprocess the CT images and manually sketch the region of interest to extract the texture features. The extracted texture features were selected by Fisher coefficient, POE+ACC and mutual information. The texture discriminant analysis uses the B11 module in the Mazda 3.3 software. The data were randomly divided into a training dataset (67%) and test dataset (33%). To further study the texture features, the training dataset can be divided into groups according to the median of GCS score, NIHSS score, and maximum diameter of hematoma. Random forest model, support vector machine model, and neural network model were built. AUC of the receiver operating characteristics curve was used to assess the performance of models with test dataset. Among all texture post-processing methods, the lowest error rate was 2.22% for the POE+ACC/nonlinear discriminant. For the maximum diameter of hematoma, GCS score, and NIHSS score group, the lowest error rate were 26.66%, 23.33%, and 30.00%, respectively. The values of AUCs were 0.87, 0.81, and 0.76, for random forest model, support vector machine model, and neural network model in the test dataset, respectively. Radiomic method with proper model may have a potential role in predicting the edema area around basal ganglia hemorrhage. It can be used as a secondary group in the diagnosis of edema area around basal ganglia hemorrhage. Abstract : Supplemental Digital Content is available in the text