Perihematomal edema in basal ganglia intracerebral hemorrhage by using radiomics approach of CT images

Abstract

Objective \nTo explore the value of CT images in distinguishing perihematomal edema in basal ganglia intracerebral hemorrhage with normal brain tissue, and its significance in assessing patients conditions and prognoses. \n \n \nMethods \nCT images and clinical data of 120 patients with basal ganglia intracerebral hemorrhage admitted to our hospital from January 2017 to September 2018 were collected, and these 120 patients were randomly assigned to group of training data set (n=90) and group of test data set (n=30) at a ratio of 3:1. The texture analysis software Mazda was used to preprocess the CT images and manually sketch the regions of interest (ROIs) to extract the texture parameters in patients from the group of training data set; Mazda software provides texture feature selection methods including mutual information (MI), Fisher coefficients (Fisher), classification error probability combined with average correlation coefficients (POE+ACC), and texture feature analysis including raw data analysis (RDA), principal component analysis (PCA), linear classification analysis (LDA) and nonlinear classification analysis (NDA); texture feature selection methods and texture feature analysis were grouped by pairs to establish different image omics labels; the error rate was used to evaluate the performance of different labels. Random forest model, support vector machine model and neural network model were built for texture parameters in patients from the group of test data set, and texture parameters extracted from patients from group of training data set were imported into these models; receiver operating characteristics curve was used to assess the performance of models. According to the maximum diameter of the hematomas, Glasgow coma scale (GCS) scores at admission, median of National Institute of Health Stroke Scale (NIHSS) scores 3 months after follow up, all patients were divided into two groups; Mazda software was used repeatedly for dimension reduction and establishment of different images omics labels; the sum of error rates from the two groups was taken as total error rate to evaluate the significance of different labels in predicting patients conditions and prognoses. \n \n \nResults \nA total of 295 texture parameters were extracted from the ROIs of the best CT images of 90 patients from group of training data set, and 10 characteristic texture parameters were obtained by each of the three dimensionality reduction methods. Among all texture post-processing methods, the lowest error rate was 2.22% for POE+ACC/NDA; AUCs were 0.87 (95% CI: 0.76-0.97), 0.81 (95% CI: 0.72-0.93) and 0.76 (95%CI: 0.67-0.89) for random forest model, support vector machine model and neural network model in the test dataset, respectively, which indicated that random forest model had the best forecast performance. The imaging omics labels established based on POE+ACC/NDA had the lowest total error rate for analysis of maximum diameter of hematoma and GCS scores at admission (26.66%, 23.33%); the imaging omics labels established based on Fisher s coefficient method and NDA had the lowest total error rate (33.33%) for analysis of NIHSS scores at 3 months of follow up. \n \n \nConclusion \nRadiomic method with proper model is of certain value in distinguishing erihematomal edema in basal ganglia intracerebral hemorrhage with normal brain tissue, and also has certain significance in evaluating the patient s conditions and prognoses. \n \n \nKey words: \nIntracerebral hemorrhage;\xa0Basal ganglia;\xa0Edema;\xa0CT radiomics;\xa0Artificial intelligence