Radiomics Based Prediction of Symptomatic Carotid Plaque in MR Images

The purpose of this study is to analyze clinical features and radiomics features obtained from MR images of the carotid arteries to evaluate the accuracy of predicting whether asymptomatic plaque would migrate to symptomatic plaque. Using the extracted radiomics features and clinical features, and machine learning algorithms are used to predict symptomatic migration. The machine learning algorithms used are SVM, Logistic regression, LightGBM, and Random Forest. Their performances are evaluated. Prediction by clinical features alone was AUC 0.709, and a combination of clinical and radiomics features was AUC 0.744 respectively. LightGBM showed the best accuracy. The combined features model showed effectiveness in predicting carotid symptomatology migration.