AUTHOR=Han Na , Hu Wanjun , Ma Yurong , Zheng Yu , Yue Songhong , Ma Laiyang , Li Jie , Zhang Jing 

TITLE=A clinical-radiomics combined model based on carotid atherosclerotic plaque for prediction of ischemic stroke

JOURNAL=Frontiers in Neurology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1343423

DOI=10.3389/fneur.2024.1343423

ISSN=1664-2295

ABSTRACT=Abstract
Objectives: We established a radiomics model of carotid atherosclerotic plaque based high-resolution vessel wall magnetic resonance imaging (HR-VWMRI) and combined with clinical indicators to accurately predict the risk of ischemic stroke. Materials and Methods: 127 patients were finally enrolled and randomly divided into training and test cohorts. HR-VWMRI three-dimensional T1 weighted imaging (T1WI) and contrast-enhanced T1WI (T1CE) were collected. A traditional model was built by recording and calculating radiographic features of the carotid plaques and patients' clinical indicators. After extracting radiomics features from T1WI and T1CE images, then the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal features and construct radiomics_T1WI model and radiomics_T1CE model. The traditional and radiomics features were used to built combined models. The performance of all the models predict ischemic stroke was evaluated in the training cohort and test cohort respectively. Results: Body mass index (BMI) and intraplaque hemorrhage (IPH) were independently related to ischemic stroke and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.79 versus 0.78 in the training and test cohorts. The AUC value of radiomics_T1WI model is the lowest in the training and test cohorts, but the prediction performance is significantly improved when the model combines IPH and BMI. The AUC value of combined_T1WI model was 0.78 and 0.81 in the training and test cohorts respectively. In addition, in the training and test cohort, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics which is combined_T1CE model had the highest AUC value of 0.84 and 0.82 respectively. Conclusions: Compared with other model, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics which is combined_T1CE model can accurately predict the risk of ischemic stroke.