Radiomics signature and deep learning signature of intrathrombus and perithrombus for prediction of Malignant Cerebral Edema after Acute Ischemic Stroke: a multicenter CT study

Abstract BackgroundMalignant cerebral edema (MCE) exerts a detrimental impact on clinical outcomes, underscoring the critical need for the development of more precise and objective predictive models. To accurately assess the predictive ability of radiomics and deep learning (DL) features in intrathrombus and perithrombus regions for the risk of MCE after acute ischemic stroke (AIS).Materials and MethodsA retrospective study was conducted, enrolling 406 AIS patients who underwent admission CT before endovascular thrombectomy (EVT). Patients from Center A were randomly divided into training and testing sets at a 7:3 ratio, while those from Center B and Center C formed the external validation cohort. Regions of interest (ROIs) of thrombus and perithrombus were manually delineated and automatically expanded in margin by one pixel. 428 radiomic features were extracted from CT images of intrathrombus and perithrombus regions, and 128 DL features were obtained by inputting these images into a VGG16 architecture. Following features fusion, least absolute shrinkage and selection operator (LASSO) regression was employed for dimensionality reduction. Eleven machine learning classifiers were used for model development. Models’ performance was evaluated using Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (AUC), with AUC differences tested using DeLong’s method.ResultsMCE occurred in 49 patients (12.1%). In the validation cohort, the logistic regression (LR) models demonstrated discriminative performance with perithrombus (LR-peri: MCC = 0.857, AUC = 0.891), intrathrombus, (LR-intra: MCC = 0.328, AUC = 0.626), and combined (LR-combined: MCC = 0.41, AUC = 0.869) models. The LR-combined model exhibited a significantly superior predictive capacity to that of LR-intra (p < 0.05).ConclusionsPerithrombus features can improve the prediction of MCE after AIS, which in turn enables the optimization of medical resource allocation.Clinical relevance statement:Emphasis is placed on the critical significance of radiomics extracted from the area in and around the thrombus in predicting MCE after AIS, which has far-reaching significance for improving patient prognosis.