Radiomics signature and deep learning signature of intrathrombus and perithrombus for prediction of Malignant Cerebral Edema after Acute Ischemic

Shuhao Wang¹Jingxuan Jiang^2*Xiaoli Gu¹Haiqi WANG¹Yangyang Nan¹Shuhao Wang²Xiaoyu Xu²Chenqing Wang³

• ¹Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
• ²Shanghai Sixth People's Hospital Affliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
• ³Shukun Beijing Technology Co Ltd, Beijing, China

Objectives: To accurately assess the predictive ability of radiomics and deep learning (DL) features in intrathrombus and perithrombus regions for the risk of malignant cerebral edema (MCE) after acute ischemic stroke (AIS).: A retrospective study was conducted, enrolling 406 AIS patients who underwent admission CT before endovascular thrombectomy (EVT). Center A patients were randomly divided (7:3) into training/testing sets; Centers B and 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. Results： MCE occurred in 49 patients (12.1%). In the validation cohort, the logistic regression (LR) models demonstrated discriminative performance with perithrombus 删除了: 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.(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).optimized medical resource allocation.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.• Machine learning models related to thrombosis can effectively predict the occurrence of MCE after AIS.• The proposed LR-peri radiomics model reached a higher area under the curve (AUC: 0.891, 95% CI: 0.762 -1.000).• Its application provides a beneficial approach for formulating personalized treatment strategies for patients with AIS.