Deep Learning Models Based on DWI-MRI for Prognosis Prediction in Acute Ischemic Stroke Receiving Intravenous Thrombolysis: Development and Validation.
Authors
Affiliations (6)
Affiliations (6)
- Department of Emergency, First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
- Department of Radiology, First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
- Department of Neurology, Traditional Chinese Medicine Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
- Department of Emergency, First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
- Department of Neurology, People's Hospital of Funing District Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
- Department of Radiology, First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, China. Electronic address: [email protected].
Abstract
To develop and validate predictive models based on diffusion-weighted imaging MRI (DWI-MRI) for assessing the prognosis of patients with acute ischemic stroke (AIS) treated with intravenous thrombolysis, and to compare the performance of deep learning versus traditional machine learning methods. A retrospective analysis was conducted on 682 AIS patients from two hospitals. Data from Hospital 1 were divided into a training set (70%) and a test set (30%), while data from Hospital 2 were used for external validation. Five predictive models were developed: Model A (clinical features), Model B (radiomic features based on DWI-MRI), Model C (deep learning features), Model D (clinical + radiomic features), and Model E (clinical + deep learning features). Performance metrics included Area Under the Curve (AUC), sensitivity, specificity, and accuracy. In the test set, Models A, B, and C achieved AUCs of 0.760, 0.820, and 0.857, respectively. The combined models, D and E, showed superior performance with AUCs of 0.904 and 0.925, respectively. Model E outperformed Model D and also demonstrated robust performance in external validation (AUC = 0.937). Deep learning models integrating DWI-MRI and clinical features outperformed traditional methods, demonstrating strong generalizability in external validation. These models may support clinical decision-making in AIS prognosis.