Deep learning models based on DWI-MRI for prognosis prediction in acute ischemic stroke receiving intravenous thrombolysis: Development and validation

Huan Yang, Wenxi Wang, Xin Zhao, Qi Xuan, Cao Jiang, Bo Zhao,
Deep learning models based on DWI-MRI for prognosis prediction in acute ischemic stroke receiving intravenous thrombolysis: Development and validation,
Journal of Neuroradiology,
Volume 52, Issue 6,
2025,
101391,
ISSN 0150-9861,
https://doi.org/10.1016/j.neurad.2025.101391.
(https://www.sciencedirect.com/science/article/pii/S015098612500149X)
Abstract: Objective
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.
Materials and 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.
Results
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).
Conclusion
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.
Keywords: Acute Ischemic Stroke; DWI-MRI; Intravenous Thrombolysis; Machine Learning; Deep Learning