Deep learning network based on high-resolution magnetic resonance vessel wall imaging combined with attention mechanism for predicting stroke recurrence in patients with symptomatic intracranial atherosclerosis

Abstract

Background: High-resolution magnetic resonance vessel wall imaging (HR-VWI) offers enhanced visualization of vascular structures, thereby facilitating the deep learning (DL) network's acquisition of more extensive and detailed image information. This study aimed to develop a high-precision integrated model leveraging DL with an attention mechanism based on HR-VWI for predicting recurrent stroke in patients with symptomatic intracranial atherosclerotic stenosis (sICAS).

Methods: A retrospective study was conducted involving 363 sICAS patients who underwent HR-VWI, with data divided into a training set (n=254) from Center 1 (The First Affiliated Hospital of Xinxiang Medical University) and a test set (n=109) from Center 2 (The Sixth People's Hospital of Shanghai Jiao Tong University). Two convolutional neural network (CNN) models, ResNet50 and DenseNet169, were employed as feature extractors to capture image information from culprit plaques in HR-VWI. Integrating the Transformer attention mechanism, an advanced ensemble model, Trans-CNN, was constructed to predict stroke recurrence in sICAS patients. Model performance was evaluated using receiver operating characteristic (ROC) curves, with DeLong's test for comparing models. Additionally, decision curve analysis (DCA) and calibration curves were utilized to assess the model's practical and clinical value.

Results: Trans-CNN demonstrated superior predictive performance, outperforming other models in both the training and test sets. Specifically, in the training set, Trans-CNN achieved an area under the curve (AUC) of 0.951 [95% confidence interval (CI): 0.923-0.974], accuracy of 0.880 (95% CI: 0.797-0.937), sensitivity of 0.900 (95% CI: 0.836-1.000), and specificity of 0.882 (95% CI: 0.757-0.948). Similarly, in the test set, it achieved an AUC of 0.912 (95% CI: 0.839-0.969), accuracy of 0.858 (95% CI: 0.743-0.936), sensitivity of 0.880 (95% CI: 0.693-1.000), and specificity of 0.810 (95% CI: 0.690-0.976). The AUC improvement of Trans-CNN over all other models was statistically significant (DeLong's test, P<0.05). Calibration curve analysis revealed good agreement between predicted probabilities and observed outcomes in both sets. DCA further underscored the potential value of Trans-CNN in guiding clinical decision-making.

Conclusions: The integrated model combining DL with an attention mechanism based on HR-VWI exhibits excellent performance in assessing the risk of stroke recurrence in sICAS patients. This advancement holds significant potential in assisting clinicians in diagnosis and developing individualized treatment strategies.

Keywords: Stroke recurrence; attention mechanism; deep learning (DL); high-resolution vessel wall imaging.