The role of the combined [18F]FDG PET/CT carotid artery and brain studies in stroke prediction: an AI-based prediction of stroke after a 10-year observation period.
Authors
Affiliations (2)
Affiliations (2)
- Department of Nuclear Medicine, Medical University of Lublin, Lublin, Poland.
- Department of Nuclear Medicine, Medical University of Lublin, Lublin, Poland. [email protected].
Abstract
Long-term (10-year) stroke risk may be assessed by integrating metabolic biomarkers from [¹⁸F]FDG PET/CT scans of carotid plaques and the brain using an artificial intelligence (AI)-based model. A prospective study enrolled 43 symptomatic patients (median age 66 years) with carotid plaques. Participants underwent Doppler ultrasound of carotid arteries and [¹⁸F]FDG PET/CT of the brain. A Bayesian network was developed to predict stroke occurrence, incorporating imaging (SUVmax) and clinical data. Model performance was evaluated using 5-fold cross-validation and a 10-year follow-up. During follow-up, 21 patients experienced a stroke. A statistically significant association was observed between increased metabolic activity (SUVmax) in the right common carotid artery and subsequent stroke (p = 0.03). The AI model achieved an overall accuracy of 77% and an area under the curve (AUC) of 0.76. This proof-of-concept study suggests that AI-integrated multimodal data, specifically [¹⁸F]FDG PET/CT imaging of carotid and brain metabolism, may provide useful exploratory biomarkers for long-term stroke risk assessment.