Abstract / Summary
The current clinical gold standard for identifying vulnerable plaque is plaque Magnetic Resonance Imaging (MRI). However, owing to its limited availability, many patients do not have access. This pilot study aims to explore whether computational fluid dynamics (CFD) modelling, established using computed tomography angiography (CTA) and neurovascular ultrasound measurements (NVUS), as part of current clinical routine, can be used to triage vulnerable plaque in patients with carotid atherosclerotic stenosis. Nine patients who underwent evaluation for carotid endarterectomy between January 2021 and March 2023 were retrospectively included. Carotid geometries were segmented from CTA and reconstructed for CFD simulations using patient-specific NVUS-derived boundary conditions. Geometric and novel hemodynamic features were extracted. A logistic regression (LR) and a support vector machine (SVM) classifier with leave-one-out cross-validation and embedded feature selection were trained to identify vulnerable plaque, assessed according to the Plaque-Reporting and Data System based on multi-modal plaque MRI findings. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall AUC, confusion matrix, and other related metrics. Eighteen carotid arteries (9/18 were vulnerable) were analyzed. The classifiers achieved mean ROC-AUC of 0.77 and 0.78 for the LR and SVM, respectively. Key risk factors identified by both classifiers included mean helicity (OR: 0.5, 95%CI: 0.42, 0.58; mean feature coefficient: -0.27, 95%CI: -0.39, -0.15), kurtosis/skewness of helicity, and skewness of streamwise vorticity. Patient-level and sub-cohort analyses also support these findings. This pilot study demonstrated that combining NVUS and CTA with CFD may facilitate the identification of vulnerable plaques. Higher order statistics of intravascular hemodynamics may serve as promising surrogate features of plaque vulnerability.