Data driven approach to identify vulnerable plaques in carotid atherosclerosis using computed tomography imaging and computational fluid dynamics: A pilot study
5 affiliations
- 1ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- 2Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
- 3Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
- 4Department of Neurosurgery, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
- 5Department of Neurology and Neurophysiology, Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany
Abstract
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. Author summary: This study explored a new way to identify vulnerable carotid plaque, which contributes to one in every five stroke cases. Currently, the best method for detecting these plaques is through plaque magnetic resonance imaging (MRI), but it is unavailable in many clinics. In this study, we used a combination of standard clinical imaging modalities and computer modeling methods to simulate blood flow in the arteries. Information about the blood flow was extracted from these results, and machine learning models were trained to predict which patients had vulnerable plaques. Our results indicate that our method has the potential to identify high-risk cases for patients without access to MRI and might help reduce the risk of stroke. This approach could eventually offer a more accessible alternative to MRI for identifying dangerous carotid plaques.
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BibTeX
@article{zheng2026data,
title = {{Data driven approach to identify vulnerable plaques in carotid atherosclerosis using computed tomography imaging and computational fluid dynamics: A pilot study}},
author = {Shaokai Zheng and David Bervini and Hugo Besson and Sofía González Martínez and Mirjam R. Heldner and Mandy Müller and Thomas Meinel and Johannes Kaesmacher and Andreas Harloff and Dominik Obrist},
journal = {PLOS Digital Health},
year = {2026},
month = oct,
doi = {10.1371/journal.pdig.0001762},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001762},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Data driven approach to identify vulnerable plaques in carotid atherosclerosis using computed tomography imaging and computational fluid dynamics: A pilot study
AU - Shaokai Zheng
AU - David Bervini
AU - Hugo Besson
AU - Sofía González Martínez
AU - Mirjam R. Heldner
AU - Mandy Müller
AU - Thomas Meinel
AU - Johannes Kaesmacher
AU - Andreas Harloff
AU - Dominik Obrist
PY - 2026
DA - 2026/10/07
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001762
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001762
AB - 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. Author summary: This study explored a new way to identify vulnerable carotid plaque, which contributes to one in every five stroke cases. Currently, the best method for detecting these plaques is through plaque magnetic resonance imaging (MRI), but it is unavailable in many clinics. In this study, we used a combination of standard clinical imaging modalities and computer modeling methods to simulate blood flow in the arteries. Information about the blood flow was extracted from these results, and machine learning models were trained to predict which patients had vulnerable plaques. Our results indicate that our method has the potential to identify high-risk cases for patients without access to MRI and might help reduce the risk of stroke. This approach could eventually offer a more accessible alternative to MRI for identifying dangerous carotid plaques.
SN - 2767-3170
ER -