Prediction of catheter navigation difficulty during mechanical thrombectomy using CTA-based carotid siphon
Ferenc Dezső Bakó1, Attila Tanács2, József Tolnai1
1Department of Medical Physics and Informatics, University of Szeged, Szeged, Hungary.
Insights
3D analysis of carotid siphon anatomy using CT angiography can predict device support needs during stroke thrombectomy. This approach improves accuracy in identifying cases requiring specific device escalation for successful catheter navigation.
Area of Science:
- Neurosurgery and Interventional Radiology
- Medical Imaging and Computational Anatomy
- Biomedical Engineering
Background:
- Mechanical thrombectomy for acute ischemic stroke faces challenges in navigating the carotid siphon.
- Complex vascular anatomy can necessitate device escalation, increasing procedure time and delaying reperfusion.
- Traditional 2D measurements may not fully represent 3D vascular features impacting device behavior.
Purpose of the Study:
- To evaluate if CT angiography (CTA)-derived carotid siphon morphometry can predict the device support level needed for aspiration catheter navigation.
- To assess the utility of 3D vascular analysis in optimizing procedural planning for mechanical thrombectomy.
Main Methods:
- Retrospective analysis of 53 patient CTA datasets undergoing endovascular thrombectomy.
- 3D segmentation and morphometric analysis of the carotid siphon using specialized software.
- Development of a logistic regression classifier to predict procedural difficulty based on geometric attributes.
Main Results:
- A model using CTA-derived morphometry achieved 75.0% accuracy in predicting device support needs in a high-confidence subset of 32 cases.
- The model demonstrated improved performance with a kappa statistic of 0.65 and a weighted F-measure of 0.73.
- High recall (100%) for M2 and precision (100%) for M3 categories were observed, though M4 remained challenging.
Conclusions:
- CTA-derived 3D carotid siphon morphometry offers clinically relevant insights into catheter navigation difficulty during thrombectomy.
- Confidence-gated classification enhances prediction reliability for anatomically defined cases.
- Further multicenter studies are needed to validate this predictive approach for patient-specific preprocedural planning.
Introduction:
Successful catheter navigation through the carotid siphon remains a major technical challenge during mechanical thrombectomy for acute ischemic stroke. Unfavorable vascular anatomy may require stepwise device escalation, prolonging procedural time and potentially delaying reperfusion. Because conventional two-dimensional measurements may not fully capture the three-dimensional vascular features influencing device behavior, this study evaluated whether CTA-derived carotid siphon morphometry can predict the level of device support required for aspiration catheter navigation.
Methods:
This retrospective study included patients who underwent endovascular thrombectomy at the Department of Neurosurgery, University of Szeged, between 15 April 2020 and 16 May 2022. From an initial cohort of 160 patients, 54 CTA datasets were suitable for three-dimensional segmentation, and 53 cases were included in the final morphometric analysis. Paired native CT and CTA scans were processed using a subtraction-based workflow in 3D Slicer, followed by vesselness filtering, segmentation, and centerline extraction. Local carotid siphon features, including cross-sectional area and curvature-related distance measurements, were extracted. Procedural difficulty was defined using a stepwise device-escalation scale reflecting the support required to advance the aspiration catheter through the carotid siphon. A logistic regression classifier with ridge regularization and correlation-based feature selection was trained using nine geometric attributes.
Results:
In the full 53-case dataset, the model achieved 58.5% accuracy, with substantial interclass overlap, particularly among higher-complexity categories. Applying a confidence-based decision criterion, retaining only predictions where the second-highest class probability was less than 60% of the highest class probability, yielded a high-confidence subset of 32 cases. In this subset, accuracy increased to 75.0%, the kappa statistic increased from 0.40 to 0.65, and the weighted F-measure reached 0.73. The model achieved 100% recall for M2 and 100% precision for M3, whereas M4 remained difficult to distinguish.
Conclusion:
CTA-derived three-dimensional morphometric analysis of the carotid siphon provides clinically relevant information on catheter navigation difficulty during mechanical thrombectomy. Confidence-gated classification identified anatomically well-defined cases in which device-support requirements could be predicted more reliably. Borderline cases remained influenced by factors beyond luminal geometry. Larger multicenter studies are warranted to validate this approach and assess its role in patient-specific preprocedural planning.
