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Updated: Jul 12, 2026
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
A radiomics-based approach with automated segmentation for identifying symptomatic basilar artery plaques in acute
Insights
A new framework accurately identifies symptomatic basilar artery plaques in patients with intracranial atherosclerotic disease (ICAD). This AI-driven approach using MRI vessel wall imaging and a foundation model improves stroke risk stratification.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Intracranial atherosclerotic disease (ICAD) is a major cause of ischemic stroke.
- Basilar artery atherosclerosis poses a significant risk for recurrent strokes.
- Accurate identification of symptomatic plaques is crucial for effective treatment.
Purpose of the Study:
- To develop and validate a framework for identifying symptomatic basilar artery plaques.
- Integrate MRI vessel wall segmentation with a tabular foundation model for plaque characterization.
- Enhance risk stratification and treatment planning for ICAD patients.
Main Methods:
- Retrospective analysis of 256 patients with basilar artery stenosis.
- Automated segmentation of basilar artery vessel wall using Vessel-SegNet on VWI.
- Feature extraction (radiomics, morphology, signal intensity) and training of Tabular Prior-Fitted Network (TabPFN).
Main Results:
- A radiomics-based model achieved an AUC of 0.887 for distinguishing symptomatic from asymptomatic plaques.
- This significantly outperformed traditional feature-based models (AUC 0.784).
- The framework demonstrated high accuracy, sensitivity, and specificity.
Conclusions:
- The integrated framework accurately identifies symptomatic basilar artery plaques.
- This AI-driven approach offers a scalable and objective tool for ICAD management.
- Supports improved risk stratification and personalized treatment strategies.
Background:
Intracranial atherosclerotic disease (ICAD) is a leading cause of ischemic stroke worldwide, with basilar artery atherosclerosis frequently involved. Despite therapeutic advances, patients with basilar artery atherosclerosis remain at substantial risk of recurrent stroke, highlighting the need for improved strategies to accurately identify symptomatic basilar artery plaques. In this study, we aimed to develop and validate a framework for identifying symptomatic basilar artery plaques by integrating MRI-based vessel wall segmentation with a tabular foundation model for quantitative plaque characterization.
Methods:
In this retrospective study, we analyzed 256 patients with basilar artery stenosis who underwent three-dimensional high-resolution vessel wall imaging (VWI) between May 2018 and November 2023. An automated convolutional neural network-based model (Vessel-SegNet) was applied to segment the basilar artery vessel wall on both pre-contrast VWI (preVWI) and contrast-enhanced VWI (ceVWI) images. Radiomics, morphological, and signal intensity features were subsequently extracted from the segmented vessel walls and used to train a Tabular Prior-Fitted Network (TabPFN) to identify symptomatic versus asymptomatic plaques. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Results:
The model based on traditional morphological and signal intensity features achieved an AUC of 0.784 (95% CI: 0.673-0.877) for distinguishing symptomatic basilar artery plaques from asymptomatic basilar artery plaques. In contrast, the radiomics-based model, incorporating features extracted from both preVWI and ceVWI, showed a significantly improved discriminative performance, with an AUC of 0.887 (95% CI: 0.798-0.955).
Conclusion:
The proposed framework, integrating automated vessel wall segmentation with a tabular foundation model, enables accurate identification of symptomatic basilar artery plaques. This approach provides a scalable and objective tool that may support risk stratification and inform treatment planning in patients with ICAD.
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