ArticleAJNR. American journal of neuroradiology2026
Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Poststroke Outcomes.
Article in AJNR. American journal of neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The trial behind it
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Who cites it
1 citing paper in PubMed.
- Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.Translational stroke research · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
background and purposeCollateral circulation is a key determinant of treatment response and outcomes in acute ischemic stroke (AIS), yet its assessment in clinical practice remains limited and subjective. While CTP offers insight into tissue viability, its restricted availability and susceptibility to artifacts reduce its practical utility, particularly in smaller centers. As an accessible alternative, we developed and validated an automated quantitative collateral index (qCI) derived from CTA using a deep learning U-Net segmentation framework and evaluated the ability of CTA-based features to predict poststroke recovery and functional outcomes. MATERIALS AND
methodsWe retrospectively analyzed prospectively collected data from 230 patients with AIS who underwent endovascular thrombectomy (EVT) between 2019 and 2023. CTA scans were segmented using a validated neural network-based vascular extraction model to generate 3D vessel networks and compute morphology metrics (vessel length, branching, fractal dimension, tortuosity). A fully automated qCI was derived through hemispheric comparison of vascular features following spatial registration. Agreement of qCI with clinician grading was quantified. Gradient boosted decision tree models were trained to predict early neurologic deterioration, early neurologic improvement, and 90-day mRS using the following: 1) CTP-only (core, penumbra, mismatch), 2) CTA-only (qCI + morphology), and 3) combined CTA + CTP features.
resultsAutomated qCI (grades 0-3) showed strong concordance with expert scoring (accuracy 0.863; Pearson
conclusionsAutomated CTA-derived qCI and cerebrovascular morphology provide rapid, objective, and reproducible collateral assessment with agreement with expert grading. These features outperform perfusion metrics in several predictive tasks and further enhance prognostic accuracy when combined with CTP. Because CTA is widely available, qCI offers a scalable, clinically practical tool for improving prediction of stroke outcome, particularly in settings where CTP is unavailable.
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Registered trials
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