Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Poststroke Outcomes

Collateral 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.

We 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.

Automated qCI (grades 0-3) showed strong concordance with expert scoring (accuracy 0.863; Pearson R = 0.880; Cohen κ = 0.786). Dichotomized collateral status achieved 0.938 accuracy (area under the receiver operating curve [AUROC] = 0.945). For 90-day mRS prediction, the CTA-only model outperformed the CTP-only model (AUROC, 0.730 versus 0.645) with better calibration (Brier score 0.178 versus 0.295). The combined CTA + CTP model performed best overall (AUROC 0.781), with similar improvements observed for early neurologic deterioration. CTA-derived features led to significant reclassification gains when added to perfusion-based models.

Automated 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.