# Cerebrovascular CTA radiomics for objective collateral grading in acute ischemic stroke

## TL;DR

This study introduces an automated method using CT scans to objectively assess blood flow in stroke patients, improving treatment decisions.

## Contribution

A fully automated CTA radiomics pipeline for objective collateral grading in acute ischemic stroke is proposed and validated.

## Key findings

- The pipeline achieved high accuracy in segmenting cerebral arteries and the circle of Willis.
- Combining vessel-tree and CoW features improved collateral score prediction accuracy (AUROC: 0.87).
- The model demonstrated strong generalizability on an external test cohort.

## Abstract

Collateral circulation is a key determinant of functional outcome after large vessel occlusion (LVO) and informs thrombectomy decisions. However, collateral grading is rater-dependent and error-prone. We developed an automated cerebrovascular radiomics pipeline to establish objective collateral scoring on computed tomography angiography (CTA).

We retrospectively analyzed admission CTAs from 343 LVO patients in the MR CLEAN trial, split into training/validation (n = 274) and testing (n = 69) sets. Vessel regions of interest were segmented using nnU-Net models trained on 40 arterial tree CTAs and 125 multiclass circle of Willis (CoW) cases. Radiomics features were extracted from vascular regions. Predictive features were identified, and a random forest classifier was trained to distinguish sufficient (> 50%) from insufficient (≤ 50%) collateral status according to the Tan score system. Performance was compared to the atlas-based middle cerebral artery (MCA) mask model and validated on an external cohort of 140 acute LVO patients.

Segmentation models accurately annotated cerebral arteries (95th percentile Hausdorff distance 4.49, Dice similarity coefficient 0.83) and CoW segments (2.27 and 0.81, respectively). After feature selection, 6 top features were identified for vessel-tree radiomics, 98 for MCA mask-based radiomics, and 32 for a combined vessel-tree/CoW model. Vessel-tree outperformed MCA mask model on both internal (area under the receiver operating characteristic curve (AUROC): 0.88 versus 0.82) and external (AUROC: 0.83 versus 0.66) test sets. Adding CoW features further improved performance, achieving 0.87 AUROC.

We presented a fully automated generalizable CTA radiomics approach for objective collateral scoring in acute LVO.

This study introduces a fully automated CTA cerebrovascular radiomics pipeline that objectively assesses collateral status in patients with acute ischemic stroke. Combining vessel-tree and circle of Willis features improved collateral score prediction accuracy and generalizability, supporting more reliable, data-driven decision-making in acute large vessel occlusion management.

Collateral circulation status informs prognosis and guides treatment in acute stroke, but grading is rater-dependent; our pipeline standardizes collateral assessment.We propose a CTA radiomics approach, trained and validated on multicenter data, externally tested on an independent cohort, demonstrating high effectiveness and generalizability.Automated and reliable collateral scoring has the potential to reduce inter-rater variability, improve workflow efficiency, and support individualized treatment decisions.

Collateral circulation status informs prognosis and guides treatment in acute stroke, but grading is rater-dependent; our pipeline standardizes collateral assessment.

We propose a CTA radiomics approach, trained and validated on multicenter data, externally tested on an independent cohort, demonstrating high effectiveness and generalizability.

Automated and reliable collateral scoring has the potential to reduce inter-rater variability, improve workflow efficiency, and support individualized treatment decisions.