ArticleNeuroradiology2025
Automated assessment of collateral circulation and infarct core: predictors of functional outcomes in acute ischemic stroke following endovascular thrombectomy.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Comparison of AI-assisted quantitative collateral score and visual collateral score in stroke thrombectomy triage.Neuroradiology · 2026Article
- Endovascular treatment and clinical outcomes in minor ischemic stroke due to large-vessel disease: a retrospective study.Frontiers in neuroscience · 2026Article
- An interpretable machine learning model for predicting prognosis in acute ischemic stroke with large vessel occlusion.Frontiers in neurology · 2026Article
Corrections and comments
- Erratum issued
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to evaluate the predictive value of automatically assessed collateral circulation (CC) and infarct core for functional outcome in acute ischemic stroke (AIS) patients treated with endovascular thrombectomy (EVT).
methodsWe conducted a retrospective cohort study of 208 patients with anterior large vessel occlusion treated with EVT. Two AI-powered software were used to automatically assess CC and infarct core. Comparative analyses included patient demographics, clinical and imaging data, and functional outcome. Univariate and multivariable logistic regression analyses were conducted to predict the 90-day functional outcome. A favorable outcome was defined as a modified Rankin scale (mRS) score ≤ 2.
resultsAmong the 208 patients, 114 (54.8%) were women and 94 were men, with a mean age of 71.4 ± 13.3 years. Patients with higher collateral score (CS) exhibited lower infarct core volumes (p < 0.001) and better mRS score at 90 days (p = 0.008). Among patients with a favorable outcome, the mean infarct core volume was lower compared to those with poor outcomes (5 mL vs. 8.6 mL, p = 0.003). In univariate logistic regression, both infarct core (OR 0.94, p = 0.005) and CS (OR 1.84, p = 0.014) were predictors of favorable outcome. However, in multivariable models, only infarct core remained a significant independent predictor [AORs of 0.95 (p = 0.021) and 0.96 (p = 0.039)].
conclusionAutomatically assessed infarct core is a robust predictor of functional outcome in AIS patients post-EVT, while CS's predictive value diminishes when adjusted for infarct core. These findings support the integration of AI-powered evaluations in clinical settings to improve prognosis and treatment strategies for AIS.
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