Journal article
Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?
Journal of neurointerventional surgery, Vol.18(5), pp.1267-1273
05/2026
DOI: 10.1136/jnis-2025-023897
PMID: 40707242
Abstract
BackgroundIn an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoing reperfusion treatment. However, its immediate availability remains limited, especially in resource-constrained regions. We sought to evaluate the association of initial core volume (ICV) measured on non-contrast computed tomography (NCCT) by a deep learning-based algorithm with outcomes in patients undergoing reperfusion treatment.MethodsConsecutive patients who received reperfusion treatments were collected from a prospectively maintained registry in three comprehensive stroke centers from January 2021 to May 2024. ICV on admission was estimated on NCCT by a previously validated deep learning algorithm (Methinks). Outcomes of interest included favorable outcome (modified Rankin Scale score 0–2 at 90 days) and symptomatic intracranial hemorrhage (sICH).ResultsThe study comprised 658 patients of mean (SD) age 72.7 (14.4) years and median (IQR) baseline National Institutes of Health Stroke Scale (NIHSS) score of 12 (6–19). Primary endovascular treatment was performed in 53.7% of patients and 24.9% received IV thrombolysis only. Patients with favorable outcomes had a lower mean (SD) automated ICV (aICV; 12.9 (26.9) mL vs 34.9 (40) mL, P<0.001). Lower aICV was associated with a favorable outcome (adjusted OR 0.983 (95% CI 0.975 to 0.992), P<0.001) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of a favorable outcome decreased by 1.7%. Patients who experienced sICH had a higher mean (SD) aICV (47.8 (61.1) mL vs 20.5 (32) mL, P=0.001). Higher aICV was independently associated with sICH (adjusted OR 1.014 (95% CI 1.004 to 1.025), P=0.009) after adjusted logistic regression. For every 1 mL increase in aICV, the odds of sICH increased by 1.4%.ConclusionIn patients with stroke undergoing reperfusion therapy, aICV assessment on NCCT predicts long-term outcomes and sICH. Further studies determining the potential role of aICV assessment to safely expand and simplify reperfusion therapies based on AI interpretation of NCCT may be justified.
Details
- Title: Subtitle
- Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?
- Creators
- Alan Flores - Hospital Universitari Joan XXIII de TarragonaXavier Ustrell - Institut de Recerca Biomèdica Catalunya SudLaia Seró - Hospital Universitari Joan XXIII de TarragonaAntoni Suarez - Stroke Unit. Neurology Department, Hospital Universitari de Tarragona Joan XXIII, Tarragona, SpainYlenia Avivar - Institut de Recerca Biomèdica Catalunya SudLeonardo Cruz-Criollo - University of IowaMilagros Galecio-Castillo - University of IowaJorge Cespedes - University of Iowa, NeurologyJudith CendreroVictor SalviaAlvaro Garcia-Tornel - Vall d'Hebron Hospital UniversitariMarta Olive Gadea - Hebron UniversityPere Canals - Vall d'Hebron Hospital UniversitariSantiago Ortega-Gutierrez - University of Iowa Hospitals and ClinicsMarc Ribó - Vall d'Hebron Hospital Universitari
- Resource Type
- Journal article
- Publication Details
- Journal of neurointerventional surgery, Vol.18(5), pp.1267-1273
- DOI
- 10.1136/jnis-2025-023897
- PMID
- 40707242
- NLM abbreviation
- J Neurointerv Surg
- ISSN
- 1759-8478
- eISSN
- 1759-8486
- Publisher
- BMJ Publishing Group Ltd
- Language
- English
- Electronic publication date
- 07/24/2025
- Date published
- 05/2026
- Academic Unit
- Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
- Record Identifier
- 9984865439402771
Metrics
6 Record Views