Trial reportStroke2022
Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.
Trial report in Stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 38 citations in OpenAlex.
- Deep learning-enabled high-performance multiphoton fluorescence vascular imaging using clinically approved fluorescent probes.iScience · 2026Article
- Review
- The Role of CT Perfusion in the Evaluation and Management of Acute Ischemic Stroke-A Systematic Review.Life (Basel, Switzerland) · 2025Review
- Intravenous Thrombolysis in Acute Ischemic Stroke: A Prognostic Prediction Model and the Role of Ischemic Core Growth Rate.CNS neuroscience & therapeutics · 2025Article
- Review
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- Late Lesion Growth following Endovascular Therapy: Is 24 h Too Early to Assess Acute Infarct Size Including the Effects of Secondary Injury?Cerebrovascular diseases (Basel, Switzerland) · 2025Article
- Assessment of Perfusion Volumes by a New Automated Software for Computed Tomography Perfusion.Stroke and vascular neurology · 2024Article
- Deep Learning-Based Prediction of Final Infarct Core from CT Perfusion Data: A Comparison to the Clinical Standard.Stroke (Hoboken, N.J.) · 2024Article
- A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke.Frontiers in neurology · 2024Review
- Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies.Journal of clinical medicine · 2023Review
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
- Follow-Up Infarct Volume Prediction by CTP-Based Hypoperfusion Index, and the Discrepancy between Small Follow-Up Infarct Volume and Poor Functional Outcome-A Multicenter Study.Diagnostics (Basel, Switzerland) · 2023Article
- Severely Hypoperfused Brain Tissue Correlates with Final Infarct Volume Despite Recanalization in DMVO Stroke.Journal of the Belgian Society of Radiology · 2023Article
- Imaging selection for reperfusion therapy in acute ischemic stroke beyond the conventional time window.Journal of neurology · 2022Review
- Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future perspectives.Frontiers in neurologyReview
- Quantitative multi-slice spiral CT perfusion parameters as predictors of collateral status and 90-day functional outcome in acute ischemic stroke.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 9 institutions in 4 countries.
Funding
Abstract
background and purposeComputed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach.
methodsWe trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project]). We calculated the mean absolute difference between the predictions of the deep learning model and the final infarct volume versus the mean absolute difference between computed tomography perfusion imaging processing by RAPID software (iSchemaView, Menlo Park, CA) and the final infarct volume. Next, we determined infarct growth rates for every patient.
resultsWe included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct volume prediction compared with the RAPID software in both the derivation, mean absolute difference 34.5 versus 52.4 mL, and validation cohort, 41.2 versus 52.4 mL (
conclusionsWe validated a deep learning-based method which improved final infarct volume estimations compared with classic computed tomography perfusion imaging processing. In addition, the deep learning model predicted individual infarct growth rates which could enable the introduction of tissue clocks during the management of acute stroke.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.