ArticleStroke2020
Clot-Based Radiomics Predict a Mechanical Thrombectomy Strategy for Successful Recanalization in Acute Ischemic Stroke.
Article in Stroke, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 4 of them syntheses that pooled it.
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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
61 citing papers in PubMed, 4 syntheses or guidelines pooled it, 98 citations in OpenAlex.
- Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004-2024).Frontiers in neurology · 2025Pooled it
- Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review.World neurosurgery · 2022Pooled it
- Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.Frontiers in neurology · 2022Pooled it
- NET-mediated immunothrombosis in ischemic stroke: pathobiology, biomarkers, and therapeutic targeting.Thrombosis journal · 2026Review
- ESR Innovation in Focus: AI in interventional radiology: existing applications and ongoing research.European radiology · 2026Article
- Dual-energy CT clot and peri-clot radiomics for predicting complete reperfusion and clinical outcome following endovascular therapy in acute ischemic stroke.Insights into imaging · 2026Article
- CT-based intrathrombus and perithrombus radiomics for predicting complete recanalization after endovascular thrombectomy in acute ischemic stroke.Frontiers in neurology · 2026Article
- A web-based radiomics nomogram combining MRI features and clinical data for predicting 30-day progression of acute ischemic stroke: a two-center study.Frontiers in neurology · 2026Article
- Cerebrospinal fluid-based clinical-radiomics model for predicting treatment prognosis of acute ischemic stroke.Quantitative imaging in medicine and surgery · 2025Article
- Dual-Energy CT-Based Assessment of Thrombotic Heterogeneity for Predicting Stroke Source and Response to Machine Thrombectomy: A Step Toward Visualization Thrombus Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.Annals of surgical oncology · 2025Article
- Segmentation of the Hyperdense Artery Sign on Noncontrast CT in Ischemic Stroke Using Artificial Intelligence.Journal of clinical neurology (Seoul, Korea) · 2025Article
- Is Personalized Mechanical Thrombectomy Based on Clot Characteristics Feasible? A Radiomics Model Using NCECT to Predict FPE in AIS Patients Undergoing Thromboaspiration.Journal of clinical medicine · 2025Article
- Acute ischemic stroke patient factors associated with poor outcomes in patients with favorable collaterals and successful thrombectomy.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2025Article
- Thrombus composition in ischaemic stroke: histological and radiological evaluation, and implications for acute clinical management.Journal of thrombosis and thrombolysis · 2025Review
- PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy.Brain sciences · 2025Article
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2024Article
- Artificial intelligence in interventional radiology: state of the art.European radiology experimental · 2024Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeMechanical thrombectomy (MTB) is a reference treatment for acute ischemic stroke, with several endovascular strategies currently available. However, no quantitative methods are available for the selection of the best endovascular strategy or to predict the difficulty of clot removal. We aimed to investigate the predictive value of an endovascular strategy based on radiomic features extracted from the clot on preinterventional, noncontrast computed tomography to identify patients with first-attempt recanalization with thromboaspiration and to predict the overall number of passages needed with an MTB device for successful recanalization.
methodsWe performed a study including 2 cohorts of patients admitted to our hospital: a retrospective training cohort (n=109) and a prospective validation cohort (n=47). Thrombi were segmented on noncontrast computed tomography, followed by the automatic computation of 1485 thrombus-related radiomic features. After selection of the relevant features, 2 machine learning models were developed on the training cohort to predict (1) first-attempt recanalization with thromboaspiration and (2) the overall number of passages with MTB devices for successful recanalization. The performance of the models was evaluated on the prospective validation cohort.
resultsA small subset of radiomic features (n=9) was predictive of first-attempt recanalization with thromboaspiration (receiver operating characteristic curve-area under the curve, 0.88). The same subset also predicted the overall number of passages required for successful recanalization (explained variance, 0.70; mean squared error, 0.76; Pearson correlation coefficient, 0.73;
conclusionsClot-based radiomics have the ability to predict an MTB strategy for successful recanalization in acute ischemic stroke, thus allowing a potentially better selection of the MTB strategy, as well as patients who are most likely to benefit from the intervention.
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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.