ReviewAging and disease2021
Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges.
Review in Aging and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05192161 (Computational Medical Imaging), which is not on this map. Cited by 49 papers, 3 of them syntheses that pooled it.
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.
Computational Medical Imaging (Radiomics) and Prediction of Diffusion/FLAIR Mismatch in Stroke Patients
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis.Frontiers in neuroscience · 2024Pooled it
- Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.Korean journal of radiology · 2022Trial
- CT Texture Analysis of Acute Ischemic Stroke: Prediction of Hemorrhagic Transformation After Thrombolysis.Brain sciences · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Radiomics features and clinical factors for predicting restenosis following endovascular therapy in patients with peripheral artery disease.BMC medical informatics and decision making · 2026Article
- High-resolution MRI-based Radiomics and Machine Learning for Identification of Middle Cerebral Artery Vulnerable PlaquesCurrent medical imaging · 2026Article
- BRIDGE: Burr hole vs. craniotomy - radiomic indicators determining the chronification grid in acute subdural hematoma evaluation.Brain & spine · 2026Article
- Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke.Frontiers in neuroscience · 2026Article
- Predicting malignant cerebral edema after acute ischemic stroke: a machine-learning model with multi-region radiomics.Quantitative imaging in medicine and surgery · 2025Article
- Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke.The neurologist · 2025Article
- Research on imaging biomarkers for chronic subdural hematoma recurrence.Medical & biological engineering & computing · 2025Article
- Functional Connectivity and MRI Radiomics Biomarkers of Cognitive and Brain Reserve in Post-Stroke Cognitive Impairment Prediction-A Study Protocol.Life (Basel, Switzerland) · 2025Article
- AI-driven integration of imaging and radiology language improves stroke mortality prediction.Frontiers in neurology · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Review
- Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.BMC medical imaging · 2024Article
- An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy.Quantitative imaging in medicine and surgery · 2024Article
- Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.European radiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year. Neuroimaging, such as cranial computed tomography or magnetic resonance imaging, is the backbone of stroke management strategies, which can guide treatment decision-making (thrombolysis or hemostasis) at an early stage. With advances in computational technologies, particularly in machine learning, visual image information can now be converted into numerous quantitative features in an objective, repeatable, and high-throughput manner, in a process known as radiomics. Radiomics is mainly used in the field of oncology, which remains an area of active research. Over the past few years, investigators have attempted to apply radiomics to stroke in the hope of gaining benefits similar to those obtained in cancer management, i.e., in promoting the development of personalized precision medicine. Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis. In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies. We also discuss the potential challenges facing its widespread implementation in routine practice and the directions for future research.
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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.