Trial reportEuropean radiology2021
Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.
Trial report in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
76 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence diagnostic performance in image-based vulnerable carotid plaque detection: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004-2024).Frontiers in neurology · 2025Pooled it
- Relation of Carotid Plaque Features Detected with Ultrasonography-Based Radiomics to Clinical Symptoms.Translational stroke research · 2022Trial
- Carotid Plaque-RADS: Impact of its ancillary features on improving the detection of symptomatic plaques.European radiology · 2026Article
- Plaque-level machine-learning prediction of carotid plaque vulnerability on computed tomography angiography.Neuroradiology · 2026Article
- A radiomics model based on CTA subtraction imaging for differentiating symptomatic and asymptomatic carotid plaques: a retrospective study.BMC cardiovascular disorders · 2026Article
- A fusion model of deep learning and conventional features based on computed tomography angiography of carotid plaque for predicting the risk of acute ischemic stroke.Quantitative imaging in medicine and surgery · 2026Article
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- A hybrid model based on vessel wall magnetic resonance imaging predicts recurrence in posterior circulation ischemic stroke: a multi-institutional study.Journal of neurointerventional surgery · 2026Article
- Integration of Conventional and Radiomic Features From Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging for Multimodal Prediction of Symptomatic Carotid Atherosclerotic Plaques.Journal of the American Heart Association · 2026Article
- Clinical outcomes of carotid artery stenting with open- versus closed-cell stents.Journal of neurointerventional surgery · 2026Article
- Dual-modal ultrasound radiomics model enhances identification of symptomatic carotid plaque: a multicenter retrospective study.The international journal of cardiovascular imaging · 2026Article
- Identification of histological carotid plaque vulnerability by CT angiography using perivascular adipose tissue radiomics signature.Insights into imaging · 2026Article
- Multimodal Radiomics Model Combining HR-VWI and Clinical Features for Identifying Symptomatic Basilar Atherosclerotic Plaques.BioMed research international · 2026Article
- Machine Learning Applied to CT Radiomics Identifies Symptomatic Carotid Plaques.Current medical imaging · 2026Article
- High-resolution MRI-based Radiomics and Machine Learning for Identification of Middle Cerebral Artery Vulnerable PlaquesCurrent medical imaging · 2026Article
- Interpretable machine learning for detecting symptomatic patients with carotid atherosclerosis on computed tomography angiography: a retrospective diagnostic study.BMC medical imaging · 2025Article
- Integrating MRI radiomics with novel fluid-based texture features (GLFZM) to predict atherothrombotic stroke risk.Radiological physics and technology · 2025Article
- Deep learning-based automatic segmentation of arterial vessel walls and plaques in MR vessel wall images for quantitative assessment.European radiology · 2025Article
16 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques. MATERIALS AND
methodsOne hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared.
resultsIntraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM.
conclusionsOur MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques. KEY POINTS: • Carotid plaque multi-contrast MRI stores other valuable information to be further exploited by radiomics analysis. • Radiomics analysis can accurately distinguish symptomatic from asymptomatic carotid plaques. • The radiomics model is superior to the traditional model in the identification of high-risk plaques.
Indexed as
Identifiers
33068185What Socratic 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 Socratic graph.