Diagnostic Performance of Vessel Wall Magnetic Resonance Imaging (VW-MRI) for Intracranial Vasculopathies: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Vessel wall magnetic resonance imaging (VW-MRI) provides superior capabilities, compared to traditional angiography, in evaluating intracranial vessel walls. This study aimed to assess the diagnostic performance of VW-MRI in intracranial vasculopathies. This systematic review and meta-analysis followed PRISMA guidelines. PubMed, Scopus, Web of Science, Cochrane Library, and ScienceDirect were searched to identify studies investigating the diagnostic performance of VW-MRI in intracranial vasculopathies. Eligible studies should report sufficient data to construct 2 × 2 contingency tables. Disease categories were included only if at least two studies were available. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were estimated using a bivariate model. Study quality was assessed using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies) tool. Ten studies on intracranial atherosclerotic disease (ICAD) demonstrated high diagnostic accuracy (sensitivity 0.877; specificity 0.808; DOR 34.2; AUC 0.907). Subgroup analyses confirmed reliability in detecting stenosis, distinguishing culprit from non-culprit lesions, and identifying eccentric wall thickening as a biomarker. Six studies on intracranial vasculitis showed strong diagnostic performance of concentric wall enhancement (sensitivity 0.817; specificity 0.864; DOR 44.9; AUC 0.909). Two studies on intracranial dissection reported high accuracy (sensitivity 0.901; specificity 0.829; DOR 58.1; AUC 0.930). VW-MRI demonstrates excellent diagnostic performance for ICAD, intracranial vasculitis, and dissection. It provides added value beyond conventional imaging by enabling evaluation of vessel wall features and lesion characterization. Further high-quality studies, with larger sample sizes, are needed to validate its clinical utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".