Non-invasive imaging of individual histological carotid plaque characteristics: A diagnostic accuracy meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Accurately detecting carotid plaque characteristics is crucial for identifying high-risk patients due to risk of cerebrovascular events and complications during revascularizations. Diagnostic accuracy of individual and overall carotid plaque characteristics using computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) compared to histology in patients with symptomatic/asymptomatic carotid plaques was aimed. METHODS: After prospective registration on PROSPERO (CRD42022329690), Medline Ovid, Embase, Cochrane Library, and Web of Science were searched without any limitations. QUADAS-2 tool was used to study quality assessment, GRADE framework to assess evidence certainty, and univariate/bivariate random-effect meta-analyses for data analysis. RESULTS: Of 5960 studies screened, 107 were identified, resulting in 253 diagnostic accuracy comparisons of 16 plaque characteristics (28 CT, 120 MRI, and 105 US). CT detected intraplaque hemorrhage (IPH) and lipid-rich necrotic core (LRNC) with good accuracy (86 % [95 %CI 67-95] and 84 % [72-91], respectively) and exhibited very high accuracy for ulceration (92 % [87-95]; 76 % on MRI and 75 % on US) and calcification (90 % [58-98] vs. 89 % [87-91] on MRI). MRI identified LRNC and IPH with good accuracy (86 % [81-89] and 86 % [84-88], respectively), and differentiated between acute/subacute/old IPH (accuracy >87 %). US accurately detected ruptured fibrous cap (85 % [77-91]), comparable to MRI (85 % [79-90]), but demonstrated lower performance for other characteristics. Finally, CT detected overall carotid morphology with 89 % accuracy, followed by MRI (86 %; p = 0.374 to CT), and significantly lower by US (78 %; p < 0.001). CONCLUSION: CT identified key plaque features, especially ulceration and calcification. MRI provided thorough plaque assessment by detecting all features and differentiating IPH age. For overall morphology, CT and MRI surpassed US accuracy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".