Comparison of Carotid Plaque Ulcer Detection Using Contrast-Enhanced and Time-of-Flight MRA Techniques
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ulceration in carotid plaque is a risk indicator for ischemic stroke. Our aim was to compare plaque ulcer detection by standard TOF and CE-MRA techniques and to identify factors that influence its detection. MATERIALS AND METHODS: Carotid MR imaging scans were acquired on 2066 participants in the ARIC study. We studied the 600 thickest plaques. TOF-MRA, CE-MRA, and black-blood MR images were analyzed together to define ulcer presence (plaque surface niche ≥2 mm in depth). Sixty ulcerated arteries were detected. These arteries were randomly assigned, along with 40 nonulcerated plaques from the remaining 540, for evaluation of ulcer presence by 2 neuroradiologists. Associations between ulcer detection and ulcer characteristics, including orientation, location, and size, were determined and explored by CFD modeling. RESULTS: One CE-MRA and 3 TOF-MRAs were noninterpretable and excluded. Of 71 ulcers in 56 arteries, readers detected an average of 39 (55%) on both TOF-MRA and CE-MRA, 26.5 (37.5%) only on CE-MRA, and 1 (1.5%) only on TOF-MRA, missing 4.5 (6%) ulcers by both methods. Ulcer detection by TOF-MRA was associated with its orientation (distally pointing versus perpendicular: OR = 5.57 [95% CI, 1.08-28.65]; proximally pointing versus perpendicular: OR = 0.21 [95% CI, 0.14-0.29]); location relative to point of maximum stenosis (distal versus isolevel: OR = 5.17 [95% CI, 2.10-12.70]); and neck-to-depth ratio (OR = 1.96 [95% CI, 1.11-3.45]) after controlling for stenosis and ulcer volume. CONCLUSIONS: CE-MRA detects more ulcers than TOF-MRA in carotid plaques. Missed ulcers on TOF-MRA are influenced by ulcer orientation, location relative to point of maximum stenosis, and neck-to-depth ratio.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".