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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".