Vulnerable carotid plaque imaging and histopathology without a dedicated MRI receiver coil
Bibliographic record
Abstract
Stroke is associated with vulnerable carotid artery plaques showing specific histopathologic features, namely a lipid-rich necrotic core, intraplaque hemorrhage, ulceration, and thin fibrous cap. While ultrasound and computed tomography (CT) can identify carotid plaques and determine the extent of stenosis, magnetic resonance imaging (MRI) provides further information regarding plaque composition and morphology. In this feasibility study, three patients with symptomatic, moderately stenosed plaques were imaged with CT angiography (CTA) and MRI (3T and 1.5T) without a dedicated receiver coil. The patients subsequently underwent carotid endarterectomy with en-bloc excision of the plaque. The CT and MR images were analyzed independently by three neuroradiologists to identify vulnerable plaque features. The images were correlated with the histopathology to confirm the findings. All three patients had one or more vulnerable plaque features on histopathology. MRI allowed for better characterization of these features when compared to CTA. The pre- and post-contrast T1-weighted (T1W) images were most helpful for identifying the lipid-rich necrotic core and thin fibrous cap, while the time of flight-magnetic resonance angiography (TOF-MRA) and contrast-enhanced (CE)-MRA were excellent for detecting plaque hemorrhage and ulceration, respectively. The 3T images showed superior spatial and contrast resolution compared to the 1.5T images for all sequences. By providing direct correlation between imaging and histopathology, this study demonstrates that 3T MRI without a dedicated surface coil is an excellent tool for assessing plaque vulnerability. In smaller hospitals or those with limited resources, it is reasonable to consider conventional MRI for patient risk stratification. Further studies are needed to determine how MRI and plaque vulnerability can be incorporated into routine clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".