Abstract 2352: 18F-fluorodeoxyglucose PET Can Identify The Inflamed and Vulnerable Carotid Plaque - A Prospective Study in Japan
Bibliographic record
Abstract
Objective - There are few studies that denote the validity of 8F-fluorodeoxyglucose (FDG) PET to detect the inflammation of severe (>70%) carotid artery stenosis in Asian populations. This study was aimed to clarify whether 18F-FDG PET can identify inflamed and vulnerable plaque at higher risk for subsequent ischemic stroke in Japanese patients with severe carotid artery stenosis. Methods - This prospective study included 33 patients with severe carotid artery stenosis between 2006 and 2011. Of these, 12 patients were symptomatic and other 21 were asymptomatic. There were 28 males and 5 females. Their mean age was 71.1 ± 8.2 years, ranging from 48 to 85. Their clinical data were precisely collected. All 33 patients underwent 18F-FDG PET and ultrasound sonography (US) to evaluate the plaque composition prior to carotid endarterectomy (CEA). FDG uptake was quantified by maximum standardized uptake values (SUV). Following surgery, the specimens were stained with the antibodies against CD68 and MMP-9. Results - High FDG uptake (SUV>2.0) was observed in 15 (45%) of 33 operated plaques. High FDG uptake significantly correlated with the other vascular disorders (P=0.048), echolucent plaque (P=0.041), lipid-rich plaque (P<0.001), CD68 expression (P<0.001), and MMP-9 expression (P=0.002). 18F-FDG PET showed significantly higher sensitivity and specificity to identify lipid-rich and CD68-positive plaque than US. However, high FDG uptake was not related to patients’ gender, age, and the degree of stenosis. There was no significant correlation between FDG uptake and symptomatic or asymptomatic patients. Conclusion - These findings strongly suggest that 18F-FDG PET would provide more valuable information to identify the inflamed, vulnerable plaque than conventional ultrasound in Japanese patients with severe carotid artery stenosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".