Variants of physiological FDG vascular activity on digital PET
Bibliographic record
Abstract
OBJECTIVE: Fluorodeoxyglucose PET/computed tomography (FDG PET/CT) is effective in detecting large vessel vasculitis. Digital PET cameras have improved spatial resolution compared with analog PET, resulting in more prominent physiological uptake in arterial walls. This study's goal was to define qualitative normal variants of arterial activity on digital PET/CT. METHODS: We retrospectively reviewed 126 oncological PET/CT studies. Exclusion criteria included history of vasculitis, immunosuppressant therapy, hyperglycemia, or altered FDG biodistribution. Qualitative vessel wall activity (common carotid, brachiocephalic, subclavian, aorta, and femoral) was visually graded by two nuclear physicians with guideline-proposed criteria: 0: ≤mediastinum, 1: <liver, 2: = liver, 3: >liver, where grade 3 is compatible, 2 is possible, and <2 is negative for vasculitis. Cranial artery uptake was visually graded as follows: grade 0: ≤surrounding tissues, grade 1: just above surrounding tissues, and grade 2: significantly above surrounding tissues, with grades 1 and 2 considered positive for cranial artery vasculitis. RESULTS: Large vessel uptake was grade 3 in 0 subjects, grade 2 in four subjects (3%), grade 1 in 87 subjects (69%), and grade 0 in 35 subjects (28%). In studies acquired ≥75 min post-injection, 1/15 subjects had grade 2 uptake. Four subjects (3%) had grade 1 vertebral artery uptake. No subjects had temporal, maxillary, or occipital artery uptake. CONCLUSION: A minority of our subjects presented with grade 2 large vessel uptake, which was associated with longer uptake times, or grade 1 cranial artery uptake, which was associated with higher age and glycemia. These findings should be interpreted with caution in patients referred for suspected vasculitis, as they may represent normal variants on digital PET.
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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) | 0.000 | 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 | 0.001 |
| 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 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".