Comparison of [18F]fluorodeoxyglucose and [68Ga]Gallium DOTA-TATE in patients with active giant cell arteritis
Bibliographic record
Abstract
[18F]Fluorodeoxyglucose (FDG) is widely used in PET/CT imaging to detect large vessel vasculitis in giant cell arteritis (GCA), but its performance is suboptimal in patients receiving glucocorticoids. We aimed to compare [68Ga]Ga-HA-DOTA-TATE, a somatostatin 2-analogue tracer, to [18F]FDG in a pilot study of patients with GCA. Eight patients with active GCA were prospectively, sequentially scanned with both [18F]FDG PET/CT and [68Ga]Ga-HA-DOTA-TATE PET/CT imaging. Images were evaluated by 2 blinded nuclear medicine specialists. Tracer uptake was assessed in 8 vascular territories using SUVmax, and target-background ratios (TBR) were calculated using both right atrium (TBRRA) and liver mean (TBRliver). Mean SUVmax and TBR of individual vascular territories and index vessels were compared. The patient median age was 71.5 years (range 64–82), and 4 (50%) were women. Active vasculitis (≥ grade 2 visual uptake in large vessels) was present in 62.5% of [18F]FDG scans. [18F]FDG scans had higher RA background activity than [68Ga]Ga-HA-DOTA-TATE (mean RA SUVmean 1.88 vs. 0.36, p < 0.001), while [68Ga]Ga-HA-DOTA-TATE had a significantly higher liver uptake (mean liver SUVmean 7.54 vs. 2.39, p < 0.001). Vascular uptake (as measured by both SUVmax and TBRliver) was significantly higher in [18F]FDG than [68Ga]Ga-HA-DOTA-TATE scans in every vascular territory (p < = 0.05 for all comparisons), including index vessels (SUVmax 4.04 vs. 1.91, p = 0.01, TBRliver 1.73 vs. 0.27, p < 0.001). In this pilot study of patients with active GCA, the arterial uptake of [68Ga]Ga-HA-DOTA-TATE was lower and less conspicuous compared to [18F]FDG. While further evaluation in larger cohorts is needed, a clear advantage of [68Ga]Ga-HA-DOTA-TATE over [18F]FDG for detecting vascular inflammation in GCA was not identified. NCT 03812302, registered 2019-01-18, URL: https://clinicaltrials.gov/search?cond=dotatate%20%26;term=giant%20cell%20arteritis .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".