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Record W4408031283 · doi:10.1186/s41824-025-00242-y

Comparison of [18F]fluorodeoxyglucose and [68Ga]Gallium DOTA-TATE in patients with active giant cell arteritis

2025· article· en· W4408031283 on OpenAlexafffund
Alison Clifford, Jonathan Abele, Ryan Hung, Frank Wuest, Jan Andersson, Susan Pike, Elaine Yacyshyn, Eric Lenza, Glen Jickling, Paolo Raggi, Jan Willem Cohen Tervaert

Bibliographic record

VenueEJNMMI Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsAlberta Health ServicesTranslational Research in OncologyUniversity of Alberta
FundersUniversity Hospital Foundation
KeywordsGiant cell arteritisMedicineNuclear medicineFluorodeoxyglucosePositron emission tomographyPathologyVasculitisDisease

Abstract

fetched live from OpenAlex

[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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.249
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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