SUVs Versus Dynamic Pharmacokinetic [<sup>18</sup>F]Fluoro-Polyethylene Glycol-Folate Uptake Parameters in Joints of Rheumatoid Arthritis Patients at Baseline and at 4 Weeks of Antitumor Necrosis Factor Therapy
Bibliographic record
Abstract
Quantitative assessment of rheumatoid arthritis (RA) activity using [18F]fluoro-polyethylene glycol (PEG)–folate PET/CT scans may prove a useful noninvasive therapeutic response assessment tool to evaluate antitumor necrosis factor therapy in RA patients. This study aims to assess [18F]fluoro-PEG-folate kinetics through a metabolite-corrected plasma input model and to investigate comparisons with simplified quantitative PET outcome measures. Methods: Dynamic [18F]fluoro-PEG-folate PET/CT scans were obtained for 6 patients for a total of 11 scans, 6 before and 5 after treatment. These scans were analyzed using conventional pharmacokinetic models. In addition, SUVs were calculated at intervals of 10–40, 20–50, 30–60, and 40–60 min after injection for comparison and imaging window optimization. Results: [18F]fluoro-PEG-folate kinetics in joints of RA patients were best described using the reversible pharmacokinetic 2-tissue compartment model with a volume of distribution (VT) mean of 1.0 (±0.5). VT values correlated between arterial and venous samples at both baseline (P < 0.001, r2 = 0.96) and 4 wk after antitumor necrosis factor treatment (P < 0.001, r2 = 0.75), both at intervals of 30–60 and 40–60 min. Changes in VT behavior during treatment could not be accurately assessed because of limited available data, but observed changes in the linear association slope may indicate changed kinetic behavior. Conclusion: The most optimal kinetic model for [18F]fluoro-PEG-folate uptake in joints of RA patients was the reversible 2-tissue compartment model. The associations between VT and a simplified SUV interval of 30–60 min allow us to quantify tracer uptake without the need for a full cross-sectional pharmacokinetic evaluation at the time of imaging. Further research will be required to accurately assess the change in tracer behavior between time points and the use of simplified assessment of changes of tracer uptake in joints over time.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".