Response to Treatment in IgG4-Related Disease Assessed by Quantitative PET/CT Scan
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess disease activity by different PET/CT measurements in IgG4-related disease (IgG4-RD) flares and their correlation with the IgG4-RD responder index (IgG4-RI). PATIENTS AND METHODS: Patients were retrospectively recruited from a single center in Barcelona, Spain. They all had IgG4-RD flares with an 18F-FDG PET/CT examination performed within the 2 first weeks of the flare onset and another one after at least 3 months of treatment between 2012 and 2018. Epidemiologic, clinical, laboratory, and therapeutic data were collected at baseline and at follow-up. Semiquantitative and volumetric measurements from PET/CT explorations were recorded. In addition, a 5-point visual scale was (adapted Deauville score) trialed. The IgG4-RI was used as the criterion standard to assess response before and after treatment. RESULTS: Eighteen patients with a total of 23 flares were included. The median time to second PET/CT examination was 7 months. Remission (complete and partial) according to IgG4-RI was observed in 20 flares (87%). All PET/CT measurements (SUVmax and SUVmean, total lesion glycolysis, MTV, and adapted Deauville score) were statistically significantly lower on the follow-up evaluation, except for the size of the lesion. The correlation of all these parameters with the IgG4-RI was positive except for SUVmean and the size of the lesion. CONCLUSIONS: Semiquantitative, volumetric, and visual parameters in PET/CT scans correlated with response to treatment assessed by IgG4-RI. Volumetric and visual items are less subject to variations and could be used to improve activity scores and treatment strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".