F18-FDG PET-CT Findings in Juvenile-Onset Polyarteritis Nodosa: A First Series and Literature Review
Bibliographic record
Abstract
Objectives: To investigate the findings associated with juvenile polyarteritis nodosa (PAN) on F18-FluoroDeoxyglucose (FDG), positron emission tomography combined with computed tomography (PET-CT). Methods: Patients diagnosed with juvenile PAN (onset <18 years) who underwent a PET-CT at diagnosis (before therapy) were enrolled. PET-CT images were systematically analyzed to identify abnormal findings associated with PAN. In addition, a systematic literature review was performed to identify previously published cases. Results: Six patients with biopsy-confirmed PAN were identified (age at onset 10–17 years). PET-CT was abnormal in all patients. Patchy muscular and subcutaneous FDG uptake with a symmetric distribution in the lower limbs was present in 4/6 patients. Increased FDG uptake in large arteries was found in 1/6 patients. FDG-avid bone lesions were identified in 2/6; additional MRI and bone biopsy results were consistent with chronic non-infectious osteomyelitis (CNO). Unspecific inflammatory findings (medullar and lymphoid organs hypermetabolism) were present in 6/6; these were the only abnormalities present in 2/6 patients. We found this pattern of PET-CT muscular involvement to differ from juvenile dermatomyositis and septic emboli (n = 7 and 2 patients, respectively). In addition, we identified four previously published cases of juvenile PAN investigated with PET-CT: one with FDG-avid muscular and subcutaneous foci, one with increased uptake in large arteries, and two with nonspecific signs (lymphoid organs hypermetabolism). Conclusions: This is the first series of juvenile PAN investigated with PET-CT. Diffuse, patchy hypermetabolic foci in the muscular and subcutaneous tissue of the lower limbs were the most common findings. These features should lead to suspicion of PAN. Further research is needed to assess the diagnostic value of PET-CT in PAN.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".