Is True Whole-Body<sup>18</sup>F-FDG PET/CT Required in Pediatric Lymphoma? An IAEA Multicenter Prospective Study
Bibliographic record
Abstract
Guidelines recommend true whole-body 18F-FDG PET/CT scans from vertex to toes in pediatric lymphoma patients, although this suggestion has not been validated in large clinical trials. The objective of the study was to evaluate the incidence and clinical impact of lesions outside the “eyes to thighs” regular field of view (R-FOV) in 18F-FDG PET/CT staging (sPET) and interim (iPET) scans in pediatric lymphoma patients. Methods: True whole-body sPET and iPET scans were prospectively obtained in pediatric lymphoma patients (11 worldwide centers). Expert panel central review of sPET and iPET scans were evaluated for lymphoma lesions outside the R-FOV and clinical relevance of these findings. Results: A total of 610 scans were obtained in 305 patients. The sPET scans did not show lesions outside the R-FOV in 91.8% of the patients, whereas in 8.2% patients the sPET scans demonstrated lesions also outside the R-FOV (soft tissue, bone, bone marrow, and skin); however, the presence of these lesions did not change the clinical stage of any patient and did not affect treatment decision. Among the 305 iPET scans, there were no new positive 18F-FDG–avid lesions outside the R-FOV, when compared with their paired sPET scans. A single lesion outside the R-FOV on iPET occurred in 1 patient (0.3%), with the primary lesion diagnosed in the femur on sPET that persisted on iPET. Conclusion: The identification of additional lesions outside the R-FOV (eyes to thighs) using 18F-FDG PET/CT has no impact in the definition of the clinical stage of disease and minimal impact in the treatment definition of patients with pediatric lymphoma. As so, R-FOV for both sPET and iPET scans could be performed.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".