Distribution and predictors of F-18-FDG uptake values of non-malignant cervical lymph nodes in pediatric patients
Bibliographic record
Abstract
Abstract Background F-18-flurodeoxyglucose (FDG) PET/CT is routinely used for staging, evaluation of response to treatment and follow-up of most pediatric malignancies. Cervical lymph nodes can be involved in some pediatric malignancies, but increased uptake in non-malignant cervical lymph nodes is not exceptional in this population. The aim of the present study is to identify predictors of the maximum uptake in non-malignant cervical lymph nodes in the pediatric population. Methods 191 FDG PET/CT studies of pediatric patients without malignant involvement of cervical lymph nodes were retrospectively reviewed. The maximal Standard Uptake Value in the hottest cervical lymph node (SUVmaxCLN), as well as demographic, technical and imaging variables were recorded. The predictive effect of those variables on SUVmaxCLN was estimated using linear regression models. Results Increased FDG activity in cervical nodes was observed in 136/191 studies (71%). The mean SUVmaxCLN was 2.2 ± 1.3. Ipsilateral palatine tonsil SUVmax, mean liver uptake, and treatment status were all statistically significant predictors of SUVmaxCLN. However, in multivariate regression analysis, only ipsilateral palatine tonsil SUVmax was found to be significant. In addition, SUVmaxCLN was greater than the mean liver uptake in 50% of all studies. This proportion was higher in younger children, reaching 77% of studies of children younger than six years. Conclusion SUVmax in ipsilateral palatine tonsil is a strong predictor of the maximal uptake value of non-malignant cervical lymph nodes in children. The intensity of uptake in non-malignant cervical lymph nodes is frequently higher than liver uptake in children, and this tendency increases for younger patients. Trial was registered In the internal hospital registry under TRN 0209-22-HMO on date 23.04.2022.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".