18F-FDG PET in the evaluation of early response to therapy in pediatric bone Langerhans cell histiocytosis
Bibliographic record
Abstract
2015 Objectives Evaluation of early response to therapy in multisystem “risk” Langerhans cell histiocytosis (LCH) is important, to allow early switch in therapy to a more intensive protocol for poor responding. Assessment of early response in bone lesions is difficult as normalization of bone lesions on anatomic imaging modalities may take a long time. A few earlier studies have suggested that 18F FDG-PET may be a useful modality in this regard. The aim of this study is to evaluate the usefulness of PET for detecting early response to therapy in LCH bone lesions compared with radiography or CT. Methods The study included 16 children (9 male, 7 female) who underwent (18)F-FDG PET and radiography and/or CT scans after initiation of therapy in LCH. Follow-up (F/U) radiography/CT (interval range was 2-22 m.) was retrospectively reviewed to confirm the findings during treatment. Results From a total number of 28 bone lesions, 18F-FDG detected 13 lesions while 22 lesions were positive on radiography during the course of treatment. 7 lesions were similar on both studies. 15 bone lesions were positive on radiography and negative on PET. Out of these 15, nine normalized on F/U radiography/CT scan studies, and 2 were partially improved while 2 were stable. In 2 lesions F/U studies were not available. A negative PET predicted favourable response to therapy earlier than did radiography/CT in 9 out of 15 bone lesions (60%). A longer F/U is required to assess whether the remaining 6 patients also achieve a CR improving further the negative predictive value of the FDG-PET scan. Conclusions F18-FDG PET is a useful modality for early assessment of response to therapy of bone lesions and will likely be helpful in decisions regarding change of therapy in poor/non responders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".