Baseline metabolic tumour burden improves risk stratification in Hodgkin lymphoma: A Children's Oncology Group study
Bibliographic record
Abstract
Summary The Children's Oncology Group AHOD0831 study used a positron emission tomography (PET) response‐adapted approach in high‐risk Hodgkin lymphoma, whereby slow early responders (SERs) received more intensive therapy than rapid early responders (RERs). We explored if baseline PET‐based characteristics would improve risk stratification. Of 166 patients enrolled in the COG AHOD0831 study, 94 (57%) had baseline PET scans evaluable for quantitative analysis. For these patients, total body metabolic tumour volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax) and peak SUV (SUVpeak) were obtained. MTV/TLG thresholds were an SUV of 2.5 (MTV2.5/TLG2.5) and 40% of the tumour SUVmax (MTV40%/TLG40%). TLG2.5 was associated with event‐free survival (EFS) in the complete cohort (p = 0.04) and in RERs (p = 0.01), but not in SERs (p = 0.8). The Youden index cut‐off for TLG2.5 was 1841. Four‐year EFS was 92% for RER/TLG2.5 up to 1841, 60% for RER/TLG2.5 greater than 1841, 74% for SER/TLG2.5 up to 1841 and 79% for SER/TLG2.5 greater than 1841. Second EFS for RER/TLG2.5 up to 1841 was 100%. Thus, RERs with a low baseline TLG2.5 experienced excellent EFS with less intensive therapy, whereas RERs with a high baseline TLG2.5 experienced poor EFS. These findings suggest that patients with a high upfront tumour burden may benefit from intensified therapy, even if they achieve a RER.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".