The role of FDG-PET scanning and PET-adapted therapy in the primary treatment of Hodgkin lymphoma: A primer for clinicians
Bibliographic record
Abstract
The evolving treatment paradigm for classical Hodgkin lymphoma (cHL) remains focused on maintaining high rates of progression-free survival (PFS) and overall survival (OS), while seeking to reduce both short-term and late toxicities from chemotherapy and radiation. Functional imaging with fluoro-deoxyglucose (FDG)‑positron emission tomography (PET) combined with computed tomography (CT) is recognized as standard for staging and response evaluation of Hodgkin lymphoma (HL). Recent randomized controlled trials evaluating FDG-PET-guided therapy for patients with limited stage and advanced stage Hodgkin lymphoma provide clinicians and patients with meaningful data upon which to base individualized treatment approaches. FDG‑PET scanning after two cycles of therapy (interim PET or PET2) represents the most important determinant of further appropriate treatment and subsequent outcomes, and is now the cornerstone of risk-adapted therapy for all patients receiving curative-intent initial therapy for Hodgkin lymphoma. For patients with limited stage cHL, post-chemotherapy assessment (after two or four cycles of treatment depending on the regimen used) is also a key determinant of the need for the addition of involved site or nodal radiation as part of combined modality therapy. This review summarizes the important role of interim and end of chemotherapy FDG-PET scanning to guide individualized initial therapy for patients to achieve optimal treatment outcomes.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".