<scp>FDG</scp>‐<scp>PET</scp>/<scp>CT</scp> in the management of lymphomas: current status and future directions
Bibliographic record
Abstract
FDG-PET/CT is the current state-of-the-art imaging in lymphoma and plays a central role in treatment decisions. At diagnosis, accurate staging is crucial for appropriate therapy selection: FDG-PET/CT can identify areas of lymphoma missed by CT alone and avoid under-treatment of patients with advanced disease stage who would have been misclassified as having limited stage disease by CT. Particularly in Hodgkin lymphoma, positive interim FDG-PET/CT scans are adversely prognostic for clinical outcomes and can inform PET-adapted treatment strategies, but such data are less consistent in diffuse large B-cell lymphoma. The use of quantitative FDG-PET/CT metrics using metabolic tumour volume, possibly in combination with other biomarkers, may better define prognostic subgroups and thus facilitate better treatment selection. After chemotherapy, FDG-PET/CT response is predictive of outcome and may identify a subgroup who benefit from consolidative radiotherapy. Novel therapies, in particular immunotherapies, exhibit different response patterns than conventional chemotherapy, which has led to modified response criteria that take into account the risk of transient pseudo-progression. In relapsed lymphoma, FDG-PET/CT after second-line therapy and prior to high-dose therapy is also strongly associated with outcome and may be used to guide intensity of salvage therapy in relapsed Hodgkin lymphoma. Currently, FDG-PET/CT has no role in the routine follow-up after complete metabolic response to therapy, but it remains a powerful tool for excluding relapse if patients develop clinical features suggestive of disease relapse. In conclusion, FDG-PET/CT plays major roles in the various phases of management of lymphoma and constitutes a step towards the pursuit of personalized treatment.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".