The value of positron emission tomography in prognosis and response assessment in non-Hodgkin lymphoma
Bibliographic record
Abstract
Positron emission tomography (PET) is widely used for post-treatment response assessment in lymphoma. However, the role of PET in prognosis and early response assessment is still being defined. Studies have shown that PET can identify response early during chemotherapy and that interim PET can predict outcome in diffuse large B-cell lymphoma (DLBCL). Whether the results of early or post-treatment response assessment can be used to determine prognosis and/or guide therapeutic decisions, known as response-adapted therapy, is currently being investigated, with considerable promise in certain lymphomas such as Hodgkin lymphoma (HL) and DLBCL. The use of interim PET is currently limited by a lack of standardized imaging protocols and reporting criteria, and unproven reproducibility of interpretation. As a result, until further data are generated and a consensus reached, interim PET should be considered investigational and applied only within the confines of clinical studies. This review provides an overview of the use of PET for prognosis and response assessment, and in response-adapted therapy. Current limitations of the technique will be summarized, and innovative uses of PET in grading, staging, and surveying lymphomas will be briefly explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".