Role of FDG PET/CT in Patients With Lymphoma Treated With Chimeric Antigen Receptor T-Cell Therapy: Current Concepts
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is a cellular therapy in which the patient's T cells are enhanced to recognize and bind to specific tumor antigens. CAR T-cell therapy was initially developed for the treatment of leukemia, but its current main indication is the treatment of relapsed or refractory non-Hodgkin lymphoma. FDG PET/CT plays a fundamental role in the diagnosis, staging, therapy response assessment, and recurrence evaluation of patients with metabolically active lymphoma. Consistent with the examination's role in lymphoma management, FDG PET/CT is also the imaging modality of choice to evaluate patients before and after CAR T-cell therapy, and evidence supporting its utility in this setting continues to accumulate. In this article, we review current concepts in CAR T-cell therapy in patients with lymphoma, emphasizing the critical role of FDG PET/CT before and after therapy. A framework is presented that entails performing FDG PET/CT at four time points over the course of CAR T-cell therapy: pretherapy at baseline at the time of decision to administer CAR T-cell therapy and after any bridging therapies and posttherapy 1 and 3 months after infusion. PET parameters assessed at these time points predict various patient 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".