Added prognostic value of baseline pre-infusion 18F-FDG PET/CT in diffuse large B-cell lymphoma patients receiving chimeric antigen receptor T-cell therapy
Bibliographic record
Abstract
In this Research Ethics Board-approved retrospective study, we evaluated pre-infusion [ 18 F]FDG PET/CT prognostic value in relapsed/refractory diffuse large B-cell lymphoma (DLBCL) patients undergoing chimeric antigen T-cell (CAR-T) therapy. A total of 159 Patients treated with CAR-T between 2018 and 2023 were reviewed. Deauville scores 4 and 5 were considered to be a significant residual disease at baseline. Standardized uptake values (SUVs), whole-body metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were calculated. Additionally, the furthest distance between tumoral lesions throughout the body (Dmax) and from the spleen (spleen Dmax) were measured. Survival analyses evaluated the predictive value of the clinical and imaging-derived variables for progression-free survival (PFS) and overall survival (OS) prognostication. Of 129 DLBCL patients with pre-infusion [ 18 F]FDG PET/CT, 117/129 (91%) had significant residual disease. The median PFS and OS post-CAR-T were six and nine months, respectively. For PFS, variables that remained significant in the multivariate analysis were serum LDH (HR = 1.68) and TLG (HR = 4.31), being independent predictors of PFS. Considering OS, the only variable which retained its significance in the multivariate analysis was [ 18 F]FDG PET/CT-derived standardized Dmax (HR = 3.28). Pre-infusion [ 18 F]FDG PET/CT can provide valuable prognostic information in CAR-T candidates, enhancing patient management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".