Outcome of primary mediastinal large B-cell lymphoma using R-CHOP: impact of a PET-adapted approach
Bibliographic record
Abstract
Cure rates for primary mediastinal large B-cell lymphoma (PMBCL) have improved with the integration of rituximab. However, the type of primary therapy and role of radiotherapy (RT) remains ill-defined. Herein, we evaluated the outcome of PMBCL primarily treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) and the impact of an end-of-treatment (EOT) 18F-fluorodeoxyglucose positron emission tomography (PET) scan to guide consolidative RT. Patients ≥18 years of age with PMBCL treated with curative intent rituximab-chemotherapy were identified. Prior to 2005, patients were recommended to receive R-CHOP + RT (RT era). Beginning in 2005, EOT PET was used to guide RT and only those with a PET-positive scan received RT (PET era). In total, 159 patients were identified, 94% were treated with R-CHOP and 44% received RT (78% in RT era, 28% in PET era). The 5-year time to progression (TTP) and overall survival (OS) for the entire cohort were 80% and 89%, respectively, similar across treatment eras. Overall, 10% had refractory disease. In total, 113 patients had an EOT PET scan: 63% negative and 37% positive with a 5-year TTP of 90% vs 71% and 5-year OS of 97% vs 88%, respectively. For those with Deauville (D)-scored PET scans (n = 103), the 5-year TTP for PET-negative cases by Deauville criteria (D1-D3, DX) was 91%, with inferior outcomes for D5 vs D4 (5-year TTP 33% vs 87%, P = .0002). Outcomes for PMBCL treated with RCHOP are favorable and use of a PET-adapted approach reduces RT in the majority of patients. A small proportion have refractory disease and may benefit from an alternate 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".