Randomized study of induction with bendamustine-rituximab ± bortezomib and maintenance with rituximab ± lenalidomide for MCL
Bibliographic record
Abstract
ABSTRACT: Although initial therapy of mantle cell lymphoma (MCL) is not standardized, bendamustine plus rituximab (BR) is commonly used in older patients. Rituximab (R) maintenance after induction is often used. Thus, the open-label, randomized phase 2 ECOG-ACRIN Cancer Research Group E1411 trial was designed to test 2 questions: (1) does addition of bortezomib to BR induction (BVR) and/or (2) addition of lenalidomide to rituximab (LR) maintenance improve progression-free survival (PFS) in patients with treatment-naïve MCL? From 2012 to 2016, 373 previously untreated patients, 87% aged ≥60 years, were enrolled in this trial. At a median follow-up of 7.5 years, there is no difference in the median PFS of BR compared with BVR (5.5 vs 6.4 years; hazard ratio [HR], 0.90; 90% confidence interval [CI], 0.70-1.16). There were no unexpected additional toxicities with BVR treatment compared with BR, with no impact on total dose/duration of treatment received. Independent of the induction treatment, addition of lenalidomide did not significantly improve PFS, with median PFS in R vs LR (5.9 vs 7.2 years; HR, 0.84; 90% CI, 0.62-1.15). Most patients completed the planned 24 cycles of LR at the scheduled dose. In summary, adding bortezomib to BR induction does not prolong PFS in treatment-naïve MCL, and LR maintenance was not associated with longer PFS compared with R alone after BR. Nonetheless, the >5-year median PFS outcomes in this prospective cooperative group trial indicate the efficacy of BR followed by R maintenance as highly effective initial therapy for older patients with MCL. This trial was registered at www.clinicaltrials.gov as #NCT01415752.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".