Bendamustine or high-dose cytarabine-based induction with rituximab in transplant-eligible mantle cell lymphoma
Bibliographic record
Abstract
The objective of this study was to explore differences in outcomes between first-line rituximab plus bendamustine (R-B) and R-CHOP/R-DHAP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone, dexamethasone, cytarabine, cisplatin) in transplant-eligible patients with mantle cell lymphoma (MCL). A population-based cohort of 97 patients aged 18 to 65 years with stage II-IV MCL, consecutively treated with R-B was retrospectively identified at BC Cancer. Baseline characteristics, response rates, and outcomes were compared with the cohort of 232 patients with MCL randomized to the R-CHOP/R-DHAP arm of the MCL Younger trial. The primary endpoint was the hazard ratio (HR) of the progression-free survival (PFS) comparison between both groups, adjusted for MCL International Prognostic Index (MIPI), Ki67 index, and blastoid/ pleomorphic morphology. Ann Arbor stage, lactate dehydrogenase, MIPI, blastoid morphology, and MCL35 assignments were similar between both groups. The overall response rate (ORR) to R-B was 90% (54% complete response [CR]); 77% of patients proceeded to autologous stem cell transplantation (ASCT) and 78% received maintenance rituximab (MR). The ORR to R-CHOP/R-DHAP was 94% (54% CR); 78% proceeded to ASCT and 2% received MR. There were no differences in PFS in unadjusted (HR, 0.87; 95% confidence interval [CI], 0.53-1.41; P = .56) or adjusted (HR, 0.79; 95% CI, 0.45-1.37; P = .40) comparisons. There were no clear differences in secondary endpoints in unadjusted or adjusted analyses. This retrospective adjusted comparison of 2 independent cohorts of younger patients with MCL suggests that R-B with ASCT and maintenance rituximab is a feasible and effective first-line treatment, with outcomes comparable to R-CHOP/R-DHAP with ASCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".