The Addition of Rituximab Eliminates the Negative Prognostic Impact of PMBCL Compared to DLBCL in Young Patients with CD20-Positive Aggressive Lymphomas Receiving a CHOP-Like Chemotherapy: Results of a Subgroup Analysis of the Mabthera International Trial Group (MInT) Study
Bibliographic record
Abstract
Abstract PMBCL is considered to be a distinct entity among diffuse large B-cell lymphoma (DLBCL). The optimal therapy is still matter of debate and the impact of the addition of rituximab to conventional chemotherapy is unknown. The aim of this subgroup analysis of the MInT study was to evaluate the effect of rituximab in PMBCL in comparison to other DLBCL with mediastinal involvement (mDLBCL). Eligible for the MInT study (Lancet Oncol2006; 7: 379–91) were patients aged 18–60 years with DLBCL who had 0–1 risk factors according to age-adjusted International Prognostic Index (IPI), stage II-IV disease, or stage I disease with bulk. Patients were randomly assigned to six cycles CHOP-like chemotherapy regimens with or without rituximab. Consolidating radiotherapy (30–40 Gy) was given to sites of primary bulky disease. Results: Of 824 patients enrolled, 87 had PMBCL and 139 mDLBCL according to nationally centralized hematopathologist review. 44 patients (51%) with PMBCL and 65 patients (47%) with mDLBCL were randomized to the rituximab arm. The subsets were balanced for IPI, but patients with PMBCL were younger (median age 35 vs 43 years); showed more frequently an elevated LDH (63% vs 33%), stage I/II disease (92% vs 65%) and bulky disease (85% vs 60%); and received more often mediastinal radiotherapy (69% vs 37%) compared with mDLBCL. Rituximab increased the rates of complete remission or complete remission unconfirmed (CR/CRu) in both subsets although this was statistically significant only for PMBCL (see Table). This was mainly due to the fact that in both subsets rituximab virtually eliminated progressive disease (PD) under primary treatment, whereas without rituximab, PD tended to be more frequent in PMBCL than in mDLBCL (24% vs 11%, p=0.09). With a median observation time of 37 months (range 0–59), estimated 3-year event-free-survival (EFS) was improved by rituximab for PMBCL (78% vs 52%, p=0.012), mDLBCL (74% vs 59%, p=0.130), and all other DLBCL (n=597, 80% vs 60%, p<0.0001). Rituximab also improved progression-free-survival (PFS) of PMBCL, but not of mDLBCL (Table). This could be explained in part by the favorable outcome of mDLBCL compared to PMBCL in the chemotherapy-only arm (3-year PFS 77% vs 64%, p=0.08). In both subsets, the overall survival (OS) benefit observed with rituximab did not reach statistical significance (Table). Multivariable cox regression models (adjusting for treatment arm; IPI; etoposide use (CHOEP21); bulky disease; histology (PBMCL vs mDLBCL); and the interactions between histology and treatment arm, and histology and IPI, respectively) identified rituximab as a strong prognostic factor and a trend to an interaction between histology and treatment arm for the outcome of a patient. Conclusion: In young patients with PMBCL, rituximab added to 6 cycles of CHOP-like chemotherapy increases response rate, EFS, and PFS to the same extent as in other DLBCL, thereby eliminating the outcome disadvantage of PMBCL observed with CHOP-like chemotherapy alone. PMBCL Rituximab CR/CRu PD 3y-EFS 3y-PFS 3y-OS No 54% (38%–70%) 24% (11%–38%) 52% (35%–66%) 64% (46%–77%) 78% (61%–88%) Yes 80% (68%–92%) 3% (0%–7%) 78% (61%–88%) 88% (70%–95%) 89% (71%–96%) P-value 0.03 0.006 0.012 0.006 0.16 mDLBCL Rituximab CR/Cru PD 3y-EFS 3y-PFS 3y-OS No 67% (55%–78%) 11% (3%–18%) 59% (46%–70%) 77% (64%–86%) 89% (79%–95%) Yes 81% (71%–91%) 2% (0%–5%) 74% (60%–83%) 81% (68%–89%) 92% (79%–97%) P-value 0.1 0.07 0.13 0.71 0.47
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".