Efficacy of Frontline Chemotherapy for Extranodal Natural Killer/T-Cell Lymphoma: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Background: Treatment with non-anthracycline (ANT)-based chemotherapy has increased survival in patients with extranodal natural killer/T-cell lymphoma (ENKTCL). However, the relative efficacy of various drug combinations has been contentious. We aimed to identify the most effective chemotherapy regimens for newly diagnosed ENKTCL. Methods: A network meta-analysis was performed to evaluate the differences in survival and treatment responses across various regimens. The primary objective was overall survival (OS), while secondary outcomes included progression-free survival (PFS), objective response rate (ORR), and complete response (CR). We utilized a Bayesian framework to perform the network meta-analysis. Rank probabilities were assessed by the surface under the cumulative ranking curve (SUCRA). Node-splitting method was used to assess the inconsistency. Results: A total of 1,113 patients were enrolled across 10 studies. Chemotherapy regimens were grouped into five modalities, for which six types of direct comparisons were available. We identified the asparaginase (ASP)/gemcitabine (GEM)-based regimens superiority over ANT-based, non-ASP/ANT-based and ASP/methotrexate (MTX)-based regimens on OS. Although no significant differences were observed compared with ASP/not otherwise specified-based, ASP/GEM-based regimens were still the best option chemotherapy for OS. Moreover, the ASP/GEM-based regimens demonstrated advantages in PFS, ORR and CR. Conclusions: According to our network meta-analysis, it appears that ASP/GEM-based regimens could potentially serve as the most effective frontline chemotherapy option for ENKTCL.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".