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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".