Treatment approaches in relapsed or refractory peripheral T-cell lymphomas
Bibliographic record
Abstract
Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of rare and aggressive non-Hodgkin's lymphomas. Clinical staging, prognostic scoring, and initial treatment strategies have historically been based on paradigms developed in B-cell lymphomas. Despite primary treatment protocols that are typically anthracycline-based and frequently involve consolidative autologous stem cell transplantation in first remission, many patients develop disease progression. There remains a high unmet medical need for improved treatment strategies in the relapsed or refractory setting. Salvage chemotherapy and stem cell transplantation in those who are suitable has traditionally been the accepted approach, but this remains a minority of the total patient population. As increasing knowledge is gleaned regarding the biological heterogeneity within the various PTCL subtypes, newer targeted agents have been developed, studied, and approved in this small, heterogeneous population of relapsed or refractory disease. Given its success and tolerability in this pretreated population, brentuximab vedotin, an anti-CD30 antibody drug conjugate, was brought earlier in the disease course and is a model for advances in the targeted treatment of PTCL. As others undergo further development in the relapsed setting and successes are brought earlier in the disease course, the outcome for PTCL patients is likely to improve. However, innovative clinical trial designs are crucial for the assessment of targeted agents in this highly heterogeneous population. This review explores the current treatment environment for patients with relapsed and refractory PTCL, including newer strategies such as targeted agents and immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".