Past, present and future therapeutic approaches in nodal peripheral T-cell lymphomas
Bibliographic record
Abstract
Peripheral T-cell lymphomas (PTCL) encompass over 30 different entities and although they share post-thymic T- or NK-cell derivation, the disease biology and genomic landscape are very diverse across subtypes. In Western populations, nodal PTCL are the most frequently encountered entities in clinical practice and although important achievements have been made in deciphering the underlying biology and in therapeutic advances, there are still large gaps in disease understanding and clinical scenarios in which controversy over best practice continues. CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone)- based chemotherapy continues to be the 'standard' treatment, with the addition of brentuximab vedotin (BV) in the combination CHP (cyclosphosphamide, doxorubicin, prednisone)-BV representing a new treatment paradigm in CD30+ PTCL although its benefit is less certain in the non-anaplastic large cell lymphoma subtypes. Given the high risk of relapse, consolidative autologous stem cell transplant is considered in nodal PTCL, outside of ALK-positive anaplastic large cell lymphoma; however, in the absence of a randomized controlled trials, practices vary. Beyond CHP-BV, most study activity has focused on adding a novel agent to CHOP (i.e., CHOP + drug X). However, with high complete remission rates observed with some novel therapy combinations, these regimens are being tested in the front-line setting, with a particular rationale in follicular helper T-cell lymphomas which have a clear sensitivity to epigenetic modifying therapies. This is well exemplified in the relapsed/refractory setting in which rational combination therapies are being developed for specific subtypes or guided by underlying biology. Taken together, we have finally moved into an era of a more personalized approach to the management of nodal PTCL.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".