Biology of classical Hodgkin lymphoma: implications for prognosis and novel therapies
Bibliographic record
Abstract
Hodgkin lymphoma is considered a prime example of treatment success, with cure rates exceeding 80% using modern combined modality therapies. However, especially in adolescents and young adults, treatment-related toxicity and long-term morbidity still represent persistent challenges. Moreover, outcomes in patients with relapsed or refractory disease remain unfavorable in the era of high-dose chemotherapy and stem-cell transplantation. Hence, there is a high demand for novel and innovative alternative treatment approaches. In recent years, many new therapeutic agents have emerged from preclinical and clinical studies that target molecular hallmarks of Hodgkin lymphoma, including the aberrant phenotype of the tumor cells, deregulated oncogenic pathways, and immune escape. The antibody-drug conjugate brentuximab vedotin and immune checkpoint inhibitors have already shown great success in patients with relapsed/refractory disease, leading to US Food and Drug Administration approval and new trials testing these agents in various clinical settings. The expanding knowledge and understanding of Hodgkin lymphoma biology and disease progression, as well as the development of robust tools for biomarker-driven risk stratification and therapeutic decision making, continue to be fundamentally important for the success of these and other novel agents. We anticipate that the availability and clinical implementation of novel molecular assays will be instrumental in an era of rapid shifts in the treatment landscape of this disease. Here, we review the current knowledge of Hodgkin lymphoma pathobiology, highlighting the related development of novel treatment strategies and prognostic models that hold the promise to continually challenge and change the current standard of care in classical Hodgkin lymphoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".