Efficacy and Safety of Chimeric Antigen Receptor (CAR)-T Cell Therapy in Patients with Non-Hodgkin Lymphoma
Bibliographic record
Abstract
OBJECTIVES: Non-Hodgkin lymphomas (NHL) are a diverse group of lymphoproliferative malignancies, often more unpredictable than Hodgkin lymphomas, with a higher likelihood of extranodal spread. NHL's resistance to standard chemotherapy has increased, leading to a growing interest in personalized treatments like chimeric antigen receptor T-cell therapies (CAR-TCT). METHODS: A literature search was conducted across PubMed, ScienceDirect, Google Scholar, and the Cochrane Library for studies on CAR-TCT in NHL treatment published until July 2024. The outcomes assessed included overall survival (OS), event-free survival (EFS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). Data were pooled using RevMan 5.41 and Comprehensive Meta-analysis 3. RESULTS: Out of 532 articles, 8 met the inclusion criteria. CAR-TCT significantly improved OS (HR: 0.79; 95% CI: 0.63-1.00; P =0.05) and PFS (HR: 0.46; 95% CI: 0.36-0.58; P <0.00001) compared with standard chemotherapy. However, EFS was not significantly different (HR: 0.54; 95% CI: 0.26-1.09; P =0.09). About 76.6% of NHL patients responded to CAR-TCT, but the ORR was similar between CAR-TCT and standard therapy (MD: 19.23%; 95% CI: -11.34% to 49.80%; P =0.22). Safety analysis found a grade ≥3 AEs incidence comparable to CAR-TCT and standard care. However, CAR-TCT was associated with higher neutropenia risk but lower thrombocytopenia, anemia, and nausea risks. CONCLUSION: CAR-TCT significantly improves OS and PFS in refractory NHL but does not notably impact EFS. While its ORR is comparable to standard chemotherapy, CAR-TCT has a better safety profile, making it a promising treatment option.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".