Current and Future Strategies for Relapsed Neuroblastoma
Bibliographic record
Abstract
More than half of the patients with high-risk neuroblastoma (NB) will relapse despite intensive multimodal therapy, with an additional 10% to 20% refractory to induction chemotherapy. Management of these patients is challenging, given disease heterogeneity, resistance, and organ toxicity including poor hematological reserve. This review will discuss the current treatment options and consider novel therapies on the horizon. Cytotoxic chemotherapy regimens for relapse and refractory NB typically center on the use of the camptothecins, topotecan and irinotecan, in combination with agents such as cyclophosphamide and temozolomide, with objective responses but poor long-term survival. I-meta-iodobenzylguanidine therapy is also effective for relapsed patients with meta-iodobenzylguanidine-avid disease, with objective responses in a third of cases. Immunotherapy with anti-GD2 has recently been incorporated into upfront therapy, but its role in the relapse setting remains uncertain, especially for patients with bulky disease. Future cell-based immunotherapies and other approaches may be able to overcome this limitation. Finally, many novel molecularly targeted agents are in development, some of which show specific promise for NB. Successful incorporation of these agents will require combinations with conventional cytotoxic chemotherapies, as well as the development of predictive biomarkers, to ultimately personalize approaches to patients with "targetable" molecular abnormalities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".