Central nervous system relapse in high-risk stage 4 neuroblastoma: The HR-NBL1/SIOPEN trial experience
Bibliographic record
Abstract
BACKGROUND: There is rising concern on the impact of new strategies, such as high-dose chemotherapy (HDC) and immunotherapy, on the pattern of relapse in high-risk neuroblastoma (HR-NBL). Our aim is to evaluate the incidence and identify risk factors for first recurrence in the central nervous system (CNS) in HR-NBL. PATIENTS AND METHODS: Data from patients with stage 4V HR-NBL included from February 2002 to June 2015 in the prospective HR-NBL trial of the European International Society of Pediatric Oncology Neuroblastoma Group were analysed. Characteristics at diagnosis, treatment and the pattern of first relapse were studied. CNS imaging at relapse was centrally reviewed. RESULTS: The 1977 included patients had a median age of 3 years (1 day-20 years); 1163 were boys. Among the 1161 first relapses, 53 were in the CNS, with an overall incidence of 2.7%, representing 6.2% of all metastatic relapses. One- and three-year post-relapse overall survival was 25 ± 6% and 8 ± 4%, respectively. Higher risk of CNS recurrence was associated with female sex (hazard ratio [HR] = 2.0 [95% confidence interval {CI}: 1.1-3.5]; P = 0.016), MYCN-amplification (HR = 2.4 [95% CI: 1.2-4.4]; P = 0.008), liver (HR = 2.5 [95% CI: 1.2-5.1]; P = 0.01) or >1 metastatic compartment involvement (HR = 7.1 [95% CI: 1.0-48.4]; P = 0.047) at diagnosis. Neither HDC nor immunotherapy was associated with higher risk of CNS recurrence. Stable incidence of CNS relapse was reported over time. CONCLUSIONS: The risk of CNS recurrence is linked to both patient and disease characteristics, with neither impact of HDC nor immunotherapy. These findings support the current treatment strategy and do not justify a CNS prophylactic treatment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".