Sequential Combination of Unfavorable Histology, Followed by Clinical Stage M, Defines High-Risk Neuroblastoma: A Report from the Children’s Oncology Group
Bibliographic record
Abstract
PURPOSE: Historically, neuroblastoma risk stratification has been performed with clinical stage as the starting point and successively adding other prognostic factors thereafter. This study takes an alternative approach to define risk groups of patients with neuroblastoma by starting with the International Neuroblastoma Pathology Classification (INPC). EXPERIMENTAL DESIGN: The cohort of patients with neuroblastoma previously used for developing the Children's Oncology Group-Revised Neuroblastoma Risk Classification (RNRC) system was reanalyzed by survival tree regression analysis, starting with the INPC distinguishing favorable-histology and unfavorable-histology categories. The resultant two branches were further divided first by the International Neuroblastoma Risk Group Staging System and successively by other prognostic factors. RESULTS: This new stratification system, the INPC-Risk Grouping (INPC-RG), is simpler than the RNRC system, eliminating unnecessary decision trees, and distinguishes four risk groups (groups I-IV). Using only INPC (unfavorable histology) and International Neuroblastoma Risk Group Staging System (stage M), INPC-RG defines patients with highly aggressive group IV tumors, whose 5-year event-free survival was worse than that of the RNRC high-risk group. Additionally, it identifies group III patients whose 5-year event-free survival spanned 50% to 80%, which was not identified by the RNRC. CONCLUSIONS: The benefits of using this new INPC-RG system are fourfold: (1) it allows for the rapid identification of group IV patients, (2) it lays the foundation for further refinement of group III, (3) it can stratify patients when the amount of tumor tissue is limited, and (4) it allows patients in resource-limited areas to be appropriately stratified, potentially improving the worldwide treatment of patients with neuroblastoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".