A nomogram of clinical and biologic factors to predict survival in children newly diagnosed with high‐risk neuroblastoma: An International Neuroblastoma Risk Group project
Bibliographic record
Abstract
BACKGROUND: Long-term outcome remains poor for children with high-risk neuroblastoma (five-year overall survival [OS] ∼50%). Our objectives were to (a) identify prognostic biomarkers and apply them in a nomogram to identify the subgroup of ultra-high-risk patients at highest risk of disease progression/death, for whom novel frontline therapy is urgently needed; and (b) validate the nomogram in an independent cohort. METHODS: A total of 1820 high-risk patients (≥18 months old with metastatic neuroblastoma), diagnosed 1998-2015, from the International Neuroblastoma Risk Groups (INRG) Data Commons were analyzed in a retrospective cohort study. Using multivariable Cox regression of OS from diagnosis, a nomogram was created from prognostic biomarkers to predict three-year OS. External validation was performed using the SIOPEN HR-NBL1 trial cohort (n = 521), evidenced by receiver operating characteristic curves. RESULTS: The nomogram, including MYCN status (P < 0.0001), lactate dehydrogenase (LDH) (P = 0.0007), and presence of bone marrow metastases (P = 0.004), had robust performance and was validated. Applying the nomogram at diagnosis (a) gives prognosis of an individual patient and (b) identifies patients predicted to have poor outcome (three-year OS was 30% ± 5% for patients with a nomogram score of > 82 points; 58% ± 1% for those ≤82 points). Median follow-up time was 5.5 years (range, 0-14.1). CONCLUSIONS: In high-risk neuroblastoma, a novel, publicly available nomogram using prognostic biomarkers (MYCN status, LDH, presence of bone marrow metastases; https://neuroblastoma.shinyapps.io/High-Risk-Neuroblastoma-Nomogram/) has the flexibility to apply a clinically suitable and context-specific cutoff to identify patients at highest risk of death. This will facilitate testing urgently needed new frontline treatment options to improve outcome for these children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".