Advancing Neuroblastoma Diagnosis: A Comprehensive Review of Computational Technologies and Methodologies for Data Analysis and Pattern Extraction
Bibliographic record
Abstract
Neuroblastoma (NB) is a childhood malignancy associated with high cancer-related mortality and disability, remaining a persistent challenge in paediatric oncology.High-risk NB tumours often metastasize, resulting in survival rates below 50%.Early detection and accurate risk stratification are thus essential for improving patient prognosis and therapeutic outcomes.In recent years, computational approaches, including machine learning (ML) and deep learning (DL), have been extensively applied to extract meaningful clinical and biological insights from multi-modal NB datasets.This review systematically synthesizes literature applying ML, DL, and statistical methods to analyze multi-omics profiles, histopathological images, and medical imaging for diagnostic and prognostic modeling in NB.It evaluates various computational methodologies for tumour classification, risk group stratification, and outcome prediction.Special attention is given to emerging advancements such as Vision Transformers (ViTs) for histopathology, self-explainable AI (S-XAI), counterfactual interpretability, and federated learning (FL) frameworks (e.g., Swarm Learning, SplitFed), which support transparency, privacy, and decentralized collaboration.Furthermore, this study highlights the clinical potential of integrating computational models into real-time decision-making workflows and emphasizes the importance of ethical fairness, multi-institutional validation, and personalized treatment strategies.By addressing these challenges, AI-driven tools are poised to significantly improve NB diagnosis, risk stratification, and outcome prediction in paediatric oncology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".