Liquid biomarkers for the management of paediatric neuroblastoma: an approach to personalised and targeted cancer therapy
Bibliographic record
Abstract
Abstract Background: Neuroblastoma is the most common extracranial solid tumour of infancy and accounts for about 6–10% of paediatric cancers. It has a biologically and clinically heterogeneous behaviour that ranges from spontaneous regression to cases of highly aggressive metastatic disease that could be unresponsive to standard therapy. In recent years, there have been several investigations into the development of various diagnostic, predictive and prognostic biomarkers towards personalised and targeted management of the disease. Materials and Methods: This paper reports on the review of current clinical and emerging biomarkers used in risk assessment, screening for early detection and diagnosis, prognostication and monitoring of the response of treatment of neuroblastoma in paediatric patients. Conclusions: Tumour markers can significantly improve diagnosis; however, the invasive, unpleasant and inconvenient nature of current tissue biopsies limits their applications, especially in paediatric patients. Therefore, the development of a non-invasive, reliable high accurate and personalised diagnostic tool capable of early detection and rapid response is the most promising step towards advanced cancer management from tumour diagnosis, therapy to patient monitoring and represents an important step towards the promise of precision, personalised and targeted medicine. Liquid biopsy assay with wide ranges of clinical applications is emerging to hold incredible potential for advancing cancer treatment and has greater promise for diagnostic purposes, identification and tracking of tumour-specific alterations during the course of the disease and to guide therapeutic decisions.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".