Review of molecular classification and treatment implications of pediatric brain tumors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Brain tumors are the most common solid tumors and leading cause of cancer-related death in children. The advent of large-scale genomics has resulted in a plethora of profiling studies that have mapped the genetic and epigenetic landscapes of pediatric brain tumors, ringing in a new era of precision diagnostics and targeted therapies. In this review, we highlight the most recent findings, focusing on studies published after 2015, and discuss how new evidence is changing the care of children with brain tumors. RECENT FINDINGS: Genome-wide and epigenome-wide profiling data have revealed distinct tumor entities within, virtually, all pediatric brain tumor groups including medulloblastoma; ependymoma; high-grade and low-grade gliomas; atypical teratoid/rhabdoid tumors; and other embryonal tumors, previously called CNS primitive neuroectodermal tumors. Whenever integrated with clinical information, many molecular alterations emerge as powerful prognostic markers and should thus be used to stratify patients and tailor therapies. SUMMARY: Optimal integration of this newly emerging knowledge in a timely and meaningful way into clinical care is a remarkable task and a matter of active debate. The historical morphology-based classification of tumors is being replaced by a genetic-based classification, and the first generation of molecularly informed clinical trials is underway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".