Biomarkers in tumors of the central nervous system – a review
Bibliographic record
Abstract
Until recently, diagnostics of brain tumors were almost solely based on morphology and immunohistochemical stainings for relatively unspecific lineage markers. Although certain molecular markers have been known for longer than a decade (combined loss of chromosome 1p and 19q in oligodendrogliomas), molecular biomarkers were not included in the WHO scheme until 2016. Now, the classification of diffuse gliomas rests on an integration of morphology and molecular results. Also, for many other central nervous system tumor entities, specific diagnostic, prognostic and predictive biomarkers have been detected and continue to emerge. Previously, we considered brain tumors with similar histology to represent a single disease entity. We now realize that histologically identical tumors might show alterations in different molecular pathways, and often represent separate diseases with different natural history and response to treatment. Hence, knowledge about specific biomarkers is of great importance for individualized treatment and follow-up. In this paper we review the biomarkers that we currently use in the diagnostic work-up of brain tumors.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".