A systematic review of CSF biomarker discovery in neuro-oncology: A roadmap to standardization and clinical application
Bibliographic record
Abstract
Abstract Effective diagnosis, prognostication and management of central nervous system (CNS) malignancies traditionally involves invasive brain biopsy but sampling and molecular profiling of cerebrospinal fluid (CSF) is a safer, rapid and non-invasive alternative that can offer a snapshot of the intracranial milieu. While numerous assays and biomarkers have been analyzed, translational challenges remain, and standardization of protocols is necessary. Here we systematically reviewed 141 studies (Medline, SCOPUS, and Biosis databases; published between January 2000 and September 29th, 2022) that molecularly profiled CSF from adults with brain malignancies including glioma, brain metastasis (BrM), and CNS lymphoma (CNSL). We provide an overview of promising CSF biomarkers, propose CSF reporting guidelines, and discuss the various considerations that go into biomarker discovery, including the influence of blood-brain barrier disruption, type of biomarker (i.e., tumor cell DNA, RNA, protein), cell-of-origin, and site of CSF acquisition (e.g., lumbar, ventricular). We also performed a meta-analysis of proteomic datasets, identifying biomarkers in CNS malignancies and establishing a resource for the research community.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".