A review of predictive, prognostic and diagnostic biomarkers for brain tumours: towards personalised and targeted cancer therapy
Bibliographic record
Abstract
Abstract Background: Brain tumours are relatively rare disease but present a large medical challenge as there is currently no method for early detection of the tumour and are typically not diagnosed until patients have progressed to symptomatic stage which significantly decreases chances of survival and also minimises treatment efficacy. However, if brain cancers can be diagnosed at early stages and also if clinicians have the potential to prospectively identify patients likely to respond to specific treatments, then there is a very high potential to increase patients’ treatment efficacy and survival. In recent years, there have been several investigations to identify biomarkers for brain cancer risk assessment, early detection and diagnosis, the likelihood of identifying which group of patients will benefit from a particular treatment and monitoring patient response to treatment. Materials and methods: This paper reports on a review of 21 current clinical and emerging biomarkers used in risk assessment, screening for early detection and diagnosis, and monitoring the response of treatment of brain cancers. Conclusion: Understanding biomarkers, molecular mechanisms and signalling pathways can potentially lead to personalised and targeted treatment via therapeutic targeting of specific genetic aberrant pathways which play key roles in malignant brain tumour formation. The future holds promising for the use of biomarker analysis as a major factor for personalised and targeted brain cancer treatment, since biomarkers have the potential to measure early disease detection and diagnosis, the risk of disease development and progression, improved patient stratification for various treatment paradigms, provide accurate information of patient response to a specific treatment and inform clinicians about the likely outcome of a brain cancer diagnosis independent of the treatment received.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".