A review of predictive, prognostic and diagnostic biomarkers for non-small-cell lung cancer: towards personalised and targeted cancer therapy
Bibliographic record
Abstract
Abstract Introduction: Lung cancer has a high mortality rate mainly due to the lack of early detection or outward signs and symptoms, thereby often progressing to advanced stages (e.g., stage IV) before it is diagnosed. However, if lung cancers can be diagnosed at an early stage and also if clinicians can prospectively identify patients likely to respond to specific treatments, then there is a very high potential to increase patients’ survival. In recent years, several investigations have been conducted to identify cancer biomarkers for lung cancer risk assessment, early detection and diagnosis, the likelihood of identifying the group of patients who will benefit from a particular treatment and monitoring patient response to treatment. Materials and Methods: This paper reports on the review of 19 current clinical and emerging biomarkers used in risk assessment, screening for early detection and diagnosis and monitoring the response of treatment of non-small-cell lung cancers. Conclusion: The future holds promise for personalised and targeted medicine from prevention, diagnosis to treatment, which take into account individual patient’s variability, though it depends on the development of effective biomarkers interrogating the key aberrant pathways and potentially targetable with molecular targeted or immunologic therapies. Lung cancer biomarkers have the potential to guide clinical decision-making since they can potentially detect the disease early, measure the risk of developing the disease and the risk of progression, provide accurate information of patient response to a specific treatment and are capable of informing clinicians about the likely outcome of a cancer diagnosis independent of the treatment received. Moreover, lung cancer biomarkers are increasingly linked to specific molecular pathway deregulations and/or cancer pathogenesis and can be used to justify the application of certain therapeutic or interventional strategies.
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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 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".