Bridging the clinical gaps: genetic, epigenetic and transcriptomic biomarkers for the early detection of lung cancer in the post-National Lung Screening Trial era
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide in part due to our inability to identify which smokers are at highest risk and the lack of effective tools to detect the disease at its earliest and potentially curable stage. Recent results from the National Lung Screening Trial have shown that annual screening of high-risk smokers with low-dose helical computed tomography of the chest can reduce lung cancer mortality. However, molecular biomarkers are needed to identify which current and former smokers would benefit most from annual computed tomography scan screening in order to reduce the costs and morbidity associated with this procedure. Additionally, there is an urgent clinical need to develop biomarkers that can distinguish benign from malignant lesions found on computed tomography of the chest given its very high false positive rate. This review highlights recent genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the early detection of lung cancer both in the diagnostic and screening setting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".