Molecular alterations in non‐small‐cell lung cancer: Perspective for targeted therapy and specimen management for the bronchoscopist
Bibliographic record
Abstract
Major advances have occurred over the past decade in our understanding of lung cancer pathobiology. Increasing knowledge of molecular aberrations in lung cancer, specifically the discovery of two driver genes in pharmacologically targetable tyrosine kinases involved in growth factor receptor signalling, epidermal growth factor receptor and anaplastic lymphoma kinase, has been of major therapeutic and prognostic importance. This discovery has allowed for new, personalized approach to the management of lung cancer. Recognizing the importance of molecular signatures of lung cancer, the College of American Pathologists, International Association for the Study of Lung Cancer and Association for Molecular Pathology released the first guidelines for molecular testing in lung cancer. The introduction of minimally invasive needle techniques for the evaluation of lung cancer patients, such as endobronchial ultrasound transbronchial needle aspiration and oesophageal ultrasound-fine-needle aspiration, has revolutionized the way lung cancer patients are assessed. Samples obtained using the minimally invasive needle approaches have been shown to be sufficient not only for routine molecular testing but also for multigenic analysis. This allows bronchoscopist to assume an increasingly important role in the diagnostic workup of patients with lung cancer at all stages of the disease and contribute to personalizing the care of lung cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 0.002 |
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".