The Role of Molecular Pathology in Non-Small-Cell Lung Carcinoma—Now and in the Future
Bibliographic record
Abstract
In recent years, better understanding of the molecular biology of non-small-cell lung carcinoma (nsclc) has led to a revolution in the work-up of these neoplasms. As a pathology diagnosis, "nsclc" without further attempt at subclassification is no longer accepted as a standard of care; separating squamous cell carcinoma from adenocarcinoma and large-cell carcinoma carries implications for prognosis and treatment decisions. Currently, detection of the presence in nsclc of mutations involving the epidermal growth factor receptor (EGFR) gene and fusion of the N-terminal portion of the protein encoded by EML4 (echinoderm microtubule-associated protein-like 4 gene) with the intracellular signaling portion of the receptor tyrosine kinase encoded by ALK (anaplastic lymphoma kinase gene)-that is, EML4-ALK-and variants has become routine in many centres because patients having tumours harbouring such alterations might benefit from tyrosine kinase inhibitors as part of their treatment regimen.The purpose of the present review is to highlight important aspects of the screening for molecular derangements in nsclc and to briefly discuss the emergence of possible future biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".