Non-Small Cell Lung Carcinoma Biomarker Testing: The Pathologist’s Perspective
Bibliographic record
Abstract
Biomarker testing has become standard of care for patients diagnosed with non-small cell lung carcinoma (NSCLC). Although, it can be successfully performed in circulating tumor cells, at present, the vast majority of investigations are carried out using direct tumor sampling, either through aspiration methods, which render most often isolated cells, or tissue sampling, that could range from minute biopsies to large resections. Consequently, pathologists play a central role in this process. Recent evidence suggests that refining NSCLC diagnosis might be clinically significant, particularly in cases of lung adenocarcinomas (ADC), which in turn, has prompted a new proposal for the histologic classification of such pulmonary neoplasms. These changes, in conjunction with the mandatory incorporation of biomarker testing in routine NSCLC tissue processing, have directly affected the pathologist's role in lung cancer work-up. This new role pathologists must play is complex and demanding, and requires a close interaction with surgeons, oncologists, radiologists, and molecular pathologists. Pathologists often find themselves as the central figure in the coordination of a process, that involves assuring that the tumor samples are properly fixed, but without disruption of the DNA structure, obtaining the proper diagnosis with a minimum of tissue waste, providing pre-analytical evaluation of tumor samples selected for biomarker testing, which includes assessment of the proportion of tumor to normal tissues, as well as cell viability, and assuring that this entire process happens in a timely fashion. Therefore, it is part of the pathologist's responsibilities to assure that the samples received in their laboratories, be processed in a manner that allows for optimal biomarker testing. This article goal is to discuss the essential role pathologists must play in NSCLC biomarker testing, as well as to provide a summarized review of the main NSCLC biomarkers of clinical interest.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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".