Tumour tissue sampling for lung cancer management in the era of personalised therapy: what is good enough for molecular testing?
Bibliographic record
Abstract
In the era of personalised cancer therapy, the demand for molecular profiling of the patient's tumour is steadily increasing. In advanced nonsmall cell lung cancer (NSCLC) patients, testing for epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) gene rearrangements has become an essential component of clinical practice to select patients who are most likely to benefit from EGFR and ALK tyrosine kinase inhibitors, respectively. Furthermore, obtaining tissue specimens from recurrent or metastatic tumours or from patients who develop resistance to initial effective therapies are essential for our understanding of the molecular basis of tumour progression and development of drug resistance. Therefore, the sampling of tumour tissue that is representative and is adequate in quantity and quality for pathological diagnosis and genomic profiling is crucial. In this review, we will discuss factors that should be considered in obtaining and processing biopsy specimens to enable routine molecular analysis in NSCLC 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".