Preanalytic parameters in epidermal growth factor receptor mutation testing for non–small cell lung carcinoma: A review of cytologic series
Bibliographic record
Abstract
The results from molecular assays can be affected significantly by the preanalytic condition of cytologic samples. The authors review current knowledge on the use of cytologic samples for epidermal growth factor receptor (EGFR) mutation testing in non-small cell lung cancer with a focus on preanalytic parameters. A systematic electronic search of the MEDLINE database was performed to identify original articles that reported the use of cytologic samples for EGFR molecular analysis and included a minimum of 100 samples. The information collected included author(s), journal, and year of publication; number of patients and samples; sampling method; type of preparation; type of fixative; staining techniques; mutation analysis techniques; tumor cellularity; the percentage of tumor cells; data on DNA quantity, quality, and concentration; failed assays; and the mutation rate. EGFR mutation analysis was conducted on 4999 cytologic samples from 22 studies that fulfilled the inclusion criteria. Fine-needle aspirates and pleural effusions were the most common types of specimens used. DNA was mainly extracted from cell blocks and smears, and the most commonly reported fixatives included formalin, ethanol, and CytoLyt. Cellularity assessments and DNA yields were available from 5 studies each. The average success rate for the assays that used cytologic specimens was 95.87% (range, 85.2%-100%). The mutation rate ranged from 6% to 50.46%, and a higher mutation detection rate and lower numbers of insufficient cases were reported for pleural effusions and lymph node samples from endobronchial ultrasound-guided transbronchial needle aspiration compared with histologic specimens. Low cellularity and a low percentage of tumor cells were associated with higher test failure rates. Future guidelines should consider the current data for specific recommendations regarding cytologic samples.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".