Benchmarking population-based EGFR mutation testing in nonsquamous non-small cell lung cancer.
Bibliographic record
Abstract
e19032 Background: Epidermal growth factor receptor (EGFR) mutation testing has become critical in the treatment of advanced non-small cell lung cancer (NSCLC) patients. This study involves a large cohort and epidemiologically unselected series of EGFR mutation testing for non-squamous NSCLC patients in a North American population, to determine mutation rates and define factors that influence success in routine clinical EGFR testing. Methods: Data from consecutive cases of Canadian province-wide testing during a 24-month period at a centralized diagnostic laboratory were reviewed. Samples were tested for exon 19 deletion and exon 21 L858R mutations using a validated PCR method with 1-5% detection sensitivity. Results: From 2,651 samples submitted, 2,404 samples were tested with 2,293 samples eligible for analysis. These included 1,780 histology and 513 cytology specimens. The overall test failure rate was 5.4%. Among successfully tested samples, the overall mutation rate was 20.6%. There were no significant differences in the failure rate, mutation rate, or mutation type found between histology and cytology samples. While tumor cellularity was significantly associated with test success or mutation rates in histology and cytology specimens respectively, mutations could be detected in all specimen types. Optimal histology samples contained ≥2 mm2of tumor tissue and ≥30% tumor cellularity. Optimal cytology cell-block samples have at least a few groups of nucleated cells dispersed throughout the block, regardless of tumor cellularity. Samples from metastatic deposits in bone, distant lymph nodes, and pleura showed higher mutation rates than primary lesions. Conclusions: Our current method for EGFR mutation testing is able to detect mutations in small tumor volume biopsies, typical of tissue obtained for initial diagnosis. EGFR mutation testing should be attempted in any type of specimen, histology or cytology. Cases that are suboptimal with a negative EGFR mutation result should be considered for repeat testing with an alternate tumor sample.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".