Epidermal Growth Factor Receptor Mutation Frequency in Squamous Cell Carcinoma and Its Diagnostic Performance in Cytological Samples: A Molecular and Immunohistochemical Study
Bibliographic record
Abstract
BACKGROUND: Epidermal growth factor receptor (EGFR) mutation is the most frequent mutation tested in lung cancer for targeted therapy in the era of personalized medicine. Knowledge about EGFR mutation is constantly expanding regarding its frequency, clinicopathological association, advancements in testing methodology and sample requirement. We investigated EGFR mutation frequency in non-small cell lung cancer (NSCLC) in North Indian patients and evaluated its diagnostic performance in cytological samples. METHODS: Molecular EGFR testing was done in 250 cases of NSCLC by both real-time polymerase chain reaction (PCR) (Therascreen) and mutation-specific EGFR immunohistochemistry (IHC). Thirty cases had both cytology samples and biopsy including 20 pleural effusions and 10 fine-needle aspirates. EGFR mutation concordance between pleural effusion and biopsy was studied. RESULTS: EGFR mutation was overall 31.6% in NSCLC with 36.5% in adenocarcinoma and 15% in squamous cell carcinoma. L858R mutation accounted for 50.7% and DEL19 for 39.3% of total EGFR mutations. Complex mutations were seen in 2% of cases. Sensitivity of mutation-specific EGFR IHC was 48.3% and specificity was 92.3%. L858R showed higher sensitivity (55% vs. 33.3%) but similar specificity (93.2% vs. 91.3%) compared to DEL19. EGFR mutation was successful in 95% of pleural effusion and showed 83.3% concordance with tissue biopsy. CONCLUSIONS: EGFR mutation frequency in North Indian patients was comparable to that of Asia-Pacific region and showed a similar pattern of histological distribution. EGFR mutation in squamous cell carcinomas is increasingly recognized which was 15% in our study. Mutation-specific EGFR IHC shows variable but generally low sensitivity and considering its significant pre- and post-analytical variables, it should be highly discouraged in patient management. Cytological samples may not only serve as suitable alternative but may be complementary to tissue biopsies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".