Utility of nonsurgical diagnostic specimens in cellular differentiation and molecular profiling (EGFR mutation and EML4-ALK detection) of NSCLC.
Bibliographic record
Abstract
e18061 Background: The utility of different non-surgical specimens for the accurate determination of cellular and molecular attributes in NSCLC has yet to be established. The primary objective of this study was to determine and compare the yield of non-surgical specimens for the accurate cellular differentiation of NSCLC and analysis of EGFR and EML4-ALK mutations. Methods: Patients with a histologic diagnosis of NSCLC from January 2004 to September 2010 were included. Diagnosis was based on cytological characteristics and IHC analysis. Fragment analysis and real-time PCR methods were used for EGFR mutation detection. EML4-ALK rearrangements were detected by FISH. Diagnostic specimens were divided into pathology specimens (PS) and cytology specimens (CS). PS included surgical and non-surgical biopsies. CS included FNA (TBNA and TTNA) and body fluid samples. These groups were compared using χ2 analyses. Yield of histologic analysis was compared in a subgroup of patients who underwent both surgical and non-surgical procedures. Results: 715 patients were included in the study. The yield of CS when compared to PS was lower for cellular differentiation (76% vs. 91%, p <0.0001) and IHC (70% vs. 89%, p <0.0001). Among the CS, TTNA provided better yield than TBNA for cellular differentiation (89% vs. 67%, p < 0.0001) and IHC (85% vs. 72%, p = 0.023). 94 patients underwent both surgical and non-surgical procedures. As compared to surgical biopsies, the yield of non-surgical procedures for cellular classification was 81% in body fluid samples, 68% in FNA, and 88% in non-surgical biopsies. 320 patients were tested for both EGFR and EML4-ALK mutations. The yield of CS versus PS was lower for EGFR mutation status (74% vs. 93% p < 0.0001). Among the CS, TTNA provided better yield than TBNA for EGFR mutation analysis (90% vs. 70%, p = 0.045) and body fluid samples were least likely (59%) to provide EGFR mutation status. Three EML4-ALK FISH-positive cases were identified. All samples were PS. All EML4-ALK positive cases tested negative for EGFR mutations. Conclusions: Non-surgical lung biopsy specimens can yield sufficient samples for histologic assignment and mutational analysis.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".