Endobronchial ultrasound-guided sampling can be used for successful molecular sampling in routine clinical practice
Bibliographic record
Abstract
Treatment in oncology is becoming increasingly driven by tumour genetics and biomarker status. In non-small cell lung cancer, mutations within the gene encoding for the tyrosine kinase domain of the epidermal growth factor receptor (EGFR) are associated with improved response to tyrosine kinase inhibitors. This has led to need for genetic analysis for this mutation in lung cancer patients. Initial studies assessed EGFR status in surgical biopsies. Many patients have advanced disease at presentation and there is a requirement to be able to perform this analysis on fine needle aspirates. Whilst studies have shown cytological samples specifically prepared are suitable for this analysis, few studies have assessed the ability to perform molecular analysis on routine samples. We hypothesised that molecular analysis would be possible from routine samples collected at our institution. All procedures performed between 2008 and 2011 were reviewed. Using patient and pathology records, data was collected on patient demographics, adequacy of samples, cytological diagnosis and molecular analyses performed. Cancer was diagnosed in 352 out of 741 cases (48%). Molecular analysis was performed in 70 cases (20% cancer cases). Molecular analysis included PCR, flow cytometry and FISH and occurred in varying cancer diagnoses including: lymphoma (B cell and Hodgkins), melanoma, sarcoma and lung and breast cancer. In a subgroup, EGFR analysis was possible in 87% (47/54) of cases with mutations present in 34% (16/47). Samples from EBUS-TBNA can be used for molecular analysis in routine clinical practice. Their use extends not only to lung but other metastatic cancers of the thorax.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".