Improving Time-to-Treatment for Advanced Non-Small Cell Lung Cancer Patients through Faster Single Gene EGFR Testing Using the Idylla™ EGFR Testing Platform
Bibliographic record
Abstract
Introduction: Patients with advanced-stage non-small cell lung cancer (NSCLC) may benefit from a short time-to-treatment (TTT). Predictive biomarker testing is performed prior to treatment, as recommended by various international expert consensus bodies. Genetic testing is more time-intensive than immunohistochemistry (IHC) and commonly contributes to prolonged TTT. For epidermal growth factor receptor-positive patients (EGFR+), further genetic testing may not be required due to the mutual exclusivity of actionable mutations. Methods: The trial cohort (N = 238) received both BC Cancer NGS panel (Oncopanel) and Idylla EGFR testing. Data were also collected for a control cohort (N = 220) that received Oncopanel testing. For each patient, the time that the lab received the sample, the time taken to report the NGS and Idylla tests, the time of first treatment, and the final treatment regimen were recorded. Results: A concordance frequency of 98.7% (232/235) was observed between the Idylla and NGS panel. The lab turnaround time (TAT) was faster for the Idylla test by an average of 12.4 days (N = 235, p < 0.01). Overall, the average TTT in the trial cohort (N = 114) was 10.1 days faster (p < 0.05) than the control (N = 114), leading to a 25% reduction in TTT. For patients treated based on EGFR positivity, the mean TTT was 16.8 days faster (p < 0.05) in the trial cohort (N = 33) than the control cohort (N = 28), leading to a 48% reduction in TTT. Conclusion: Using the Idylla EGFR test as part of the molecular testing repertoire in advanced-stage NSCLC patients could significantly reduce TTT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".