Contribution of the IdyllaTM System to Improving the Therapeutic Care of Patients with NSCLC through Early Screening of EGFR Mutations
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) genotyping, a critical examen for the treatment decisions of patients with non-small cell lung cancer (NSCLC), is commonly assayed by next-generation sequencing (NGS), but this global approach takes time. To determine whether rapid EGFR genotyping tests by the IdyllaTM system guides earlier therapy decisions, EGFR mutations were assayed by both the IdyllaTM system and NGS in 223 patients with NSCLC in a bicentric prospective study. IdyllaTM demonstrated agreement with the NGS method in 187/194 cases (96.4%) and recovered 20 of the 26 (77%) EGFR mutations detected using NGS. Regarding the seven missed EGFR mutations, five were not detected by the IdyllaTM system, one was assayed in a sample with insufficient tumoral cells, and the last was in a sample not validated by the IdyllaTM system (a bone metastasis). IdyllaTM did not detect any false positives. The average time between EGFR genotyping results from IdyllaTM and the NGS method was 9.2 ± 2.2 working days (wd) (12.6 ± 4.0 calendar days (cd)). Subsequently, based on the IdyllaTM method, the timeframe from tumor sampling to the initiation of EGFR-TKI was 7.7 ± 1.2 wd (11.4 ± 3.1 cd), while it was 20.3 ± 6.7 wd (27.2 ± 8.3 cd) with the NGS method (p < 0.001). We thus demonstrated here that the IdyllaTM system contributes to improving the therapeutic care of patients with NSCLC by the early screening of EGFR mutations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".