Nomogram to predict the presence of<i>EGFR</i>activating mutation in lung adenocarcinoma
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) tumour genotyping is crucial to guide treatment decisions regarding the use of EGFR tyrosine kinase inhibitors in nonsmall cell lung cancer (NSCLC). However, some patients may not be able to obtain tumour testing, either because tissue is limited and/or tests are not routinely offered. Here, we aimed to build a model-based nomogram to allow for prediction of the presence of EGFR mutations in NSCLC. We retrospectively collected clinical and pathological data on 3,006 patients with NSCLC who had their tumours genotyped for EGFR mutations at five institutions worldwide. Variables of interest were integrated in a multivariate logistic regression model. In the 2,392 non-Asian patients with lung adenocarcinomas, the most important predictors of harbouring EGFR mutation were: lower tobacco smoking exposure (OR 0.41, 95% CI 0.37-0.46), longer time interval between smoking cessation and diagnosis (OR 2.19, 95% CI 1.71-2.80), advanced stage (OR 1.58, 95% CI 1.18-2.13), and papillary (OR 4.57, 95% CI 3.14-6.66) or bronchioloalveolar (OR 2.84, 95% CI 1.98-4.06) histologically predominant subtype. A nomogram was established and showed excellent discriminating accuracy: the concordance index on an independent validation dataset was 0.84. As clinical practices transition to incorporating genotyping as part of routine care, this nomogram could be highly useful to predict the presence of EGFR mutations in lung adenocarcinoma in non-Asian patients when mutational profiling is not available or possible.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".