Imaging-Based Surrogate Markers of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma: A Local Perspective
Bibliographic record
Abstract
PURPOSE: To identify computed tomography (CT) features of epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma in Canadian population and whether imaging-based surrogate markers of EGFR mutation in our population were similar to those found in the Asian population. MATERIALS AND METHODS: Pretreatment CT scans of 223 patients with adenocarcinoma of the lung (112 with EGFR mutation and 111 without mutation) were retrospectively assessed for 20 specific CT features by 2 radiologists, who were blinded to the status of EGFR mutation. Univariate and multivariate logistic regression analyses as well as areas under the receiver operating characteristic curve were performed to discriminate characteristics of EGFR-activating mutation features. RESULTS: < .05), similarly to the Asian population. Multivariate logistic regression analyses of combined clinical and radiological features identified less than 20 pack-year smoking history, smaller tumor diameter, fine or coarse spiculations, noncentral location of the tumor, and lack of centrilobular emphysema and pleural attachment as the strongest independent prognostic factors for the presence of an EGFR mutation. These combined features improved prognostic ability area under the curve to 0.879, compared to 0.788 for clinical features only. CONCLUSION: Several CT findings may help predict the presence of an activating mutation in EGFR in lung adenocarcinomas in our Canadian population. Combining clinical and radiological features improves prognostic ability to determine the EGFR mutation status compared to clinical features alone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".