CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To systematically investigate the relationship between CT morphological features and the presence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC). METHODS: All studies about the CT morphological features of NSCLC with EGFR mutations published between January 1, 2000 and March 15, 2015 were searched in the PubMed and EMBASE databases. Qualified studies were selected according to inclusion criteria. The frequency of EGFR mutations and CT features of ground-glass opacity (GGO) content, tumor size, cavitation, air-bronchogram, lobulation, and spiculation were extracted. The relationship between EGFR mutations and each of these CT features was tested based upon the weighted mean difference or inverse variance in the form of an odds ratio at a 95% confidence interval using Forest Plots. The publication bias was examined using Egger's test. RESULTS: A total of 13 studies, consisting of 2146 NSCLC patients, were included, and 51.12% (1097/2146) of patients had EGFR mutations. The EGFR mutations were present in NSCLC with part-solid GGO in contrast to nonsolid GGO (OR = 0.49, 95% CI = 0.25-0.96, P = 0.04). Other CT features such as tumor size, cavitation, air-bronchogram, lobulation and spiculation did not demonstrate statistically significant correlation with EGFR mutations individually (P = 0.91; 0.67; 0.12; 0.45; and 0.36, respectively). No publication bias among the selected studies was noted in this meta-analysis (Egger's tests, P > 0.05 for all). CONCLUSION: This meta-analysis demonstrated that NSCLC with CT morphological features of part-solid GGO tended to be EGFR mutated, which might provide an important clue for the correct selection of patients treated with molecular targeted therapies.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".