<i>EGFR</i> Mutation Detection and Its Association With Clinicopathological Characters of Lung Cancer Patients
Bibliographic record
Abstract
Background: Lung cancer is the most common type of cancer worldwide with an estimation of 1.82 million new cancer cases diagnosed; and it is the leading cause of cancer-related deaths. Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase identified as being highly expressed in cancer cells including lung cancers. The aim of the study is to determine the EGFR mutation status in non-small cell lung cancer (NSCLC) patients to investigate the association between the EGFR mutation status and clinicopathological characters of patients. Methods: The tissue samples of the lung cancer patients were collected bronchoscopically. The EGFR mutations of 70 NSCLC patients were determined by the immunohistochemistry (IHC). Results: EGFR mutations were present in 24 cases (34.29%), including 19 (79.13%) cases of exon 19 and five (20.83%) cases of exon 21 mutation. EGFR mutations were frequently associated with adenocarcinoma and non-smoker. Statistically significant association of EGFR mutations with adenocarcinoma subtypes and non-smokers was found (P < 0.05); and no significant association of EGFR mutation with the age of the patient (P = 0.4647) and the stage (P = 0.4578) of the tumor was found. When we compared between these two mutations, no significant association with age (P=0.614) and smoking status (P=0.127) was found in this study. Conclusions: EGFR mutations were significantly associated with female sex, non-smoker and adenocarcinoma subtypes. The analysis of EGFR mutation by the IHC method is a potentially useful tool to guide clinicians for personalized treatment of NSCLC patients of adenocarcinoma subtype. World J Oncol. 2018;9(5-6):151-155 doi: https://doi.org/10.14740/wjon1167 Â
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".