Efficacy of Osimertinib in Lung Squamous Cell Carcinoma Patients with EGFR Gene Mutation–Case Report and a Literature Review
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related mortality worldwide. It is responsible for 80–85% of lung cancer cases. NSCLC can be divided into several groups, led by adenocarcinoma (ADC)–40–50% and squamous cell carcinoma (SCC)–20–30%. The development of new molecular therapies targeting particular abnormalities such as mutations in the EGFR (Epidermal Growth Factor Receptor) gene or ROS1 or ALK genes rearrangements resolved in novel strategies in advanced NSCLC management. EGFR mutation occurs mostly in patients with ADC and those patients are mostly females with no or light smoking history. The hereby presented patient fitted the ADC characteristics, while they were diagnosed with SCC. The unusual diagnosis implied further genetic testing, which established the occurrence of L858R substitution in exon 21 in the EGFR gene. A 63-year-old female was admitted to the unit due to a dry cough, pain in the right chest area and dyspnoea. When diagnosed, the patient had a peripheral mass in the right lung superior lobe (55 × 40 mm), satellite nodules in the apex of the same lung and packets of disintegrating lymph nodes. Positron Emission Tomography (PET-CT) confirmed a diffuse neoplastic process qualified as stage IV on the TNM scale. Due to EGFR gene mutation, the woman was administered osimertinib, however, the treatment did not succeed, and other therapeutic solutions were undertaken. The patient died 10 months after diagnosis. Patients with advanced ADC harboring EGFR mutation can receive osimertinib, a third-generation tyrosine kinase inhibitor (TKI), however, the use of TKIs in SCC remains controversial. In some published cases, osimertinib treatment led to success, in others, the therapy did not result in the expected final effect. Small sample groups and diverse molecular backgrounds indicate the need for further research in this field. Thus, the treatment decision-making process in those patients overall remains extremely demanding and ambiguous.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".