Lung Cancer in Young Patients: Higher Rate of Driver Mutations and Brain Involvement, but Better Survival
Bibliographic record
Abstract
Lung cancer (LC) is the leading cause of cancer mortality worldwide. Despite advances in the treatment strategy, including surgery, chemotherapy, radiotherapy, immunotherapy, and targeted therapy, 5-year survival is estimated as 9% to 20%.1,2 During the past decade, LC incidence has been increasing and age at the time of diagnosis continues to decrease.3,4 Median age at diagnosis is 70 years, and approximately 13% of all patients with LC are younger than age 50 years. Numerous studies have suggested that LC in young patients constitutes an entity with unique characteristics, such as a higher percentage of female patients, a lower rate of smoking history, a higher percentage of family history of LC, a higher rate of adenocarcinoma histology, and more advanced stage at diagnosis.5-13 However, it is still controversial whether youthful patients with LC have better or worse outcomes.14-16 In addition, most of the literature regarding young patients is associated with Asian cohorts, whereas less data about white communities are available. Currently, a considerable percentage of patients with non–small-cell lung cancer (NSCLC) benefit from personalized therapy protocols that are based on the genomic profile of tumors.17,18 Mutations in the epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) genes affect the prognosis of patients. Recent studies have shown that young patients with NSCLC harbor more driver mutations than older patients. The rate of mutations documented in the young white population varies between articles and is approximately 20% to 30% for EGFR mutation and 10% to 20% for ALK rearrangement.17-19 There are conflicting data on whether younger patients with NSCLC achieve better or worse outcomes compared with the older population, yet most studies show that younger patients have better survival rates.7-10 Identifying the clinicopathologic characteristics and making appropriate proactive molecular profiling of the youthful population can guide treatment strategy in the clinical setting. Therefore, in the current study, we carried out a comprehensive analysis of patient clinicopathologic features and clinical outcomes in both young (age ≤ 50 years) and older (age > 60 years) patients with NSCLC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".