The Clinical Characteristics, Treatment, and Prognosis of Lung Cancer in Young Patients in the New Era of Cancer Treatment: A Retrospective and Comprehensive Analysis
Bibliographic record
Abstract
BACKGROUND: This study was aimed to comprehensively investigate the clinical and molecular characteristics, treatments, and outcomes of young patients with lung cancer in the new era of cancer treatment. METHODS: Clinical data from patients aged 18 to 45 with lung cancer, treated at our hospital from January 2014 through January 2024, were systematically collected and analyzed. RESULTS: This study enrolled a total of 343 patients, with a predominance of females, never-smokers, and those diagnosed at an advanced stage. Adenocarcinoma was the most common histology (72.0%), and rare tumors could also be seen in young patients, such as pulmonary sarcomatoid carcinoma and pulmonary mucoepidermoid carcinoma. The mutation rate of the epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) in NSCLC patients were 35.9% (111/309) and 14.2% (44/309), respectively. PD-L1 expression was assessed in 55 patients, with 14 showing high expression (≥50%) and 24 showing negative expression (<1%). The median overall survival (mOS) for the entire cohort was 80.2 months, with a 5-year survival rate of 55.7%. For patients with stage I, II, and III disease, the mOS had not yet been reached, whereas the mOS for stage IV patients was 39.7 months. Targeted therapy, particularly second-generation ALK tyrosine kinase inhibitors (TKIs), significantly improved the prognosis of patients with driver gene mutations. Chemotherapy combined with immunotherapy was beneficial for patients with progressive disease or driver gene negativity in NSCLC and was associated with improved OS in small cell lung cancer (SCLC). Female, family history of lung cancer, positive driver genes, and first-line use of second-generation ALK-TKIs are independent prognostic factors in young patients with advanced NSCLC. CONCLUSIONS: Our findings highlight the importance of early diagnosis, targeted therapy, and immunotherapy in improving outcomes for young patients with lung cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".