Molecular Profiling in Non-Small-Cell Lung Cancer: A Single-Center Study on Prevalence and Prognosis
Bibliographic record
Abstract
The aim of this study is to evaluate the prognostic value of molecular profiling in patients with metastatic non-small-cell lung cancer (NSCLC). This single-center study included patients diagnosed and treated between July 2020 and April 2024. The molecular profiles of patients detected by either next-generation sequencing or conventional methods were reviewed retrospectively. Survival analyses were conducted based on the targetable alterations and treatments received. Seventy patients were included, with a median age of 65 years and a median overall survival (OS) of 13 months. Of all patients, 56 (80%) had at least one molecular alteration, and the most frequent alteration was TP53 (52.9%), followed by KRAS (20%) and EGFR (8.6%). Eighteen patients (25.7%) had an alteration amenable to targeted therapy. Patients who could reach a matched targeted therapy at any treatment line exhibited a longer median OS compared to those who could not (not reached vs. 6.9 months, p = 0.042). Patients with a targetable alteration for first-line treatment demonstrated a longer progression-free survival compared to those without a targetable alteration (not reached vs. 4.9 months, p = 0.006). According to current guidelines, conducting molecular testing to identify all potential targetable alterations in NSCLC is the cornerstone of the treatment decision process. The survival analysis in this study emphasized the impact of the use of targeted therapies on the survival outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| 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".