Molecular advances in early-stage and locally advanced non-small cell lung carcinoma: Shaping the future of precision oncology-systematic review
Bibliographic record
Abstract
ObjectiveTo synthesize recent molecular advances that inform diagnosis, risk-stratification, and perioperative treatment in early-stage and locally advanced non-small cell lung carcinoma (NSCLC), with emphasis on comprehensive genomic profiling, minimal residual disease (MRD) detection by circulating tumor DNA (ctDNA), and the translation of biomarkers into targeted and immunotherapy strategies.MethodsSystematic review registered in PROSPERO (CRD420251076423). Searches of PubMed, Scopus, Web of Science, and Embase (January 2015-April 2025) followed PRISMA 2020/PRISMA-S. From 4640 records, 890 duplicates were removed; 3750 titles/abstracts were screened; 150 full texts were assessed; 75 studies met inclusion criteria. Risk of bias used Newcastle-Ottawa Scale (NOS) for observational studies and Cochrane RoB 2 tool for randomized controlled trials; certainty was summarized with GRADE where applicable.ResultsActionable alterations (e.g. EGFR, ALK, KRAS, MET, RET, BRAF, NTRK) are prevalent in early-stage NSCLC and comparable to advanced disease, supporting routine comprehensive genomic profiling in curative-intent settings. Next-generation sequencing (NGS) and ctDNA enable the detection of MRD, earlier relapse prediction, and dynamic treatment monitoring. Perioperative strategies integrating targeted therapy and immunotherapy (e.g. adjuvant EGFR-TKI, neoadjuvant chemo-immunotherapy) improve pathological and disease-free outcomes in selected biomarker-defined populations. Evidence profiles generally show low-to-moderate risk of bias and moderate-to-high certainty for key outcomes related to profiling and MRD, with heterogeneity across platforms and endpoints.ConclusionsMolecular advances-particularly broad NGS and ctDNA-based MRD-are reshaping the perioperative management of early and locally advanced NSCLC, enabling precision selection for targeted and immunotherapy approaches. Standardization of testing workflows and reporting, and cost-effective implementation are priorities for equitable adoption and for future trials that combine NGS, MRD, and multi-omic/AI-driven risk stratification.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".