Objective To 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. Methods Systematic 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. Results Actionable 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. Conclusions Molecular 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.
Wael Abdo Hassan (Tue,) studied this question.