Economic evaluations of next-generation sequencing for targeted therapy in non-small cell lung cancer: a systematic...

This study aimed to systematically review model-based economic evaluations of next-generation sequencing for guiding targeted therapy in non-small cell lung cancer.

We searched PubMed, Embase, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, VIP Database, and Wanfang Database from inception to March 20, 2026, to identify economic evaluations of next-generation sequencing for molecular testing in non-small cell lung cancer. Two reviewers independently screened studies, extracted data, and assessed reporting quality using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist. A qualitative synthesis was conducted to summarize study characteristics, model structures, testing strategies, cost-effectiveness results, and sensitivity analyses.

Eighteen model-based economic evaluations were included. Seven modeling approaches were identified, among which hybrid models combining a decision tree with a partitioned survival model were most frequently used, followed by decision tree-Markov models. Most studies evaluated next-generation sequencing against single-gene testing or sequential molecular testing, although the tested genes, panel sizes, comparator strategies, and treatment pathways varied substantially across studies. Epidermal growth factor receptor and anaplastic lymphoma kinase were the most commonly included biomarkers. Overall, most studies suggested that next-generation sequencing was cost-effective compared with conventional testing strategies, but several studies from Asian settings reported unfavorable cost-effectiveness results. Key drivers of model results included treatment costs, time horizon, testing strategy, prevalence of actionable alterations, and turnaround time.

Current evidence suggests that NGS-guided strategies may improve health outcomes in NSCLC, but their economic value remains highly context-dependent. Cost-effectiveness is influenced not only by testing strategies, but also by the availability, accessibility, and cost of matched targeted therapies, as well as turnaround time and other real-world implementation factors. Future evaluations should therefore assess NGS within the broader molecular testing-treatment pathway and better reflect real-world clinical practice.