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Cancer Genomics and Diagnostics / Lung Cancer Treatments and Mutations / Genomics and Rare Diseases · Review
Frontiers in Public Health · September 8, 2026
A consensus or society position rather than new primary data.
This systematic review of 18 model-based economic evaluations finds that NGS-guided molecular testing for targeted therapy in NSCLC is generally cost-effective compared to single-gene or sequential testing, but cost-effectiveness is highly context-dependent and influenced by treatment costs, prevalence of actionable alterations, turnaround time, and real-world implementation factors. Notably, several Asian studies reported unfavorable cost-effectiveness results, indicating that conclusions do not generalise uniformly across health systems and settings.
Systematic review of health economic models. Model-based economic evaluations of NGS for molecular testing in non-small cell lung cancer; no restriction on geographic region or health system type.. Intervention: Next-generation sequencing for molecular testing and targeted therapy guidance in NSCLC. Compared with: Single-gene testing or sequential molecular testing strategies. n = 18. Global; seven databases searched including Chinese regional databases; several Asian studies identified with unfavorable results..
18 model-based economic evaluations were included in the systematic review Hybrid models combining decision tree with partitioned survival model were most frequently used, followed by decision tree–Markov models Most studies suggested NGS was cost-effective compared with conventional testing strategies
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Systematic review of economic evidence synthesizing 18 model-based evaluations to inform policy on NGS adoption in NSCLC, with contextual conclusions about cost-effectiveness variability rather than a single definitive clinical or economic finding.
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Aim This study aimed to systematically review model-based economic evaluations of next-generation sequencing for guiding targeted therapy in non-small cell lung cancer. Methods 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. Results 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. Conclusion 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.
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