Evidence map›Paper›PMID 42498928›Full record

ArticleCost effectiveness and resource allocation : C/E2026

Cost-effectiveness analysis of molecular reflex testing-based targeted therapy in non-small cell lung cancer from a German healthcare payer perspective using a decision tree model.

Leonie Schumacher, Melina Sophie Kurte, Tabea Poos, Markus Thalheimer, Frank Griesinger, Florian Kron

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Article in Cost effectiveness and resource allocation : C/E, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Leonie SchumacherVITIS Healthcare Group, Cologne, Germany. l.schumacher@vitis-healthcare.com.
Melina Sophie KurteVITIS Healthcare Group, Cologne, Germany.
Tabea PoosVITIS Healthcare Group, Cologne, Germany.
Markus ThalheimerDepartment of Quality Management and Medical Controlling, University Hospital Heidelberg, Heidelberg, Germany.
Frank GriesingerPius Hospital Oldenburg, Oldenburg, Germany.
Florian KronVITIS Healthcare Group, Cologne, Germany. f.kron@vitis-healthcare.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReflex testing (RT) - pathologist-initiated molecular testing performed at non-small cell lung cancer (NSCLC) diagnosis - enables rapid identification of actionable genomic alterations for targeted therapy (TT) selection. Despite national and international guideline recommendations, approximately 15% of patients with advanced NSCLC in Germany remain untested for driver mutations. The portion of untested patients in early stages is likely to be even higher.

objectiveThis study simulated the clinical and economic impact of introducing RT for untested NSCLC patients under different implementation scenarios, using a decision-tree model from the German statutory health insurance (SHI) perspective.

methodsA decision-tree model simulated four RT cases (C): (C1) no molecular testing, (C2) testing per German S3 guideline recommendations, (C3) testing according to current TT approvals, and (C4) comprehensive panel testing (standard in Austria). PD-L1 expression was tested in all cases. The untested NSCLC population was projected for 2025 based on SHI demographics. Model inputs comprised RT sensitivity, mutation and stage distribution, drug acquisition costs (Lauer-Taxe, April 2025), and median overall/progression-free survival (mOS/mPFS) per therapeutic strategy. A scenario analysis was conducted assuming NGS test costs of €3,000 per panel to assess their potential influence on cost-effectiveness versus no testing.

resultsAll RT cases (C2-C4) substantially increased actionable mutation detection, enabling earlier TT initiation and lower mean treatment costs versus no molecular testing. C2-C4 were clinically dominant, driven by increased mOS by 5.48-5.47 months and mPFS by 4.89-5.42 months. Model yielded annual cost savings versus no testing of €740 (C2) and €535 (C3/C4) per patient (annual cost savings of €1,175 - €1,621 per life-year gained). When €3,000 next-generation sequencing (NGS) panel costs were included, annual costs per patient increased modestly (C2: €2,260; C3/C4: €2,465 versus no testing), at annual incremental costs of €4,946 - €5,409 per life-year gained.

conclusionsReflex testing across implementation cases, by enabling earlier identification of actionable biomarkers and receipt of matched targeted therapy, improves clinical outcomes and is cost-effective for untested NSCLC patients from the German SHI perspective. Structured initiatives, such as national Network for Genomic Medicine (nNGM), could further expand equitable access to precision oncology in Germany.

Indexed as

Cost-effectiveness-analysisMolecular testingNGSNSCLCTargeted therapy

Identifiers

PMID42498928
PMCPMC13397798

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.