Evidence map›Paper›PMID 42130296›Full record

ArticleAnnals of medicine2026

Cost-effectiveness of adjuvant therapy with camrelizumab in high-risk locoregionally advanced nasopharyngeal carcinoma: from the perspective of the Chinese healthcare system.

Rui Wang, Yuxin Feng, Rong Long, Liangfang Shen, Qin Zhou

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Article in Annals of medicine, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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

5 authors.

Rui WangDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yuxin FengDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Rong LongDepartment of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Liangfang ShenDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Qin ZhouDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID 0000-0002-5003-161X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveImmune checkpoint inhibitors, particularly programmed cell death 1 inhibitors, have shown promising clinical outcomes in the adjuvant treatment of locoregionally advanced nasopharyngeal carcinoma (LA-NPC), offering a more effective and less toxic treatment option. This study aimed to explore the cost-effectiveness of adjuvant immunotherapy with camrelizumab for patients with LA-NPC from the perspective of the Chinese healthcare system.

methodsA Markov model was developed to evaluate the health and economic outcomes of adjuvant camrelizumab versus standard therapy (observation) in high-risk LA-NPC. Clinical efficacy and safety data were derived from the phase 3 DIPPER trial. The model incorporated three distinct health states and simulated the outcomes over a lifetime horizon. Key endpoints included overall cost, quality-adjusted life year (QALY), incremental cost-effectiveness ratio (ICER), and incremental net health benefit (INHB), assessed at a predefined willingness-to-pay (WTP) threshold of $39,613 per QALY. One-way and probabilistic sensitivity analyses were conducted to assess the robustness of the model.

resultsCompared with standard therapy, adjuvant camrelizumab for high-risk patients with LA-NPC resulted in an added 4.39 QALYs at an incremental cost of $21,539, yielding an ICER of $4,910/QALY and an INHB of 3.85 QALYs. At the predefined WTP, camrelizumab demonstrated a 98.6% probability of being a cost-effective adjuvant treatment for LA-NPC in China. The health utility value of event-free survival was the most significant parameter influencing the model results. Sensitivity analysis results supported the robustness of the model.

conclusionsIn China, adjuvant immunotherapy with camrelizumab following induction-concurrent chemoradiotherapy offers a potentially cost-effective approach for the management of patients with high-risk LA-NPC, yielding an ICER significantly below the predefined WTP threshold.

Indexed as

Antibodies, Monoclonal, HumanizedImmune Checkpoint InhibitorsNasopharyngeal CarcinomaNasopharyngeal NeoplasmsChemotherapy, AdjuvantChinaCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansMaleMarkov ChainsQuality-Adjusted Life YearsAntibodies, Monoclonal, HumanizedcamrelizumabImmune Checkpoint Inhibitorsadjuvant immunotherapycamrelizumabcost-effectivenessLocoregionally advanced nasopharyngeal carcinoma

Identifiers

PMID42130296
PMCPMC13178033

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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.