Evidence map›Paper›PMID 42574211›Full record

ArticleHuman vaccines & immunotherapeutics2026

Cost-effectiveness analysis of finotonlimab in the treatment of advanced recurrent metastatic head and neck squamous cell carcinoma in China.

Yuan Gao, Na Lv, Ruigang Diao

Abstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yuan GaoDepartment of Clinical Pharmacy, Yantai Yuhuangding Hospital, Yantai, Shandong, China.
Na LvDepartment of Clinical Pharmacy, Yantai Yuhuangding Hospital, Yantai, Shandong, China.
Ruigang DiaoDepartment of Clinical Pharmacy, Yantai Yuhuangding Hospital, Yantai, Shandong, China.ORCID 0000-0001-7946-9140

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The SCT-I10A-B301 trial demonstrated efficacy and safety of finotonlimab combined with chemotherapy in patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). However, the economics of this therapy are unknown. This study aims to assess the cost-effectiveness of incorporating finotonlimab into chemotherapy regimens for R/M HNSCC from the viewpoint of the Chinese healthcare system. A Markov model was developed to evaluate the treatment costs and outcomes of finotonlimab combined with chemotherapy for R/M HNSCC. Survival data were obtained from the SCT-I10A-B301 trial. Considering only direct medical costs, survival data, and utilities. The principal result measures employed were the incremental cost-effectiveness ratio (ICER). To identify how parameter uncertainty affected the model, one-way sensitivity analyses, probabilistic sensitivity analysis (PSA), and scenario studies were carried out. The results showed that the financial burden of finotonlimab, when administered concomitantly with chemotherapy ($36,996.99) was $26,072.18 higher than chemotherapy ($10,924.81) but also increased by 0.23QALY (0.89QALY vs. 0.66QALY), with an ICER of $112,384.47/QALY, which was higher than the willingness to pay (WTP) of $40,744/QALY. The model's sensitivity to three factors was particularly pronounced: the utility of PFS, the utility of PD, and the cost of finotonlimab. Scenario analysis showed that a price reduction for finotonlimab could significantly reduce the ICER to near the WTP threshold. Compared with chemotherapy alone, finotonlimab combined with chemotherapy has been determined to be an ineffective, cost-effective therapeutic strategy for the first-line medical treatment of R/M HNSCC in China, and the price discount of finotonlimab can improve its cost-effectiveness.

Indexed as

Antibodies, Monoclonal, HumanizedAntineoplastic Agents, ImmunologicalCost-Benefit AnalysisCost-Effectiveness AnalysisHead and Neck NeoplasmsNeoplasm Recurrence, LocalSquamous Cell Carcinoma of Head and NeckChinaHumansMarkov ChainsAntibodies, Monoclonal, HumanizedAntineoplastic Agents, Immunologicalchemotherapycost-effectivenessFinotonlimabfirst-line medical treatmenthead and neck cancer

Identifiers

PMID42574211
PMCPMC13471096

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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.