Evidence map›Paper›PMID 42444870›Full record

ArticleJournal of thoracic disease2026

Optimal drug regimens for squamous non-small cell lung cancer: a systematic review and a Bayesian network meta-analysis of randomized trials.

Yan Ye, Yaqian Li, Nuoyu Yan, Lan Sun

Abstract read
In one paragraph

Article in Journal of thoracic disease, 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

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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

4 authors.

Yan YeDepartment of Oncology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
Yaqian LiDepartment of Oncology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
Nuoyu YanDepartment of Oncology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
Lan SunDepartment of Oncology, Bishan Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Current treatment options for patients with squamous non-small cell lung cancer (sqNSCLC) include immune checkpoint inhibitors combined with chemotherapy, monotherapy with immune therapy, and platinum-based doublet chemotherapy, among others. However, there is still a lack of systematic comparisons regarding the relative efficacy and safety of these regimens. This study aims to comprehensively assess the clinical benefits [such as overall survival (OS), progression-free survival (PFS)] and adverse event rates across different treatment strategies via a network meta-analysis, providing evidence-based guidance for individualized treatment decisions in sqNSCLC patients. Methods: We searched PubMed, Embase, Cochrane Library, and Web of Science from inception to May 3, 2025 for eligible randomized controlled trials (RCTs). The primary outcome measures were PFS and OS; secondary outcomes included objective response rate (ORR) and grade ≥3 treatment-related adverse events (≥3 TRAEs). A network meta-analysis was carried out using Stata 16.0 and R 4.4.0. Results: A total of 32 RCTs, involving 9,652 participants, were included. This study analyzed 18 different treatment strategies. The network meta-analysis showed that for PFS, paclitaxel-based doublet chemotherapy combined with programmed cell death-1 and programmed death-ligand 1 [PD-(L)1] checkpoint inhibitors [surface under the cumulative ranking curve (SUCRA): 96.52%] and gemcitabine-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors (SUCRA: 85.16%) were the most effective. For OS, paclitaxel-based doublet chemotherapy combined with a Toll-like receptor-2 agonist (CADI-05) (SUCRA: 92.11%), paclitaxel-based doublet chemotherapy combined with PD-(L)1 and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) checkpoint inhibitors (SUCRA: 89.65%), and paclitaxel-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors (SUCRA: 83.58%) were the top three choices. The combination of paclitaxel-based doublet chemotherapy with PD-(L)1 checkpoint inhibitors (SUCRA: 87.00%) was associated with the best improvement in ORR. Conclusions: For first-line treatment of sqNSCLC, the current evidence supports the use of paclitaxel-based doublet chemotherapy combined with PD-(L)1 checkpoint inhibitors as one of the preferred treatment options. However, more high-quality trials are needed to confirm these findings.

Indexed as

chemotherapylung squamous cell carcinoma (lung SCC)network meta-analysis (NMA)Non-small cell lung cancer (NSCLC)programmed cell death-1-programmed death-ligand 1 blockade (PD-1-PD-L1 blockade)

Identifiers

PMID42444870
PMCPMC13358752

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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.