Evidence map›Paper›PMID 42866186›Full record

ArticleJournal of thoracic disease2026

Preoperative fractional exhaled nitric oxide in differentiating pulmonary nodules and pathological examination of non-small cell lung cancer: a retrospective cohort study.

Xue Wang, Huang-Man Xiao, Yong-Yin Gao, Xue-Yan Bian, Yan-Li Qiu, Yue Li

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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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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.

Xue WangDepartment of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Huang-Man XiaoDepartment of Dermatology, Children's Hospital, Tianjin University, Tianjin, China.
Yong-Yin GaoDepartment of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Xue-Yan BianDepartment of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Yan-Li QiuDepartment of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Yue LiDepartment of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and preoperative differentiation of pulmonary nodules remains challenging. We aimed to investigate the clinical value of preoperative fractional exhaled nitric oxide (FENO) in distinguishing benign pulmonary nodules from malignant pulmonary nodules and characterizing its relationship with pathological features of non-small cell lung cancer (NSCLC). Methods: After exclusions from 1,046 consecutive patients, 550 patients were included and classified into malignant (NSCLC) and benign groups based on postoperative pathological examination. Using the Mann-Whitney U test, FENO levels were compared between the two groups. Receiver operating characteristic (ROC) curve analysis was conducted with the optimal cutoff value determined based on the Youden index. Multivariate binary logistic regression analysis was conducted to assess the independent association of FENO with the malignant behavior of NSCLC. In the malignant group, FENO levels were compared across pathological types, tumor-node-metastasis (TNM) stages, and programmed death-ligand 1 (PD-L1) expression groups using the Mann-Whitney U test and the Kruskal-Wallis H test. Subsequently, multivariate linear regression analysis was conducted to explore the independent association between FENO and PD-L1 expression. Results: Preoperative levels of FENO were significantly higher in the malignant group than in the benign group {median [interquartile range (IQR)]: 23 [18-29] Conclusions: Preoperative levels of FENO demonstrated moderate diagnostic value for malignant nodules. It also exhibited exploratory associations with the squamous histological subtype and PD-L1 expression. Prospective, multicenter validation is needed before clinical adoption.

Indexed as

adenocarcinomaFractional exhaled nitric oxide (FENO)lung cancerprogrammed death-ligand 1 (PD-L1)pulmonary nodules

Identifiers

PMID42866186
PMCPMC13655882

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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.