Application of exhaled breath analysis using a graphene sensor array for lung cancer screening and diagnosis: A prospective cohort study of 4 580 patients_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

 HE Zhengfu , CHEN Qiaofen , WU Jianmin

1. Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, P. R. China;
2. Hangzhou Huixin Sensing Technology Co., Ltd., Hangzhou, 310058, P. R. China;
3. Department of Chemistry, Zhejiang University, Hangzhou, Zhejiang Province 310058, P. R. China;

Corresponding author：

HE Zhengfu, Email: hezhengfu@zju.edu.cn

Keywords：

Lung cancer; screening; exhaled breath; sensor; non-invasive

DOI：

10.7507/1007-4848.202506061

Video：

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Objective To explore a novel method for early lung cancer screening based on exhaled breath analysis. Methods This study enrolled patients with suspected pulmonary malignancies and healthy individuals undergoing physical examinations at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Qingchun and Qiantang campuses) from September 2023 to June 2024. Enrolled subjects were categorized into a lung cancer group, a benign nodule/tumor group, and a healthy control group. Exhaled breath samples were collected using a sensor array constructed from multiple graphene composite materials to capture breath fingerprints. Based on the collected data, screening and diagnostic models for lung cancer were developed and their performance was evaluated. Results A total of 4 580 subjects were included. Among them, 3 195 were pathologically diagnosed with pulmonary malignancies, including 1 394 males and 1 801 females with a mean age of (58.93±12.37) years, 599 were diagnosed with benign nodules/tumors (339 males, 260 females; mean age: 57.10±11.06 years), and 786 were healthy controls with no pulmonary nodules detected on chest CT (420 males, 366 females; mean age: 29.75±9.32 years). The screening model for high-risk populations (distinguishing patients with lung cancer/high-risk pulmonary nodules from healthy individuals) demonstrated excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.926. At the optimal Youden’s index (cutoff threshold of 63.5%), the external test set achieved a specificity of 85.2%, a sensitivity of 88.4%, and an accuracy of 86.8%. The diagnostic model (distinguishing patients with lung cancer/premalignant lesions from those with benign pulmonary nodules/healthy individuals) achieved an AUC of 0.818. At its optimal Youden’s index (cutoff threshold of 47.0%), the external test set showed a specificity of 71.7%, a sensitivity of 77.3%, and an accuracy of 74.5%. Conclusion The non-invasive breath analysis platform based on a sensor array, developed in this study, can achieve rapid and relatively accurate lung cancer screening by analyzing breath fingerprints. This confirms the feasibility of this technology for early lung cancer screening and holds promise for facilitating the early detection and intervention of lung cancer.

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1. Zheng RS, Chen R, Han BF, et al. Cancer incidence and mortality in China, 2022. Chin J Oncol, 2024, 46(3): 221-231.
2. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent, 2024, 4(1): 47-53.
3. Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis, 2018, 9(2): 117.
4. Furuhashi T, Toda K, Weckwerth W. Review of cancer cell volatile organic compounds: their metabolism and evolution. Front Mol Biosci, 2025, 11: 1499104.
5. Furuhashi T, Toda K, Weckwerth W. Review of cancer cell volatile organic compounds: their metabolism and evolution. Front Mol Biosci, 2025, 11: 1499104.
6. Zou Y, Hu Y, Jiang Z, et al. Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study. Ann Med, 2022, 54(1): 790-802.
7. Mathew TL, Pownraj P, Abdulla S, et al. Technologies for clinical diagnosis using expired human breath analysis. Diagnostics (Basel), 2015, 5(1): 27-60.
8. Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, et al. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy, 2021, 17(1): 1-382.
9. Kort S, Brusse-Keizer M, Gerritsen JW, et al. Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters. ERJ Open Res, 2020, 6(1): 00221-2019.
10. Kort S, Brusse-Keizer M, Schouwink H, et al. Diagnosing non-small cell lung cancer by exhaled breath profiling using an electronic nose: a multicenter validation study. Chest, 2023, 163(3): 697-706.
11. Jia Z, Thavasi V, Venkatesan T, et al. Breath analysis for lung cancer early detection: a clinical study. Metabolites, 2023, 13(12): 1197.
12. Chou H, Godbeer L, Allsworth M, et al. Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform. Metabolomics, 2024, 20(4): 72.
13. Zhu X, Liu D, Chen Q, et al. A paper-supported graphene-ionic liquid array for E-nose application. Chem Commun (Camb), 2016, 52(14): 3042-3045.
14. Liu X, Chen Q, Xu S, et al. A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection. Adv Sci (Weinh), 2024, 11(34): e2401695.
15. Chen Q, Guo X, Jiang Y, et al. A mobile E-nose prototype for online breath analysis. Adv Sens Res, 2023: 2300018.
16. Chen Q, Chen Z, Liu D, et al. Constructing an E-nose using metal-ion-induced assembly of graphene oxide for diagnosis of lung cancer via exhaled breath. ACS Appl Mater Interfaces, 2020, 12(15): 17713-17724.
17. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA: ACM, 2016: 785-794.
18. Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics, 2018, 15(1): 41-51.
19. Hastie T, Tibshirani R, Friedman JH, et al. The elements of statistical learning: data mining, inference, and prediction: 2. Springer, 2009.
20. Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
21. Li N, Tan F, Chen W, et al. One-off low-dose CT for lung cancer screening in China: a multicentre, population-based, prospective cohort study. Lancet Respir Med, 2022, 10(4): 378-391.
22. Nekolla EA, Brix G, Griebel J. Lung cancer screening with low-dose CT: radiation risk and benefit-risk assessment for different screening scenarios. Diagnostics (Basel), 2022, 12(2): 364.
23. Slatore CG, Wiener RS. Pulmonary nodules: a small problem for many, severe distress for some, and how to communicate about it. Chest, 2018, 153(4): 1004-1015.
24. Jin Y, Yang F, Chen K. An overview of current development and barriers on liquid biopsy in patients with early-stage non-small-cell Lung cancer. Hol Integr Oncol, 2023, 2(1): 43.
25. Kwon HJ, Park UH, Goh CJ, et al. Enhancing lung cancer classification through integration of liquid biopsy multi-omics data with machine learning techniques. Cancers (Basel), 2023, 15(18): 4556.
26. Sikosek T, Horos R, Trudzinski F, et al. Early detection of lung cancer using small RNAs. J Thorac Oncol, 2023, 18(11): 1504-1523.
27. Deveson IW, Gong B, Lai K, et al. Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology. Nat Biotechnol, 2021, 39(9): 1115-1128.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesApplication of exhaled breath analysis using a graphene sensor array for lung cancer screening and diagnosis: A prospective cohort study of 4 580 patients

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