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Non-invasive Breath Analysis of Pulmonary Nodules
Open accessAbstract
Introduction: The search for non-invasive diagnostic methods of lung cancer (LC) has led to new avenues of research, including the exploration of the exhaled breath. Previous studies have shown that LC can, in principle, be detected through exhaled-breath analysis. This study evaluated the potential of exhaled-breath analysis for the distinction of benign and malignant pulmonary nodules (PNs). Methods: Breath samples were taken from 72 patients with PNs in a prospective trial. Profiles of volatile organic compounds were determined by (1) gas chromatography/mass spectrometry (GC-MS) combined with solid-phase microextraction and (2) a chemical nanoarray. Results: Fifty-three PNs were malignant and 19 were benign with similar smoking histories and comorbidities. Nodule size (mean ± SD) was 2.7 ± 1.7 versus 1.6 ± 1.3 cm (p = 0.004), respectively. Within the malignant group, 47 were non–small-cell lung cancer and six were small-cell lung cancer. Thirty patients had early-stage disease and 23 had advanced disease. Gas chromatography/mass spectrometry analysis identified a significantly higher concentration of 1-octene in the breath of LC, and the nanoarray distinguished significantly between benign versus malignant PNs (p < 0.0001; accuracy 88 ± 3%), between adeno- and squamous-cell carcinomas [LINE SEPARATOR](p < 0.0001; 88 ± 3%) and between early stage and advanced disease (p < 0.0001; 88 ± 2%). Conclusions: In this pilot study, breath analysis discriminated benign from malignant PNs in a high-risk cohort based on LC-related volatile organic compound profiles. Furthermore, it discriminated adeno- and squamous-cell carcinoma and between early versus advanced disease. Further studies are required to validate this noninvasive approach, using a larger cohort of patients with PNs detected by computed tomography.
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Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
Siegel, R., Naishadham, D., & Jemal, A. (2012) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20138 · Cited by 10,679
Cancer cell metabolism: warburg and beyond
Hsu, P. P., & Sabatini, D. M. (2008) · Cell · doi:10.1016/j.cell.2008.08.021 · Cited by 2,452 · cited 1×
“It would be reasonable to expect a higher quantity and variety of metabolites released by the SCLC, because SCLC cells are rapidly dividing cells that require more adenosine triphosphate, nucleotides, fatty acids, membrane lipids, and proteins.(30) This assumption is based on our previous report of pronounced differen…”
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Cited by
Flexible Sensors Based on Nanoparticles
Segev-Bar, M., & Haick, H. (2013) · ACS Nano · doi:10.1021/nn402728g · Cited by 511
Self-citationReview—Non-Invasive Monitoring of Human Health by Exhaled Breath Analysis: A Comprehensive Review
Das, S., & Pal, M. (2020) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ab67a6 · Cited by 466
Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing
Hu, W., Wan, L., Jian, Y., Ren, C., Jin, K., Su, X., Bai, X., Haick, H., Yao, M., & Wu, W. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201800488 · Cited by 366
Self-citationNanomaterial-Based Sensors for Detection of Disease by Volatile Organic Compounds
Broza, Y. Y., & Haick, H. (2013) · Nanomedicine · doi:10.2217/nnm.13.64 · Cited by 358
Self-citationAdvanced Materials for Health Monitoring with Skin‐Based Wearable Devices
Jin, H., Abu‐Raya, Y. S., & Haick, H. (2017) · Advanced Healthcare Materials · doi:10.1002/adhm.201700024 · Cited by 317
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Metadata
- Authors
- Peled, N., Hakim, M., Bunn, P. A., Miller, Y. E., Kennedy, T. C., Mattei, J., Mitchell, J. D., Hirsch, F. R., & Haick, H.
- Venue
- Journal of Thoracic Oncology
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 7
- Issue
- 10
- Pages
- 1528-1533
- ISSN
- 1556-0864 (ISSN-L)
- OpenAlex
- W1998960474
- Cited by
- 194
- References
- 3
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- bronze
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