The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array
Open accessAbstract
BACKGROUND: The pattern of volatile organic compounds (VOCs) in the exhaled breath of patients with lung cancer may be unique. New sensor systems that detect patterns of VOCs have been developed. One of these sensor systems, a colorimetric sensor array, has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge. The colours of these spots change based on the chemicals with which they come into contact. In this proof of principle study, the ability of this sensor system to detect a pattern of VOCs unique to lung cancer is assessed. METHODS: Individuals with lung cancer, those with other lung diseases and healthy controls performed tidal breathing of room air for 12 min while exhaling into a device designed to draw their breath across a colorimetric sensor array. The colour changes that occurred for each individual were converted into a numerical vector. The vectors were analysed statistically, using a random forests technique, to determine whether lung cancer could be predicted from the responses of the sensor. RESULTS: 143 individuals participated in the study: 49 with non-small cell lung cancer, 18 with chronic obstructive pulmonary disease 15 with idiopathic pulmonary fibrosis 20 with pulmonary arterial hypertension 20 with sarcoidosis and 21 controls. A prediction model was developed using observations from 70% of the subjects. This model was able to predict the presence of lung cancer in the remaining 30% of subjects with a sensitivity of 73.3% and a specificity of 72.4% (p = 0.01). CONCLUSIONS: The unique chemical signature of the breath of patients with lung cancer can be detected with moderate accuracy by a colorimetric sensor array.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Vestbo, J., Hurd, S. S., Agustí, A. G., Jones, P. W., Vogelmeier, C., Anzueto, A., Barnes, P. J., Fabbri, L. M., Martinez, F. J., Nishimura, M., et al. (2013) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201204-0596pp · Cited by 15,983
PAUWELS, R. A., BUIST, A. S., CALVERLEY, P. M. A., JENKINS, C. R., & HURD, S. S. (2001) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.163.5.2101039 · Cited by 6,587
Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., Smigal, C., & Thun, M. J. (2006) · CA: A Cancer Journal for Clinicians · doi:10.3322/canjclin.56.2.106 · Cited by 5,314
A colorimetric sensor array for odour visualization
Rakow, N. A., & Suslick, K. S. (2000) · Nature · doi:10.1038/35021028 · Cited by 1,490
Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study
Phillips, M., Gleeson, K., Hughes, J. M. B., Greenberg, J., Cataneo, R. N., Baker, L., & McVay, W. P. (1999) · The Lancet · doi:10.1016/s0140-6736(98)07552-7 · Cited by 918
Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., D'Arcangelo, G., Roscioni, C., Finazzi-Agrò, A., & D'Amico, A. (2003) · Biosensors and Bioelectronics · doi:10.1016/s0956-5663(03)00086-1 · Cited by 663
Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath
Machado, R. F., Laskowski, D., Deffenderfer, O., Burch, T., Zheng, S., Mazzone, P. J., Mekhail, T., Jennings, C., Stoller, J. K., Pyle, J., et al. (2005) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200409-1184oc · Cited by 602
Self-citationPatz, E. F., Goodman, P. C., & Bepler, G. (2000) · New England Journal of Medicine · doi:10.1056/nejm200011303432208 · Cited by 599
Detection of Lung Cancer With Volatile Markers in the Breatha
Phillips, M., Cataneo, R. N., Cummin, A. R. C., Gagliardi, A. J., Gleeson, K., Greenberg, J., Maxfield, R. A., & Rom, W. N. (2003) · Chest · doi:10.1378/chest.123.6.2115 · Cited by 567
Cited by
Electronic Nose: Current Status and Future Trends
Röck, F., Barsan, N., & Weimar, U. (2008) · Chemical Reviews · doi:10.1021/cr068121q · Cited by 1,437
Review—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
Organic field-effect transistor-based flexible sensors
Yuvaraja, S., Nawaz, A., Liu, Q., Dubal, D., Surya, S. G., Salama, K. N., & Sonar, P. (2020) · Chemical Society Reviews · doi:10.1039/c9cs00811j · Cited by 386
A review of breath analysis for diagnosis of human health
Kim, K.-H., Jahan, S. A., & Kabir, E. (2012) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2011.09.013 · Cited by 345
An investigation on electronic nose diagnosis of lung cancer
D’Amico, A., Pennazza, G., Santonico, M., Martinelli, E., Roscioni, C., Galluccio, G., Paolesse, R., & Di Natale, C. (2010) · Lung Cancer · doi:10.1016/j.lungcan.2009.11.003 · Cited by 330
Metadata
- Authors
- Mazzone, P. J., Hammel, J., Dweik, R., Na, J., Czich, C., Laskowski, D., & Mekhail, T.
- Venue
- Thorax
- Publisher
- BMJ
- Published
- 2007-02-27
- Volume
- 62
- Issue
- 7
- Pages
- 565-568
- ISSN
- 0040-6376 (ISSN-L)
- OpenAlex
- W2116349217
- Cited by
- 329
- References
- 19
- FWCI
- 10.97
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array. https://app.cochat.ai/papers/10.1136/thx.2006.072892
- The paper
- Mazzone, P. J., Hammel, J., Dweik, R., Na, J., Czich, C., Laskowski, D., & Mekhail, T. (2007). Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array. Thorax. https://doi.org/10.1136/thx.2006.072892
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
