TY - JOUR
T1 - Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath
AU - de Vries, Rianne
AU - Farzan, Niloufar
AU - Fabius, Timon
AU - de Jongh, Frans H. C.
AU - Jak, Patrick M. C.
AU - Haarman, Eric G.
AU - Snoey, Erik
AU - in ’t Veen, Johannes C. C. M.
AU - Dagelet, Yennece W. F.
AU - Maitland-van der Zee, Anke-Hilse
AU - Lucas, Annelies
AU - van den Heuvel, Michel M.
AU - Wolf-Lansdorf, Marguerite
AU - Muller, Mirte
AU - Baas, Paul
AU - Sterk, Peter J.
N1 - Funding Information: The BreathCloud project was sponsored by the Lung Foundation Netherlands and the Dutch VriendenLoterij. The study described in this article is a spin-out of the BreathCloud project and was carried out without additional funding. Publisher Copyright: © 2023 The Author(s)
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Background: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. Research Question: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? Study Design and Methods: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. Results: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). Interpretation: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.
AB - Background: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. Research Question: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? Study Design and Methods: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. Results: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). Interpretation: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.
KW - COPD
KW - breath test
KW - breathomics
KW - eNose
KW - early detection
KW - lung cancer
UR - http://www.scopus.com/inward/record.url?scp=85166635666&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.chest.2023.04.050
DO - https://doi.org/10.1016/j.chest.2023.04.050
M3 - Article
C2 - 37209772
SN - 0012-3692
VL - 164
SP - 1315
EP - 1324
JO - Chest
JF - Chest
IS - 5
ER -