TY - JOUR
T1 - Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose
T2 - A Multicenter Validation Study
AU - Kort, Sharina
AU - Brusse-Keizer, Marjolein
AU - Schouwink, Hugo
AU - Citgez, Emanuel
AU - de Jongh, Frans H.
AU - van Putten, Jan W.G.
AU - van den Borne, Ben
AU - Kastelijn, Elisabeth A.
AU - Stolz, Daiana
AU - Schuurbiers, Milou
AU - van den Heuvel, Michel M.
AU - van Geffen, Wouter H.
AU - van der Palen, Job
N1 - Funding Information:
This work was supported by the eNose Company.
Publisher Copyright:
© 2022 American College of Chest Physicians
PY - 2023/3
Y1 - 2023/3
N2 - Background: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. Research Question: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? Study Design and Methods: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. Results: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. Interpretation: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. Clinical Trial Registration: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025
AB - Background: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. Research Question: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? Study Design and Methods: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. Results: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. Interpretation: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. Clinical Trial Registration: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025
KW - electronic nose
KW - exhaled breath
KW - lung cancer
KW - validation
KW - 2023 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85148664639&partnerID=8YFLogxK
U2 - 10.1016/j.chest.2022.09.042
DO - 10.1016/j.chest.2022.09.042
M3 - Article
C2 - 36243060
AN - SCOPUS:85148664639
SN - 0012-3692
VL - 163
SP - 697
EP - 706
JO - Chest
JF - Chest
IS - 3
ER -
