Screening and diagnosis of combustion smoking with exhaled breath analysis via proton transfer reaction time-of-flight mass spectrometry
Abstract
BACKGROUND
Tobacco smoking is a leading cause for lung cancer, even for non-smokers. Assessing exposure, particularly to passive or secondhand smoke, is challenging due to reliance on self-reporting and the invasiveness of traditional biomarker tests. Exhaled breath analysis via Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-TOF-MS) provides a non-invasive approach for detecting relevant volatile organic compounds (VOCs). This study aimed to develop and validate a PTR-TOF-MS-based model to classify individuals according to smoking status and exposure level.
METHODS
Exhaled breath from 3942 participants (active smokers, passive smokers, non-smokers) were collected by a standardized offline breath sampler and subsequently analyzed using high-resolution PTR-TOF-MS. Spectral m/z features were utilized to train, validate, and test seven machine learning models (6:2:2 data split). Performance was evaluated using accuracy, sensitivity, specificity, and Area Under the Curve (AUC), with optimized hyperparameters.
RESULTS
Distinct VOC profiles were identified for active, passive, and non-smokers, indicating PTR-TOF-MS sensitivity to varying exposure levels. XGBoost model demonstrated superior performance, effectively differentiating active smokers from non-smokers with high test set accuracy of 92.08% (95% CI: 87.91%-95.17%), sensitivity of 90.83% (95% CI: 84.19%-95.33%), specificity of 93.33% (95% CI: 87.29%-97.08%), and AUC of 0.9678 (95% CI: 0.9438-0.9867). Key…
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