Mass spectrometry-based analysis of exhaled gases: a promising diagnostic tool for lung cancer detection

Abstract Purpose Exhaled gas analysis is a non-invasive and straightforward method for the diagnosis of lung cancer. This study aimed to assess whether characteristic compounds in exhaled gas can serve as specific diagnostic markers for lung cancer. Methods This study enrolled 410 participants, comprising 102 healthy individuals and 308 lung cancer patients. Participants' exhaled gases were collected using the in-house fabricated exhaled gas collection device. The exhaled gases were then detected using extractive electrospray ionization mass spectrometry (EESI-MS). Subsequently, partial least squares discriminant analysis (PLS-DA) was conducted to screen for significantly different compounds. A lung cancer diagnostic model was constructed through the support vector machine (SVM) algorithm. Results A total of 17 and 11 characteristic compounds common to cohort 1 and cohort 2 were obtained by PLS-DA analysis in both positive and negative ion modes. A lung cancer diagnostic model was constructed based on Cohort 3. The model contained 17 characteristic compounds in positive ion mode with 92.50% accuracy and 92.39% sensitivity. In negative ion mode, the model with 11 characteristic compounds also performed excellently with an accuracy of 90.83% and a sensitivity of 94.57%. Cross-validation in Cohorts 1 and 2 confirmed the model's robustness, with area under the curve values surpassing 0.97 and both accuracy and sensitivity rates exceeding 90.00%. Ultimately, 28 significantly differential metabolites were identified. Conclusions This study underscores the potential of exhaled gas analysis as a reliable method for diagnosis of lung cancer. Trial registration ClinicalTrials.gov NCT06086587, registered on 26 September 2023.