Exhaled Breath Analysis Using a Novel Electronic Nose for Different Respiratory Disease Entities.

Abstract

Purpose: Electronic noses (eNose) and gas chromatography mass spectrometry (GC-MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC-MS.

Materials and methods: Patients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019-July 2022). Exhaled breath samples were collected for eNose and GC-MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed.

Results: A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1-0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients.

Conclusions: Our study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC-MS identifying candidate VOC biomarkers.