Complementary Breath Profiling Using eNose and Gas Chromatography‐Mass Spectrometry for Noninvasive Triage of Indeterminate Pulmonary Nodules

ABSTRACT

Breath analysis may improve the noninvasive triage of CT‐detected pulmonary nodules. We prospectively evaluated two complementary breath platforms, an electronic nose (eNose) and gas chromatography‐mass spectrometry (GC‐MS), in patients with newly detected nodules. The eNose cohort included 89 participants (55 malignant and 34 benign), and the GC‐MS cohort included 75 participants (52 malignant and 23 benign). For each platform, three logistic regression models were assessed: clinical‐only, biomarker‐only, and combined clinical + biomarker. For eNose, apparent discrimination improved from the clinical‐only model (AUC 0.67; misclassification rate [MCR] 40.4%) to the sensor‐only model (AUC 0.86; MCR 22.5%), with further improvement in the combined clinical + sensor model (AUC 0.88; MCR 22.5%). Internal validation showed attenuation, with the combined eNose model achieving a leave‐one‐out cross‐validation (LOOCV) AUC of 0.67. For GC‐MS, LASSO logistic regression showed strong apparent discrimination for the VOC‐only (AUC 0.97; MCR 4%), although internal validation indicated attenuation of performance (bootstrap‐corrected AUC 0.64; LOOCV AUC 0.65). The combined clinical + VOC model remained comparably high (AUC 0.96; MCR 8%), outperforming the clinical‐only benchmark (AUC 0.73; MCR 32%). These findings support complementary, noninvasive breath‐based approaches for pulmonary nodule triage and warrant larger multicenter validation studies.