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

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.