Point-of-Care Breath Volatile Organic Compound Analysis as a Tool for Lung Cancer Screening: A Pilot Feasibility Study
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Background
Low-dose computed tomography (LDCT) screening reduces lung cancer mortality but is limited by low uptake and associated with high rates of false-positives and indeterminate-nodules. Breath volatile organic compound (VOC) analysis is a non-invasive candidate biomarker approach that could complement LDCT, but prior work has relied on laboratory-based high-resolution mass spectrometry (HRMS), limiting point-of-care deployment.
Methods
In this pilot study, breath samples were collected from 40 patients with treatment-naïve, pathologically confirmed non-small cell lung cancer (NSCLC) and 25 lung-cancer-screening-eligible healthy controls. Paired samples were analyzed via a compact point-of-care GC-MS platform (CLARION) and a laboratory HRMS reference. Diagnostic classification models were built independently for each platform using elastic net logistic regression with leave-one-out cross-validation, and performance was evaluated by area under the receiver operating characteristic curve (AUC).
Results
CLARION identified 103 VOCs across breath specimens, compared to over 900 identified by HRMS. Despite this difference in panel size, CLARION achieved diagnostic performance nearly identical to HRMS for distinguishing NSCLC cases from controls (AUC 0.864 vs. 0.863). Compared to controls, performance statistics were similar for early-stage NSCLC (AUC 0.854 vs. 0.841) and adenocarcinoma (AUC 0.770 vs. 0.787). VOCs of interest include p-cymene, phenol, propylbenzene, tetradecane, β-ocimene, 2,3-dihydro-indole, and 1-methylthio-(Z)-1-propene.
Conclusion
A compact, point-of-care breath GC-MS platform achieved diagnostic performance for NSCLC detection comparable to a laboratory HRMS reference despite a substantially smaller detected VOC panel. These findings support continued development of point-of-care breath VOC testing as a non-invasive, field-deployable complement to LDCT-based lung cancer screening.
