Advanced strategy for cancer detection based on volatile organic compounds in breath
Abstract
The analysis of volatile organic compounds (VOCs) in exhaled breath has emerged as a promising non-invasive approach for cancer diagnosis, offering advantages in speed, safety, cost-effectiveness, and real-time monitoring. Two primary methodologies are employed for VOCs detection: mass spectrometry (MS)-based techniques, which provide high-precision identification and quantification of individual compounds, and sensor-based pattern recognition methods, which detect disease-specific VOC signatures. Despite their diagnostic potential, inconsistencies in accuracy highlight the need for a comprehensive evaluation of these techniques. This review synthesizes evidence from clinical studies through meta-analysis to assess the diagnostic performance of MS and sensor-based approaches. Furthermore, we examine variations in VOC profiles across cancer types, which may influence diagnostic precision, and discuss key biomarkers, analytical methodologies, current challenges, and future directions in VOCs-based diagnostics. Meta-analysis revealed a high diagnostic accuracy, with a mean area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.91-0.96), sensitivity of 89% (95% CI 87%-90%), and specificity of 87% (95% CI 84%-88%). Notably, no significant difference was observed between MS and sensor-based methods (AUC: 0.91 vs. 0.93, p = 0.286), supporting the potential of sensor technologies for clinical application. Subgroup analysis further indicated no statistical…
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