Advanced strategy for cancer detection based on volatile organic compounds in breath.

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 difference in AUCs between heterogeneous and homogeneous sensor groups, suggesting that simplified detection systems may be feasible. Despite these promising results, standardization of protocols and methodological consistency remain critical challenges. Future efforts should focus on large-scale, well-designed clinical trials to validate and optimize VOCs-based breath tests, enhancing their diagnostic reliability and translational potential in oncology.