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Electronic nose (eNose) technology shows promising results as a reliable, non-invasive, point-of-care diagnostic tool for lung cancer. The study, recently published in the Annals of Oncology, offers new insights into how breath analysis can support faster and more accurate lung cancer detection in clinical settings.
Although an accurate and timely diagnosis is essential for improving the prognosis and management of lung cancer patients, the current diagnostic work-up of individuals suspected of having lung cancer is still suboptimal. Electronic nose (eNose) analysis of exhaled breath based on pattern recognition may be used to accurately and more quickly identify patients who require invasive diagnostic procedures and to support clinical decision-making.
However, key challenges hindering its clinical adoption include inadequate descriptions of analytical methods, the influence of endogenous and exogenous factors, limited knowledge of individual volatile organic compounds (VOCs) and the biological pathways driving the eNose breath profile, potential interference from other cancers in control groups and lung cancer patients, small sample sizes, and the absence of multi-center and prospective external validation studies.
A step forward in breath-based diagnostics
Alessandra Buma (Department of Respiratory Medicine, Radboudumc), Benthe Muntinghe-Wagenaar (Department of Respiratory Medicine, University Medical Center Groningen) and colleagues addressed these key issues in their study and confirmed that eNose technology enables reliable, non-invasive, point-of-care lung cancer detection at thoracic oncology outpatient clinics. Importantly, the authors found that accurate detection was consistent across tumor characteristics, disease stage, diagnostic centers, and clinical characteristics, underscoring the applicability of eNose analysis in clinical practice.
Furthermore, simultaneous identification of individual VOCs showed that the lung cancer breath profile is likely the result of shifts in molecular subgroup abundances rather than significant changes in individual VOCs. The authors hereby highlighted the advantage of eNose technology based on pattern recognition over individual VOC identification for diagnosing diseases with complex underlying biological mechanisms.
Why this matters
By addressing the key challenges hindering the clinical adoption of exhaled breath analysis by eNose, the study by Buma, Muntinghe-Wagenaar, and colleagues provides the information at the intersection of biomedical science and clinical practice necessary to pursue the final steps towards implementation of this valuable diagnostic biomarker.
Their study contributes to knowledge essential for improving the prognosis and management of lung cancer patients, especially in the era of lung cancer screening where small tumours are increasingly prevalent, and in cases where current diagnostic technologies are limited or invasive.
The authors emphasize that future studies should focus on assessing the diagnostic performance of eNose analysis in the screening setting and determining its clinical utility in routine diagnostic pathways.
This research is part of Radboudumc Reasearch Program: Thoracic Oncology
About the publication
Buma AIG, Muntinghe-Wagenaar MB, van der Noort V, de Vries R, Schuurbiers MMF, Sterk PJ, Schipper S, Meurs J, Cristescu SM, Hiltermann TJN, van den Heuvel MM. Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study. Ann Oncol. 2025 Mar 31:S0923-7534(25)00125-5. doi: 10.1016/j.annonc.2025.03.013. Epub ahead of print. PMID: 40174676.
