Ann Oncol

Electronic nose technology shows promise in lung cancer detection

April 11, 2025

Study details: This multi-center prospective external validation study aimed to assess the diagnostic performance of an electronic nose (eNose) for lung cancer detection. Adults with clinical and/or radiological suspicion of lung cancer were recruited from thoracic oncology outpatient clinics in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The study evaluated both an original eNose model and a new model tailored to the target population.

Results: The study included 364 participants. The original eNose model achieved a Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) of 0.92 in COPD patients and 0.80 in all participants. At 95% sensitivity, the specificity, positive predictive value (PPV), and negative predictive value (NPV) were 72%, 51%, 95%, and 74%, respectively. The new eNose model, in the validation cohort, identified lung cancer with an ROC-AUC of 0.83, 94% sensitivity, 63% specificity, PPV of 79%, and NPV of 89%. Detection was consistent across various tumor characteristics, disease stages, diagnostic centers, and clinical characteristics.

Clinical impact: eNose analysis of exhaled breath is a reliable, non-invasive method for lung cancer detection in outpatient settings. The high sensitivity and negative predictive value suggest that eNose technology could be effectively integrated into clinical practice for early lung cancer screening, potentially improving patient outcomes through earlier diagnosis and treatment.

Source:

Buma AIG, et al. (2025, March 31). Ann Oncol. Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study. https://pubmed.ncbi.nlm.nih.gov/40174676/