Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study.

Abstract

Background: Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in COPD patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.

Patients and methods: This multi-center prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in The Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose model was assessed in various population subsets based on ROC-AUC, specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training and validation cohort were used.

Results: Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with a ROC-AUC of 0.92 (95% CI: 0.85-0.99) in COPD patients (n=98/116; 84%) and 0.80 (95% CI: 0.75-0.85) in all participants (n=216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n=72/121; 60%) with a ROC-AUC of 0.83 (95% CI: 0.75-0.91), 94% sensitivity, 63% specificity, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centers, and clinical characteristics.

Conclusion: This multi-center prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic center, and clinical characteristics.