TY - JOUR
T1 - Lung cancer detection by electronic nose analysis of exhaled breath
T2 - a multicentre prospective external validation study
AU - Buma, A I G
AU - Muntinghe-Wagenaar, M Benthe
AU - van der Noort, V
AU - de Vries, R
AU - Schuurbiers, M M F
AU - Sterk, P J
AU - Schipper, S
AU - Meurs, J
AU - Cristescu, S M
AU - Hiltermann, T J N
AU - van den Heuvel, M M
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/7
Y1 - 2025/7
N2 - 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 chronic obstructive pulmonary disease (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 multicentre 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 models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used. Results: Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (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 an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics. Conclusion: This multicentre 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 centre, and clinical characteristics.
AB - 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 chronic obstructive pulmonary disease (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 multicentre 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 models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used. Results: Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (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 an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics. Conclusion: This multicentre 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 centre, and clinical characteristics.
KW - diagnostic biomarker
KW - electronic nose
KW - exhaled breath analysis
KW - lung cancer
UR - http://www.scopus.com/inward/record.url?scp=105003976423&partnerID=8YFLogxK
U2 - 10.1016/j.annonc.2025.03.013
DO - 10.1016/j.annonc.2025.03.013
M3 - Article
C2 - 40174676
SN - 0923-7534
VL - 36
SP - 786
EP - 795
JO - Annals of oncology : official journal of the European Society for Medical Oncology
JF - Annals of oncology : official journal of the European Society for Medical Oncology
IS - 7
ER -