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

Summary

In 364 adults with suspected lung cancer, the original eNose model achieved an AUC of 0.92 (95% CI 0.85–0.99) in COPD patients and 0.80 (0.75–0.85) overall. At 95% sensitivity, specificity/PPV/NPV were 72%/95%/72% in COPD and 51%/74%/88% overall. A newly trained model yielded AUC 0.83, sensitivity 94%, specificity 63%, PPV 79%, NPV 89% in validation, performing consistently across tumor characteristics and stages.

Key Findings

• Original eNose model AUC: 0.92 in COPD and 0.80 overall.
• At 95% sensitivity, specificity/PPV/NPV were 72%/95%/72% (COPD) and 51%/74%/88% (overall).
• New model validation: AUC 0.83, sensitivity 94%, specificity 63%, PPV 79%, NPV 89%.
• Performance was consistent across tumor characteristics, disease stage, centers, and clinical features.

Clinical Implications

eNose could be adopted as a front-line triage tool in thoracic oncology clinics to prioritize diagnostic pathways, potentially reducing unnecessary biopsies and accelerating diagnosis, especially in COPD patients.

Limitations

• Study conducted in specialized thoracic oncology clinics in two centers, potentially limiting generalizability
• Not yet tested as a population screening tool or in primary care pathways

Future Directions

Prospective impact studies in broader care pathways (primary care to oncology), cost-effectiveness analyses, device standardization, and integration with imaging/risk models.