Abstract
Introduction: Lung cancer is the leading cause of global cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, has the potential to detect non-small cell lung cancer (NSCLC) early in the course of the disease in a non-invasive way, which may improve outcome. Analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC) have not been performed extensively yet.
Methods: Subjects diagnosed with AC or SCC and healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands) for 5 minutes. The diagnostic accuracy was studied in a prospective multicenter study in 81 patients with confirmed AC and 26 patients with confirmed SCC. In the 2 analysis, respectively, 109 and 91 healthy subjects were included. The results were compared with the accuracy to diagnose NSCLC as 1 group. Limited case sample sizes resulted in different group sizes for healthy subjects. Data compression and artificial neural networks were used for the statistical analysis of VOC data.
Results: AC patients had a mean age of 63.0 years and SCC patients had a mean age of 63.5 years. Table 1 shows the diagnostic performance of the Aeonose™ in terms of sensitivity, specificity, negative predictive value and area under the curve for the different groups.
Conclusion: The data suggest that the Aeonose™ can contribute to the early diagnostic workup of lung cancer. When differentiating between subtypes of NSCLC, the diagnostic performance improves.
Methods: Subjects diagnosed with AC or SCC and healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands) for 5 minutes. The diagnostic accuracy was studied in a prospective multicenter study in 81 patients with confirmed AC and 26 patients with confirmed SCC. In the 2 analysis, respectively, 109 and 91 healthy subjects were included. The results were compared with the accuracy to diagnose NSCLC as 1 group. Limited case sample sizes resulted in different group sizes for healthy subjects. Data compression and artificial neural networks were used for the statistical analysis of VOC data.
Results: AC patients had a mean age of 63.0 years and SCC patients had a mean age of 63.5 years. Table 1 shows the diagnostic performance of the Aeonose™ in terms of sensitivity, specificity, negative predictive value and area under the curve for the different groups.
Conclusion: The data suggest that the Aeonose™ can contribute to the early diagnostic workup of lung cancer. When differentiating between subtypes of NSCLC, the diagnostic performance improves.
| Original language | English |
|---|---|
| Article number | PA1761 |
| Journal | European respiratory journal |
| Volume | 52 |
| DOIs | |
| Publication status | Published - 15 Sept 2018 |
| Externally published | Yes |
| Event | ERS International Congress 2018 - Paris Expo Porte de Versailles, Paris, France Duration: 15 Sept 2018 → 19 Sept 2018 |
