S Kort1, M M Tiggeloven2, M Brusse-Keizer3, J W Gerritsen4, J H Schouwink2, E Citgez2, F H C de Jongh2, S Samii5, J van der Maten6, M van den Bogart7, J van der Palen8. 1. Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands. Electronic address: s.kort@mst.nl. 2. Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands. 3. Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands. 4. The eNose Company, Zutphen, the Netherlands. 5. Department of Pulmonary Medicine, Deventer Ziekenhuis, Deventer, the Netherlands. 6. Department of Pulmonary Medicine, Medisch Centrum Leeuwarden, Leeuwarden, the Netherlands. 7. Department of Pulmonary Medicine, Bernhoven Uden, Uden, the Netherlands. 8. Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, the Netherlands.
Abstract
OBJECTIVES: Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC's) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions. MATERIALS AND METHODS: In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed. RESULTS: Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86. CONCLUSION: Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing.
OBJECTIVES:Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC's) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions. MATERIALS AND METHODS: In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed. RESULTS: Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86. CONCLUSION: Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing.
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