Training and Validating a Portable Electronic Nose for Lung Cancer Screening
| Autor: | Michel R. A. van Hooren, Bernd Kremer, Rens M. G. E. van de Goor, Kenneth W. Kross, Anne-Marie C. Dingemans |
|---|---|
| Přispěvatelé: | RS: GROW - R2 - Basic and Translational Cancer Biology, KNO, Promovendi ODB, MUMC+: MA AIOS Keel Neus Oorheelkunde (9), RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), MUMC+: Oncologie Centrum (3), MUMC+: MA Keel Neus Oorheelkunde (3), MUMC+: MA Keel Neus Oorheelkunde (9) |
| Rok vydání: | 2017 |
| Předmět: |
Pulmonary and Respiratory Medicine
Electronic nose technology Male medicine.medical_specialty Lung Neoplasms Artificial neural network model Diagnostic accuracy 03 medical and health sciences 0302 clinical medicine Internal medicine Diagnosis medicine Humans TECHNOLOGY Volatile organic compounds Lung cancer Electronic Nose Aged Electronic nose business.industry Area under the curve Middle Aged medicine.disease 030228 respiratory system Oncology 030220 oncology & carcinogenesis Screening ARRAY Female BREATH BIOMARKERS business Lung cancer screening |
| Zdroj: | Journal of Thoracic Oncology, 13(5), 676-681. Elsevier Science |
| ISSN: | 1556-1380 1556-0864 |
| Popis: | Introduction: Profiling volatile organic compounds in exhaled breath enables the diagnosis of several types of cancer. In this study we investigated whether a portable point-of-care version of an electronic nose (e-nose) (Aeonose, [eNose Company, Zutphen, the Netherlands]) is able to discriminate between patients with lung cancer and healthy controls on the basis of their volatile organic compound pattern. Methods: In this study, we used five e-nose devices to collect breath samples from patients with lung cancer and healthy controls. A total of 60 patients with lung cancer and 107 controls exhaled through an e-nose for 5 minutes. Patients were assigned either to a training group for building an artificial neural network model or to a blinded control group for validating this model. Results: For differentiating patients with lung cancer from healthy controls, the results showed a diagnostic accuracy of 83% with a sensitivity of 83%, specificity of 84%, and area under the curve of 0.84. Results for the blinded group showed comparable results, with a sensitivity of 88%, specificity of 86%, and diagnostic accuracy of 86%. Conclusion: This feasibility study showed that this portable e-nose can properly differentiate between patients with lung cancer and healthy controls. This result could have important implications for future lung cancer screening. Further studies with larger cohorts, including also more participants with early-stage tumors, should be performed to increase the robustness of this noninvasive diagnostic tool and to determine its added value in the diagnostic chain for lung cancer. (C) 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. |
| Databáze: | OpenAIRE |
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