Detection of lung cancer in exhaled breath with electronic nose technology

Lung cancer is the leading cause of cancer-related mortality worldwide. The 5-year survival rate for localized stage non-small cell lung cancer (NSCLC) approximates 60%, whilst the 5-year survival rate for metastatic disease equals 5%. Despite substantial progress in the treatment options, such as targeted therapies with tyrosine kinase inhibitors (TKI’s), immune therapy, improvements in surgical options, and personalized treatment, this high lung cancer related mortality reflects the fact that the majority of the patients present with advanced-stage disease, which is not curable.

In the past decades, various non-invasive technologies have been investigated as a potential tool to diagnose lung cancer. One of these technologies concerns exhaled breath analysis based on pattern recognition by electronic nose technology. Exhaled breath contains, besides inorganic compounds, also thousands of volatile organic compounds (VOCs) reflecting physiological and pathophysiological metabolic processes in the body. In case of a disease, metabolism alters leading to exhalation of a different composition of VOCs which can be captured by highly sensitive sensors and measured with artificial intelligence techniques. This type of technology mimics human olfaction in which one needs to be trained to recognize familiar smells and allows the electronic nose to recognize a ‘smell’ that matches lung cancer, or any other condition for which the electronic nose has been trained. In this thesis, we investigated the potential of exhaled breath analysis to diagnose lung cancer by performing methodological and clinical studies in which we trained and validated an electronic nose (Aeonose™) to distinguish patients with lung cancer from subjects without lung cancer.