Detection of lung cancer with electronic nose and logistic regression analysis

Abstract

Lung cancer is a very common malignancy with a low five-year survival rate. Artificial olfactory sensor (electronic nose) is a tool that recently has been studied as a probable optimal screening tool for early detection of lung cancer, but still no statistical method has been put forward as the preferable one. The aim of the study was to explore the use of logistic regression analysis (LRA) to analyse patients' exhaled breath samples with electronic nose in order to differentiate lung cancer patients (regardless of the stage of the cancer) from patients with other lung diseases and healthy individuals. Patients with histologically or cytologically verified, untreated lung cancer, patients with other lung diseases such as benign lung tumors, chronic obstructive pulmonary disease, asthma, pneumonia, etc, and healthy volunteers were enrolled in the study, in total 252 cancer patients and 223 patients without cancer. Breath sample collection and analysis were performed with Cyranose 320 sensor device and data further analysed using LRA. The LRA correctly differentiated lung cancer patients from no-cancer patients. The overall sensitivity in detecting patients having cancer was 95.8% for smokers and 96.2% for non-smokers and the overall specificity was 90.6% for non-smokers and 92.3% for smokers. Exhaled breath analysis by electronic nose using LRA is able to discriminate lung cancer patients from patients with other lung diseases and from healthy individuals.