Analysis of breath samples for lung cancer survival

Analyses of exhaled air by means of electronic noses offer a large diagnostic potential. Such analyses are non-invasive; samples can also be easily obtained from severely ill patients and repeated within short intervals. Lung cancer is the most deadly malignant tumor worldwide, and monitoring of lung cancer progression is of great importance and may help to decide best therapy. In this report, twenty-two patients with diagnosed lung cancer and ten healthy volunteers were studied using breath samples collected several times at certain intervals and analysed by an electronic nose. The samples were divided into three sub-groups; group d for survivor less than one year, group s for survivor more than a year and group h for the healthy volunteers. Prediction models based on partial least square and artificial neural nets could not classify the collected groups d, s and h, but separated well group d from group h. Using artificial neural net, group d could be separated from group s. Excellent predictions and stable models of survival day for group d were obtained, both based on partial least square and artificial neural nets, with correlation coefficients 0.981 and 0.985, respectively. Finally, the importance of consecutive measurements was shown.