Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis

Abstract

Cancer is currently one of deadliest and most feared diseases in the developed world, and, particularly, lung cancer (LC) is one of the most common types and has one of the highest death/incidence ratios. An early diagnosis for LC is probably the most accessible possibility to try and save patients and lower this ratio. Recently, research concerning LC-related breath biomarkers has provided optimistic results and has become a real option to try and obtain a fast, reliable, and early LC diagnosis. In this paper, a combination of fieldeffect transistor (FET) sensors and artificial neural networks (ANNs) has been employed to classify and estimate the partial pressures of a series of polar and nonpolar volatile organic compounds (VOCs) present in prepared gaseous mixtures. The objective of these preliminary tests is to give an idea of how well this technology can be used to analyze artificial or real breath samples by quantifying the LC-related VOCs or biomarkers. The results of this step are very promising and indicate that this methodology deserves further research using more complex samples to find the existing limitations of the FET-ANN combination. Recently, a new approach for cancer diagnosis is