The Development and Optimisation of a Urinary Volatile Organic Compound Analytical Platform Using Gas Sensor Arrays for the Detection of Colorectal Cancer

Abstract

The profile of Volatile Organic Compounds (VOCs) may help prioritise at-risk groups for early cancer detection. Urine sampling has been shown to provide good disease accuracy whilst being patient acceptable compared to faecal analysis. Thus, in this study, urine samples were examined using an electronic nose with metal oxide gas sensors and a solid-phase microextraction sampling system. A calibration dataset (derived from a previous study) with CRC-positive patients and healthy controls was used to train a radial basis function neural network. However, a blinded analysis failed to detect CRC accurately, necessitating an enhanced data-processing strategy. This new approach categorised samples by significant bowel diseases, including CRC and high-risk polyps. Retraining the neural network showed an area under the ROC curve of 0.88 for distinguishing CRC versus nonsignificant bowel disease (without CRC, polyps or inflammation). These findings suggest that, with appropriate training sets, urine VOC analysis could be a rapid, low-cost method for early detection of precancerous colorectal polyps and CRC.