Development and application of a new electronic nose instrument for the detection of colorectal cancer

• Published in: Biosensors & bioelectronics Vol. 67; pp. 733 - 738
• Main Authors: Westenbrink, E, Arasaradnam, R P, O'Connell, N, Bailey, C, Nwokolo, C, Bardhan, K D, Covington, J A
• Format: Journal Article
• Language: English
• Published: England 15.05.2015
• Subjects: Algorithms; Bacteria; Biomarkers, Tumor - urine; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Cancer; Colorectal Neoplasms - pathology; Colorectal Neoplasms - urine; Control equipment; Diseases; Electronic Nose; Electronic noses; Gases - isolation & purification; Gases - urine; Humans; Patients; Searching; Urine; Volatile Organic Compounds - urine; Urine; Volatile organic compounds; Gas sensors; Colorectal cancer; Electronic nose
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2014.10.044

Colorectal cancer is a leading cause of cancer death in the USA and Europe with symptoms that mimick other far more common lower gastrointestinal (GI) disorders. This difficulty in separating colorectal cancer from these other diseases has driven researchers to search for an effective, non-invasive screening technique. Current state-of-the-art method of Faecal Immunochemical Testing achieving sensitivity ~90%, unfortunately the take-up in the western world is low due to the low patient acceptability of stool samples. However, a wide range of cancers have been distinguished from each-other and healthy controls by detecting the gas/volatile content emanating patient biological media. Dysbiosis afforded by certain disease states may be expressed in the volatile content of urine - a reflection of the gut bacteria's metabolic processes. A new electronic nose instrument was developed at the University of Warwick to measure the gas/volatile content of urine headspace, based on an array of 13 commercial electro-chemical and optical sensors. An experimental setup was arranged for a cohort of 92 urine samples from patients of colorectal cancer (CRC), irritable bowel syndrome (IBS) and controls to be run through the machine. Features were extracted from response data and used in Linear Discriminant Analysis (LDA) plots, including a full 3-disease classification and one focussing on distinguishing CRC from IBS. The latter case was tested by the success of re-classification using an (n-1) K-nearest neighbour algorithm, showing 78% sensitivity and 79% specificity to CRC.