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BioMed Research International
Volume 2013, Article ID 303982, 11 pages
Research Article

A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics

1Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
2Division of Gastroenterology, Zeidler Ledcor Centre, University of Alberta, Edmonton, AB, Canada T6G 2X8
3Department of Surgery, 2D2.29 WC Mackenzie Health Science Centre, University of Alberta, Edmonton, AB, Canada T6G 2R7

Received 10 July 2013; Revised 29 August 2013; Accepted 8 September 2013

Academic Editor: Yudong Cai

Copyright © 2013 Roman Eisner et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via 1H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two.