title: A machine-learned predictor of colonic polyps based on urinary metabolomics.
abstract: 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 (1)H-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.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8.
authors.forename: Roman
authors.initials: R
authors.lastname: Eisner
authors.forename: Russell
authors.initials: R
authors.lastname: Greiner
authors.forename: Victor
authors.initials: V
authors.lastname: Tso
authors.forename: Haili
authors.initials: H
authors.lastname: Wang
authors.forename: Richard N
authors.initials: RN
authors.lastname: Fedorak
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D003110:Colonic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003111:Colonic Polyps / Q000473:pathology / Q000652:urine*; D003113:Colonoscopy; D005260:Female; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D055432:Metabolomics; D008875:Middle Aged; D011379:Prognosis