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A machine-learned predictor of colonic polyps based on urinary metabolomics

• Roman Eisner
• Russ Greiner, Dept of Computing Science; PI of AICML
• Victor Tso
• Haili Wang
• Richard Fedorak

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.

Citation

BibTeX

@article{Eisner+al:13,
  author = {Roman Eisner and Russ Greiner and Victor Tso and Haili Wang and
    Richard Fedorak},
  title = {A machine-learned predictor of colonic polyps based on urinary
    metabolomics},
  Volume = "2013",
  Number = "303982",
  journal = {BioMed Research International},
  year = 2013,
}

Last Updated: February 10, 2020
Submitted by Sabina P

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