Trial reportBioMed research international2013
A machine-learned predictor of colonic polyps based on urinary metabolomics.
Trial report in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- State of omics-based microbial diagnostics of CRC.Gut microbes · 2025Review
- Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.Annals of laboratory medicine · 2025Article
- Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection.NPJ precision oncology · 2024Article
- Artificial intelligence for the prevention and prediction of colorectal neoplasms.Journal of translational medicine · 2023Article
- Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings.Cancer biomarkers : section A of Disease markers · 2023Article
- Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.International journal of molecular sciences · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.Translational andrology and urology · 2021Review
- Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms.Diagnostics (Basel, Switzerland) · 2021Article
- Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry.Scientific reports · 2020Article
- Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.Frontiers in pharmacology · 2020Review
- Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019Article
- Review
- Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.International journal of molecular sciences · 2018Article
- Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.Metabolomics : Official journal of the Metabolomic Society · 2017Article
- Urine metabolomics as a predictor of patient tolerance and response to adjuvant chemotherapy in colorectal cancer.Molecular and clinical oncology · 2017Article
- Development and Validation of a High-Throughput Mass Spectrometry Based Urine Metabolomic Test for the Detection of Colonic Adenomatous Polyps.Metabolites · 2017Article
- Clinical validation of a novel urine-based metabolomic test for the detection of colonic polyps on Chinese population.International journal of colorectal disease · 2017Article
- Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls.Analytical chemistry · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.