Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients
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Background: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. Methods: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. Results: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. Conclusion: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.
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Blood remains the most commonly studied biospecimen for early detection, but many analytes are also present in other proximate samples, including pancreatic juice, stool, urine, saliva, and pancreatic cyst fluid.
“In passing, it also needs to be stated that, although blood remains the most commonly studied biospecimen for early detection, many of the analytes are also present in other proximate samples, including pancreatic juice,53,54 stool,55 urine,56,57 saliva,58 and pancreatic cyst fluid.59”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [57]
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Self-citation
- Authors
- Blyuss, O., Zaikin, A., Cherepanova, V., Munblit, D., Kiseleva, E. M., Prytomanova, O. M., Duffy, S. W., & Crnogorac-Jurcevic, T.
- Affiliations
- University of Hertfordshire (GB)
- Sechenov University (RU)
- University College London (GB)
- Oles Honchar Dnipro National University (UA)
- Queen Mary University of London (GB)
- Corresponding author
- Blyuss, Oleg
- ORCID
- Venue
- British Journal of Cancer
- Published
- 2019-12-20
- Volume
- 122
- Issue
- 5
- Pages
- 692-696
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC7054390
- OpenAlex
- W2995984299
- Cited by
- 67
- References
- 36
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 11 · 2023: 9 · 2022: 9 · 2021: 12 · 2020: 5
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Cancer (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Hertfordshire Research Archive (University of Hertfordshire) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion)
- Funders
- Pancreatic Cancer Research Fund
- DH | National Institute for Health Research
- DH | NIHR | Efficacy and Mechanism Evaluation Programme
- Topics
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
- Keywords
- pancreatic ductal adenocarcinoma, risk score, urinary biomarkers, LYVE-1, REG1B, TFF1, CA19-9, risk prediction model, logistic regression, machine learning algorithm comparison, early cancer detection, non-invasive screening, patient stratification
- MeSH terms
- Machine Learning, Trefoil Factor-1, Aged, Algorithms, Creatinine, Humans, Middle Aged, Neoplasm Staging, Pancreatic Neoplasms, Predictive Value of Tests, Risk, Biomarkers, Tumor, Models, Statistical, Logistic Models, Case-Control Studies, Carcinoma, Pancreatic Ductal, Vesicular Transport Proteins, Lithostathine, Early Detection of Cancer
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