University of Hertfordshire

From the same journal

By the same authors


  • Oleg Blyuss
  • Alexey Zaikin
  • Valeriia Cherepanova
  • Daniel Munblit
  • Elena M Kiseleva
  • Olga M Prytomanova
  • Stephen W Duffy
  • Tatjana Crnogorac-Jurcevic
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Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalBritish Journal of Cancer
Publication statusPublished - 20 Dec 2019


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.


© The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.

ID: 18773486