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dc.contributor.authorBlyuss, O
dc.contributor.authorZaikin, A
dc.contributor.authorCherepanova, V
dc.contributor.authorMunblit, D
dc.contributor.authorKiseleva, EM
dc.contributor.authorPrytomanova, OM
dc.contributor.authorDuffy, SW
dc.contributor.authorCrnogorac-Jurcevic, T
dc.date.accessioned2020-04-17T16:28:28Z
dc.date.available2019-12-04
dc.date.available2020-04-17T16:28:28Z
dc.date.issued2019-12-20
dc.identifier.citationBlyuss, O., Zaikin, A., Cherepanova, V. et al. Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients. Br J Cancer 122, 692–696 (2020). https://doi.org/10.1038/s41416-019-0694-0en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/63621
dc.description.abstractBACKGROUND: 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.en_US
dc.format.extent692 - 696
dc.languageeng
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofBritish Journal of Cancer
dc.rightsCreative Commons Attribution 4.0 International License
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDevelopment of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients.en_US
dc.typeArticleen_US
dc.rights.holder(c) 2019 The Author(s).
dc.identifier.doi10.1038/s41416-019-0694-0
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/31857725en_US
pubs.issue5en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume122en_US
dcterms.dateAccepted2019-12-04
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.project483cf8e1-88a1-4b8b-aecb-8402672d45f8en_US


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Creative Commons Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International License