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dc.contributor.authorElvatun, Severin
dc.contributor.authorKnoors, Daan
dc.contributor.authorNygård, Mari Kiens
dc.contributor.authorUusküla, Anneli
dc.contributor.authorVõrk, Andres
dc.contributor.authorNygård, Jan Franz
dc.date.accessioned2024-04-12T06:10:18Z
dc.date.available2024-04-12T06:10:18Z
dc.date.issued2023-11-24
dc.description.abstractBackground - Cervical cancer is a preventable disease, despite being one of the most common types of female cancers worldwide. Integrating existing programs for cervical cancer screening with personalized risk prediction algorithms can improve population-level cancer prevention by enabling more targeted screening and contrive preventive healthcare innovations. While algorithms developed for cervical cancer risk prediction have shown promising performance in internal validation on more homogeneous populations, their ability to generalize to external populations remains to be assessed.<p> <p>Methods - To address this gap, we perform a cross-population comparative study of personalized prediction algorithms for more personalized cervical cancer screening. Using data from the Norwegian and Estonian populations, the algorithms are validated on internal and external datasets to study their potential biases and limitations when applied to different populations. We evaluate the algorithms in predicting progression from low-grade precancerous cervical lesions, simulating a clinically relevant application of more personalized risk stratification.<p> <p>Results - As expected, our numerical experiments show that algorithm performance varies depending on the population. However, some algorithms show strong generalization capacity across different data sources. Using Kaplan-Meier estimates, we demonstrate the strengths and limitations of the algorithms in detecting cancer progression over time by comparing to the trends observed from data. We assess their overall discrimination performance in personalized risk predictions by analyzing the accuracy and confidence in individual risk estimates.<p> <p>Discussion and Conclusion - This study examines the effectiveness of personalized prediction algorithms across different populations. Our results demonstrate the potential for generalizing risk prediction algorithms to external populations. These findings highlight the importance of considering population diversity when developing risk prediction algorithms.en_US
dc.identifier.citationElvatun, Knoors, Nygård, Uusküla, Võrk, Nygård. Cross-population evaluation of cervical cancer risk prediction algorithms. International Journal of Medical Informatics. 2023;181en_US
dc.identifier.cristinIDFRIDAID 2235373
dc.identifier.doi10.1016/j.ijmedinf.2023.105297
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.urihttps://hdl.handle.net/10037/33388
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalInternational Journal of Medical Informatics
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleCross-population evaluation of cervical cancer risk prediction algorithmsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)