University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne School of Population and Global Health
  • Melbourne School of Population and Global Health - Research Publications
  • View Item
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne School of Population and Global Health
  • Melbourne School of Population and Global Health - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

    Thumbnail
    Download
    Published version (2.127Mb)

    Citations
    Scopus
    Web of Science
    Altmetric
    11
    8
    Author
    Smith, T; Muller, DC; Moons, KGM; Cross, AJ; Johansson, M; Ferrari, P; Fagherazzi, G; Peeters, PHM; Severi, G; Huesing, A; ...
    Date
    2019-04-01
    Source Title
    Gut
    Publisher
    BMJ PUBLISHING GROUP
    University of Melbourne Author/s
    Severi, Gianluca
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Smith, T., Muller, D. C., Moons, K. G. M., Cross, A. J., Johansson, M., Ferrari, P., Fagherazzi, G., Peeters, P. H. M., Severi, G., Huesing, A., Kaaks, R., Tjonneland, A., Olsen, A., Overvad, K., Bonet, C., Rodriguez-Barranco, M., Huerta, J. M., Gurrea, A. B., Bradbury, K. E. ,... Tzoulaki, I. (2019). Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies. GUT, 68 (4), pp.672-683. https://doi.org/10.1136/gutjnl-2017-315730.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/254815
    DOI
    10.1136/gutjnl-2017-315730
    Abstract
    OBJECTIVE: To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts. DESIGN: Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability). RESULTS: The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC. CONCLUSION: Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [53039]
    • Melbourne School of Population and Global Health - Research Publications [5329]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors