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
(2019) In Gut 68(4). p.672-683- 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2019-04-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- cancer prevention, colorectal cancer, colorectal cancer screening, epidemiology, medical statistics
- in
- Gut
- volume
- 68
- issue
- 4
- pages
- 12 pages
- publisher
- BMJ Publishing Group
- external identifiers
-
- pmid:29615487
- scopus:85054448458
- ISSN
- 0017-5749
- DOI
- 10.1136/gutjnl-2017-315730
- language
- English
- LU publication?
- yes
- id
- 9b52bdcc-ae92-4363-abeb-5fbfa89f8fc6
- date added to LUP
- 2020-09-14 15:53:16
- date last changed
- 2024-03-05 09:46:58
@article{9b52bdcc-ae92-4363-abeb-5fbfa89f8fc6,
abstract = {{<p>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.</p>}},
author = {{Smith, Todd and Muller, David C. and Moons, Karel G.M. and Cross, Amanda J. and Johansson, Mattias and Ferrari, Pietro and Fagherazzi, Guy and Peeters, Petra H.M. and Severi, Gianluca and Hüsing, Anika and Kaaks, Rudolf and Tjonneland, Anne and Olsen, Anja and Overvad, Kim and Bonet, Catalina and Rodriguez-Barranco, Miguel and Huerta, Jose Maria and Barricarte Gurrea, Aurelio and Bradbury, Kathryn E. and Trichopoulou, Antonia and Bamia, Christina and Orfanos, Philippos and Palli, Domenico and Pala, Valeria and Vineis, Paolo and Bueno-De-Mesquita, Bas and Ohlsson, Bodil and Harlid, Sophia and Van Guelpen, Bethany and Skeie, Guri and Weiderpass, Elisabete and Jenab, Mazda and Murphy, Neil and Riboli, Elio and Gunter, Marc J. and Aleksandrova, Krasimira Jekova and Tzoulaki, Ioanna}},
issn = {{0017-5749}},
keywords = {{cancer prevention; colorectal cancer; colorectal cancer screening; epidemiology; medical statistics}},
language = {{eng}},
month = {{04}},
number = {{4}},
pages = {{672--683}},
publisher = {{BMJ Publishing Group}},
series = {{Gut}},
title = {{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}},
url = {{http://dx.doi.org/10.1136/gutjnl-2017-315730}},
doi = {{10.1136/gutjnl-2017-315730}},
volume = {{68}},
year = {{2019}},
}