RT Journal Article
T1 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.
A1 Smith, Todd
A1 Muller, David C
A1 Moons, Karel G M
A1 Cross, Amanda J
A1 Johansson, Mattias
A1 Ferrari, Pietro
A1 Fagherazzi, Guy
A1 Peeters, Petra H M
A1 Severi, Gianluca
A1 Hüsing, Anika
A1 Kaaks, Rudolf
A1 Tjonneland, Anne
A1 Olsen, Anja
A1 Overvad, Kim
A1 Bonet, Catalina
A1 Rodriguez-Barranco, Miguel
A1 Huerta, Jose Maria
A1 Barricarte Gurrea, Aurelio
A1 Bradbury, Kathryn E
A1 Trichopoulou, Antonia
A1 Bamia, Christina
A1 Orfanos, Philippos
A1 Palli, Domenico
A1 Pala, Valeria
A1 Vineis, Paolo
A1 Bueno-de-Mesquita, Bas
A1 Ohlsson, Bodil
A1 Harlid, Sophia
A1 Van Guelpen, Bethany
A1 Skeie, Guri
A1 Weiderpass, Elisabete
A1 Jenab, Mazda
A1 Murphy, Neil
A1 Riboli, Elio
A1 Gunter, Marc J
A1 Aleksandrova, Krasimira Jekova
A1 Tzoulaki, Ioanna
K1 cancer prevention
K1 colorectal cancer
K1 colorectal cancer screening
K1 epidemiology
K1 medical statistics
AB To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts. 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). 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. 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.
YR 2018
FD 2018-04-03
LK https://hdl.handle.net/10668/26700
UL https://hdl.handle.net/10668/26700
LA en
DS RISalud
RD Apr 4, 2025