TY - JOUR
T1 - Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals
T2 - A systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies
AU - Smith, Todd
AU - Muller, David C.
AU - Moons, Karel G.M.
AU - Cross, Amanda J.
AU - Johansson, Mattias
AU - Ferrari, Pietro
AU - Fagherazzi, Guy
AU - Peeters, Petra H.M.
AU - Severi, Gianluca
AU - Hüsing, Anika
AU - Kaaks, Rudolf
AU - Tjonneland, Anne
AU - Olsen, Anja
AU - Overvad, Kim
AU - Bonet, Catalina
AU - Rodriguez-Barranco, Miguel
AU - Huerta, Jose Maria
AU - Barricarte Gurrea, Aurelio
AU - Bradbury, Kathryn E.
AU - Trichopoulou, Antonia
AU - Bamia, Christina
AU - Orfanos, Philippos
AU - Palli, Domenico
AU - Pala, Valeria
AU - Vineis, Paolo
AU - Bueno-De-Mesquita, Bas
AU - Ohlsson, Bodil
AU - Harlid, Sophia
AU - Van Guelpen, Bethany
AU - Skeie, Guri
AU - Weiderpass, Elisabete
AU - Jenab, Mazda
AU - Murphy, Neil
AU - Riboli, Elio
AU - Gunter, Marc J.
AU - Aleksandrova, Krasimira Jekova
AU - Tzoulaki, Ioanna
N1 - Publisher Copyright:
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2019.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - cancer prevention
KW - colorectal cancer
KW - colorectal cancer screening
KW - epidemiology
KW - medical statistics
UR - http://www.scopus.com/inward/record.url?scp=85054448458&partnerID=8YFLogxK
U2 - 10.1136/gutjnl-2017-315730
DO - 10.1136/gutjnl-2017-315730
M3 - Article
C2 - 29615487
AN - SCOPUS:85054448458
SN - 0017-5749
VL - 68
SP - 672
EP - 683
JO - Gut
JF - Gut
IS - 4
ER -