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000166661 1001_ $$aAleksandrova, Krasimira$$b0
000166661 245__ $$aDevelopment and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score.
000166661 260__ $$aHeidelberg [u.a.]$$bSpringer$$c2021
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000166661 520__ $$aNutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population.The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992-2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed.The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell's C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264-0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084-0.575)).LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.
000166661 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
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000166661 650_7 $$2Other$$aCancer prevention
000166661 650_7 $$2Other$$aColorectal cancer
000166661 650_7 $$2Other$$aLifestyle behaviour
000166661 650_7 $$2Other$$aRisk prediction
000166661 650_7 $$2Other$$aRisk screening
000166661 7001_ $$aReichmann, Robin$$b1
000166661 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b2$$udkfz
000166661 7001_ $$aJenab, Mazda$$b3
000166661 7001_ $$aBueno-de-Mesquita, H Bas$$b4
000166661 7001_ $$aDahm, Christina C$$b5
000166661 7001_ $$aEriksen, Anne Kirstine$$b6
000166661 7001_ $$aTjønneland, Anne$$b7
000166661 7001_ $$aArtaud, Fanny$$b8
000166661 7001_ $$aBoutron-Ruault, Marie-Christine$$b9
000166661 7001_ $$aSeveri, Gianluca$$b10
000166661 7001_ $$0P:(DE-He78)6519c85d61a3def7974665471b8a4f74$$aHüsing, Anika$$b11
000166661 7001_ $$aTrichopoulou, Antonia$$b12
000166661 7001_ $$aKarakatsani, Anna$$b13
000166661 7001_ $$aPeppa, Eleni$$b14
000166661 7001_ $$aPanico, Salvatore$$b15
000166661 7001_ $$aMasala, Giovanna$$b16
000166661 7001_ $$aGrioni, Sara$$b17
000166661 7001_ $$aSacerdote, Carlotta$$b18
000166661 7001_ $$aTumino, Rosario$$b19
000166661 7001_ $$aElias, Sjoerd G$$b20
000166661 7001_ $$aMay, Anne M$$b21
000166661 7001_ $$aBorch, Kristin B$$b22
000166661 7001_ $$aSandanger, Torkjel M$$b23
000166661 7001_ $$aSkeie, Guri$$b24
000166661 7001_ $$aSánchez, Maria-Jose$$b25
000166661 7001_ $$aHuerta, José María$$b26
000166661 7001_ $$aSala, Núria$$b27
000166661 7001_ $$aGurrea, Aurelio Barricarte$$b28
000166661 7001_ $$aQuirós, José Ramón$$b29
000166661 7001_ $$aAmiano, Pilar$$b30
000166661 7001_ $$aBerntsson, Jonna$$b31
000166661 7001_ $$aDrake, Isabel$$b32
000166661 7001_ $$avan Guelpen, Bethany$$b33
000166661 7001_ $$aHarlid, Sophia$$b34
000166661 7001_ $$aKey, Tim$$b35
000166661 7001_ $$aWeiderpass, Elisabete$$b36
000166661 7001_ $$aAglago, Elom K$$b37
000166661 7001_ $$aCross, Amanda J$$b38
000166661 7001_ $$aTsilidis, Konstantinos K$$b39
000166661 7001_ $$aRiboli, Elio$$b40
000166661 7001_ $$aGunter, Marc J$$b41
000166661 773__ $$0PERI:(DE-600)2131669-7$$a10.1186/s12916-020-01826-0$$gVol. 19, no. 1, p. 1$$n1$$p1$$tBMC medicine$$v19$$x1741-7015$$y2021
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