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| Home > Publications database > Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score. > print |
| 001 | 166661 | ||
| 005 | 20240229133526.0 | ||
| 024 | 7 | _ | |a 10.1186/s12916-020-01826-0 |2 doi |
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| 037 | _ | _ | |a DKFZ-2021-00028 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Aleksandrova, Krasimira |b 0 |
| 245 | _ | _ | |a Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score. |
| 260 | _ | _ | |a Heidelberg [u.a.] |c 2021 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1610448259_25477 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Nutrition 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. |
| 536 | _ | _ | |a 313 - Krebsrisikofaktoren und Prävention (POF4-313) |0 G:(DE-HGF)POF4-313 |c POF4-313 |x 0 |f POF IV |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 650 | _ | 7 | |a Cancer prevention |2 Other |
| 650 | _ | 7 | |a Colorectal cancer |2 Other |
| 650 | _ | 7 | |a Lifestyle behaviour |2 Other |
| 650 | _ | 7 | |a Risk prediction |2 Other |
| 650 | _ | 7 | |a Risk screening |2 Other |
| 700 | 1 | _ | |a Reichmann, Robin |b 1 |
| 700 | 1 | _ | |a Kaaks, Rudolf |0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |b 2 |u dkfz |
| 700 | 1 | _ | |a Jenab, Mazda |b 3 |
| 700 | 1 | _ | |a Bueno-de-Mesquita, H Bas |b 4 |
| 700 | 1 | _ | |a Dahm, Christina C |b 5 |
| 700 | 1 | _ | |a Eriksen, Anne Kirstine |b 6 |
| 700 | 1 | _ | |a Tjønneland, Anne |b 7 |
| 700 | 1 | _ | |a Artaud, Fanny |b 8 |
| 700 | 1 | _ | |a Boutron-Ruault, Marie-Christine |b 9 |
| 700 | 1 | _ | |a Severi, Gianluca |b 10 |
| 700 | 1 | _ | |a Hüsing, Anika |0 P:(DE-He78)6519c85d61a3def7974665471b8a4f74 |b 11 |
| 700 | 1 | _ | |a Trichopoulou, Antonia |b 12 |
| 700 | 1 | _ | |a Karakatsani, Anna |b 13 |
| 700 | 1 | _ | |a Peppa, Eleni |b 14 |
| 700 | 1 | _ | |a Panico, Salvatore |b 15 |
| 700 | 1 | _ | |a Masala, Giovanna |b 16 |
| 700 | 1 | _ | |a Grioni, Sara |b 17 |
| 700 | 1 | _ | |a Sacerdote, Carlotta |b 18 |
| 700 | 1 | _ | |a Tumino, Rosario |b 19 |
| 700 | 1 | _ | |a Elias, Sjoerd G |b 20 |
| 700 | 1 | _ | |a May, Anne M |b 21 |
| 700 | 1 | _ | |a Borch, Kristin B |b 22 |
| 700 | 1 | _ | |a Sandanger, Torkjel M |b 23 |
| 700 | 1 | _ | |a Skeie, Guri |b 24 |
| 700 | 1 | _ | |a Sánchez, Maria-Jose |b 25 |
| 700 | 1 | _ | |a Huerta, José María |b 26 |
| 700 | 1 | _ | |a Sala, Núria |b 27 |
| 700 | 1 | _ | |a Gurrea, Aurelio Barricarte |b 28 |
| 700 | 1 | _ | |a Quirós, José Ramón |b 29 |
| 700 | 1 | _ | |a Amiano, Pilar |b 30 |
| 700 | 1 | _ | |a Berntsson, Jonna |b 31 |
| 700 | 1 | _ | |a Drake, Isabel |b 32 |
| 700 | 1 | _ | |a van Guelpen, Bethany |b 33 |
| 700 | 1 | _ | |a Harlid, Sophia |b 34 |
| 700 | 1 | _ | |a Key, Tim |b 35 |
| 700 | 1 | _ | |a Weiderpass, Elisabete |b 36 |
| 700 | 1 | _ | |a Aglago, Elom K |b 37 |
| 700 | 1 | _ | |a Cross, Amanda J |b 38 |
| 700 | 1 | _ | |a Tsilidis, Konstantinos K |b 39 |
| 700 | 1 | _ | |a Riboli, Elio |b 40 |
| 700 | 1 | _ | |a Gunter, Marc J |b 41 |
| 773 | _ | _ | |a 10.1186/s12916-020-01826-0 |g Vol. 19, no. 1, p. 1 |0 PERI:(DE-600)2131669-7 |n 1 |p 1 |t BMC medicine |v 19 |y 2021 |x 1741-7015 |
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