ArticleBMC medicine2021
Development and validation of a lifestyle-based model for colorectal cancer risk prediction: the LiFeCRC score.
Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 120 citations in OpenAlex.
- Derivation and Validation of a Predictive Model for Advanced Colorectal Neoplasia Among Average-Risk Adults in China.International journal of cancer · 2026Article
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- The impact of dietary fiber on colorectal cancer patients based on machine learning.Frontiers in nutrition · 2025Article
- Development and Validation of a Colorectal Cancer Prediction Model: A Nationwide Cohort-Based Study.Digestive diseases and sciences · 2024Article
- External validation of the colorectal cancer risk score LiFeCRC using food frequency questions in the HUNT study.International journal of colorectal disease · 2024Article
- Lifestyle predictors of colorectal cancer in European populations: a systematic review.BMJ nutrition, prevention & health · 2024Article
- Lifestyle, clinical and histological indices-based prediction models for survival in cancer patients: a city-wide prospective cohort study in China.Journal of cancer research and clinical oncology · 2023Article
- Garlic consumption in relation to colorectal cancer risk and to alterations of blood bacterial DNA.European journal of nutrition · 2023Article
- Development and validation of a blood biomarker score for predicting mortality risk in the general population.Journal of translational medicine · 2023Article
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- Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration.BMJ (Clinical research ed.) · 2023Article
- A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case-Control Studies.Metabolites · 2023Article
- Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features.International journal of environmental research and public health · 2022Article
- External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank.BMC medicine · 2022Article
- Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma.Journal of personalized medicine · 2021Article
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Authors and funding
42 authors at 20 institutions in 10 countries.
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Abstract
backgroundNutrition 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.
methodsThe 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.
resultsThe 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)).
conclusionsLiFeCRC 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.
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