title: Risk Prediction Models for Colorectal Cancer: A Systematic Review.
abstract: Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.
authors.affiliation: The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. jau20@medschl.cam.ac.uk.
authors.forename: Juliet A
authors.initials: JA
authors.lastname: Usher-Smith
authors.affiliation: The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. Department of General Practice, Melbourne Medical School Faculty of Medicine, Dentistry & Health Sciences The University of Melbourne, Carlton, Victoria, Australia.
authors.forename: Fiona M
authors.initials: FM
authors.lastname: Walter
authors.affiliation: The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. Department of General Practice, Melbourne Medical School Faculty of Medicine, Dentistry & Health Sciences The University of Melbourne, Carlton, Victoria, Australia.
authors.forename: Jon D
authors.initials: JD
authors.lastname: Emery
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, Level 4, The University of Melbourne, Victoria, Australia.
authors.forename: Aung K
authors.initials: AK
authors.lastname: Win
authors.affiliation: The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Griffin
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000378:metabolism; D015992:Body Mass Index; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000453:epidemiology*; D055088:Early Detection of Cancer / Q000379:methods*; D006801:Humans; D015994:Incidence; D008403:Mass Screening; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor