title: Personalizing colorectal cancer screening: a systematic review of models to predict risk of colorectal neoplasia.
abstract: OBJECTIVE
  A valid risk prediction model for colorectal neoplasia would allow patients to be screened for colorectal cancer (CRC) on the basis of risk. We performed a systematic review of studies reporting risk prediction models for colorectal neoplasia.
  
  
  METHODS
  We conducted a systematic search of MEDLINE, Scopus, and Cochrane Library databases from January 1990 through March 2013 and of references in identified studies. Case-control, cohort, and cross-sectional studies that developed or attempted to validate a model to predict risk of colorectal neoplasia were included. Two reviewers independently extracted data and assessed model quality. Model quality was considered to be good for studies that included external validation, fair for studies that included internal validation, and poor for studies with neither.
  
  
  RESULTS
  Nine studies developed a new prediction model, and 2 tested existing models. The models varied with regard to population, predictors, risk tiers, outcomes (CRC or advanced neoplasia), and range of predicted risk. Several included age, sex, smoking, a measure of obesity, and/or family history of CRC among the predictors. Quality was good for 6 models, fair for 2 models, and poor for 1 model. The tier with the largest population fraction (low, intermediate, or high risk) depended on the model. For most models that defined risk tiers, the risk difference between the highest and lowest tier ranged from 2-fold to 4-fold. Two models reached the 0.70 threshold for the C statistic, typically considered to indicate good discriminatory power.
  
  
  CONCLUSIONS
  Most current colorectal neoplasia risk prediction models have relatively weak discriminatory power and have not demonstrated generalizability. It remains to be determined how risk prediction models could inform CRC screening strategies.
authors.affiliation: Department of Medicine, Stanford University School of Medicine, Stanford, California.
authors.forename: Gene K
authors.initials: GK
authors.lastname: Ma
authors.affiliation: Department of Medicine, Stanford University School of Medicine, Stanford, California; Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California. Electronic address: uri.ladabaum@stanford.edu.
authors.forename: Uri
authors.initials: U
authors.lastname: Ladabaum
mesh_terms: D015179:Colorectal Neoplasms / Q000175:diagnosis*; D055088:Early Detection of Cancer / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D057285:Precision Medicine / Q000379:methods*; D018570:Risk Assessment / Q000379:methods
keywords: Colon Cancer Screening; Early Detection; Risk Stratification; Systematic Review