title: Cancer risk models and preselection for screening.
abstract: OBJECTIVE
  The invitation to population screening is based on age criteria in many countries. Screening is not offered to younger or older participants, because the benefits in these age groups do not outweigh the harms. One could argue that it is not so much age that determines the benefits but the risk of developing preclinical and treatable cancer. Cancer risk varies with age but is also affected by other factors.
  
  
  METHODS
  We performed a systematic review for risk models for the three types of cancer for which population screening programs exist: breast, cervical and colon cancer. We used an evaluation scheme that distinguishes three phases of model development: model derivation, validation and impact analysis. Data were collected in August 2010.
  
  
  RESULTS
  We identified two colorectal, four breast and three cervix cancer risk models. One colorectal, four breast and none of the cervix cancer models have been externally validated. We could not identify evaluations of the impact on population screening effectiveness.
  
  
  CONCLUSIONS
  We conclude that risk models for the pre-selection of screening have been developed. These models could improve the pre-selection for screening, help in making personal decisions about participation, and reduce adverse effects of population screening. The validity of this hypothesis, as well as practicalities and issues of equity and reliability, have to be tested in further studies.
authors: Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, Netherlands. i.stegeman@amc.nl
authors: Inge
authors: I
authors: Stegeman
authors: Patrick M
authors: PM
authors: Bossuyt
mesh_terms: D001943:Breast Neoplasms / Q000517:prevention & control; D002278:Carcinoma in Situ / Q000517:prevention & control; D015179:Colorectal Neoplasms / Q000517:prevention & control; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000458:organization & administration*; D015233:Models, Statistical; D009369:Neoplasms / Q000517:prevention & control*; D018579:Patient Selection; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D002583:Uterine Cervical Neoplasms / Q000517:prevention & control