Familial Cancer

, Volume 12, Issue 2, pp 217–228

Prediction models in Lynch syndrome

Authors

  • Fay Kastrinos
    • Herbert Irving Comprehensive Cancer CenterColumbia University Medical Center
    • Division of Digestive and Liver DiseasesColumbia University Medical Center
    • Columbia University College of Physicians and Surgeons
  • Judith Balmaña
    • Department of Medical Oncology, Hospital Vall d’HebronUniversitat Autònoma de Barcelona
    • Population Sciences DivisionDana-Farber Cancer Institute
    • Division of GastroenterologyBrigham and Women’s Hospital
    • Harvard Medical School
Original Article

DOI: 10.1007/s10689-013-9632-0

Cite this article as:
Kastrinos, F., Balmaña, J. & Syngal, S. Familial Cancer (2013) 12: 217. doi:10.1007/s10689-013-9632-0

Abstract

Prediction models for the identification of Lynch syndrome have been developed to quantify an individual’s risk of carrying a mismatch repair gene mutation and help clinicians decide for whom further risk assessment and genetic testing is necessary. There are diverse clinical settings in which a healthcare provider has the opportunity to assess an individual for Lynch syndrome. Prediction models offer a potentially feasible and useful strategy to systematically identify at-risk individuals, whether they are affected with colorectal cancer or not, and to help with management of the implications of molecular and germline test results. Given the complexity of diagnostic information currently available to clinicians involved in identifying and caring for patients with Lynch syndrome, prediction models provide a useful and complementary aid in medical decision-making. Systematic implementation of prediction models estimates should be considered in routine clinical care and at various stages of cancer risk assessment and prevention. In this manuscript, we review the main prediction models developed for Lynch syndrome, focus on their specific features and performance assessed in several validation studies, compare the models with other clinical and molecular strategies for the diagnosis of Lynch syndrome, and discuss their potential uses in clinical practice.

Keywords

Lynch syndromePrediction modelsPREMM1,2,6MMRProMMRPredict

Copyright information

© Springer Science+Business Media Dordrecht 2013