Original Article

Familial Cancer

, Volume 12, Issue 2, pp 217-228

First online:

Prediction models in Lynch syndrome

  • Fay KastrinosAffiliated withHerbert Irving Comprehensive Cancer Center, Columbia University Medical CenterDivision of Digestive and Liver Diseases, Columbia University Medical CenterColumbia University College of Physicians and Surgeons
  • , Judith BalmañaAffiliated withDepartment of Medical Oncology, Hospital Vall d’Hebron, Universitat Autònoma de Barcelona
  • , Sapna SyngalAffiliated withPopulation Sciences Division, Dana-Farber Cancer InstituteDivision of Gastroenterology, Brigham and Women’s HospitalHarvard Medical School Email author 

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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 syndrome Prediction models PREMM1,2,6 MMRPro MMRPredict