Epidemiological models for breast cancer risk estimation

Abstract

Abstract Breast cancer is the most common malignancy affecting women worldwide. Effective prevention and screening are only possible if there is precise risk prediction for cancer in an individual patient. Mathematical models for estimation of breast cancer risk were developed on the basis of epidemiological studies. It is possible to identify women at high risk for this disease using patient history data and the analysis of various demographic and hereditary factors. The Gail risk model, originally developed in the United States to selectively identify patients for breast cancer chemoprevention studies, remains to be the most widely used and properly validated. The Cuzick-Tyrer model is more advanced and was developed for the International Breast Intervention Study (IBIS-1). It incorporates the assessment of additional hereditary factors, body mass index, menopausal status and hormone replacement therapy use. Genetic models aiming at calculating individual risk for BRCA1 and BRCA2 mutation carrier-state have also been designed. In this review we discuss the usefulness of various risk estimation models and their possible application for breast cancer prophylaxis.