Assessment of the Gail model in estimating the risk of breast cancer: Effect of cancer worry and risk in healthy women

Abstract

Background: There has been substantial interest in developing methods to predict the risk of breast cancer. The Gail model is one the first model have been widely used to identify women at higher risk of breast cancer. Aim: This study aimed to determine the 5-year and the general life-time risk of breast cancer and also to determine breast cancer predictors in women using the Gail model. Methods: We used the Gail model to estimate the risk of breast cancer in female Turkish outpatients aged above 35 years in this cross-sectional study. Age, life-style habits, breast-feeding duration, family history of breast cancer, and body mass index were compared between high and low-risk subjects. We have performed the Patient Health Questionnaire 9-item (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) tools on patients regarding depression and anxiety. We also assessed the association of these covariates with the estimated risk of breast cancer in multivariate linear regression analysis. Results: We enrolled 1065 subjects with a mean age of 52.9 ± 8.4 years. The mean of the five-year risk for breast cancer was 1.33%±0.6. Meanwhile, the mean of lifetime risks for breast cancer was 10.15%±3.18, respectively. Nearly one-third of the participants had one child, 55.9% had breast-fed their children more than six months. Meanwhile, 18.5% of the subjects had a high depression score, 15.2% had a high anxiety score. Higher age, age at first birth, and parity; lower age at menarche; presence of menopause and family history of breast cancer were higher in the high-risk group. Higher age, and age at first birth; lower age at menarche; family history of breast cancer, presence of menopause, and parity were independently associated with higher breast cancer risk. Conclusion: We identified certain risk factors for breast cancer in our study population and Gail model is a reliable and useful breast cancer risk prediction model for clinical decision-making. This study contributes to the body of evidence in order to facilitate early detection and better plan for possible malignancies in Turkish population.