Developing a risk prediction model for breast cancer: a Statistical Utility to Determine Affinity of Neoplasm (SUDAN-CA Breast)

BACKGROUND: Breast cancer risk prediction models are widely used in clinical settings. Although most of the well-known models were designed based on data collected from western population, yet they have been utilized for surveillance purposes in many limited-resource countries. Given the genetic variations in risk factors that exist between different races, we therefore aimed to develop and validate a tool for breast cancer risk assessment among Sudanese women. METHODS: Using cross-sectional design, 153 subjects were eligible to participate in our study. Data were collected from the only couple of tertiary centers in Sudan. They underwent multiple logistic regression using purposeful selection method to build the model. Various adjustments were made to determine significant predictors. Overall performance, calibration and discrimination were assessed by R (2), O/E ratio and c-statistic, respectively. RESULTS: SUDAN predictors of breast cancer were: age, menarche, family history, vegetables and fruits weekly servings, and type of cereals that traditional cuisine is made of. Both Nagelkerke R (2) (0.495) and O/E ratio (0.78) were good. c-statistic expressed the excellent discriminatory power of the model (0.864, p < 0.001, 95% CI 0.81–0.92). CONCLUSIONS: Our findings suggest that SUDAN provides a simple, efficient and well-calibrated tool to predict and classify women’s lifetime risks of developing breast cancer. Input from our model could be deployed to guide utilization of the more advanced screening modalities in resource-limited settings to maximize cost effectiveness. Consequently, this might improve the stage at which the diagnosis is usually made.