Validating the IBIS and BOADICEA Models for Predicting Breast Cancer Risk in the Iranian Population

Abstract

BackgroundSeveral approaches have been suggested for incorporating risk factors to predict the future risk of breast cancer. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and International Breast Cancer Intervention Study (IBIS) are among these approaches. We compared the performance of these models in discriminating between cases and noncases in the Iranian population. Patients and MethodsWe performed a case-control study in Tehran, from November 2015 to April 2016, and enrolled 1633 women aged 24 to 75 years, including 506 cases of breast cancer, 916 population-based controls, and 211 clinic-based controls. We calculated and compared the risk of breast cancer predicted by the IBIS and BOADICEA models and the logistic regression model. For model discrimination, we computed the area under the receiver operating characteristic (ROC) curve. ResultsThe risk of breast cancer predicted by the IBIS model was higher than the BOADICEA model, but lower than the logistic model. The area under the ROC plots indicated that the logistic regression model showed better discrimination between cases and noncases (71.53%) compared with the IBIS model (49.36%) and BOADICEA model (35.84%). Based on the Pierson correlation coefficient, the correlation between IBIS and BOADICEA models was much stronger than the correlation between IBIS and logistic models (0.3884 and 0.1639, respectively). ConclusionThe IBIS model discriminated cases and noncases better than the BOADICEA model in the Iranian population. However, the discrimination of the logistic regression model, which included a larger array of familial, genetic, and personal risk factors, was better than the 2 models.