Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases

Abstract Purpose To analyze women with suspicious findings (assessed as Breast Imaging Reporting and Data System [BI-RADS] 4), examining the value of clinical and imaging predictors in predicting cancer diagnosis. Patients and Methods A set of 2138 examinations (1978 women) given a BI-RADS 4 with matching pathology results were analyzed. Predictors such as patient demographics, clinical risk factors, and imaging-derived features such as BI-RADS assessment and qualitative breast density were considered. Independent predictors of breast cancer were determined by univariate analysis and multivariate logistic regression. Results In univariate analysis, age, race, body mass index, age at first live birth, BI-RADS assessment, qualitative breast density, and risk triggers were found to be independent predictors. In multivariate analysis, age, BI-RADS score, breast density, race, presence of a lump, and number of risk triggers were the most predictive. An integrative logistic regression model achieved a performance of 0.84 cross-validated area under the curve. No variable was a constant independent predictor when stratifying the population on the basis of the BI-RADS score. Conclusion While BI-RADS assessment remains the strongest predictor of breast cancer, the inclusion of clinical risk factors such as age, breast density, presence of a lump, and number of risk triggers derived from guidelines improves the specificity of identifying individuals with imaging descriptors associated with BI-RADS 4A and 4B that are more likely to be diagnosed with breast cancer.