A Breast Cancer Risk Prediction Model Based on the Clinical Characteristics and Sonographic Features

  Objective  To propose a breast cancer risk prediction model by analyzing the clinical characteristics and sonographic features of breast lesions.

  Methods  A total of 116 consecutive breast lesion samples obtained by biopsy in our hospital from July 2007 to January 2009 were retrospectively examined. Open biopsies were performed on each patient. The pathological results were used as the golden standard of diagnosis.Multivariate logistic regression analysis was used to identify the independent risk factors of breast cancer including age, family history of breast cancer, the hardness, mobility, shape, margin, orientation, posterior acoustic features, and calcification of the masses. The prediction model was developed and a receiver operating characteristic (ROC) curve was used to evaluate the efficacy of the prediction model.

  Results  Of the 116 breast lesions examined, 52 breast lesions were diagnosed as breast cancer. The independent risk factors included the patient's age ofmore than 50 years old (OR=6.61, 95%CI 1.07-40.72), hard mass (OR=6.56, 95%CI 1.32-32.58), irregular shape (OR=19.93, 95%CI 2.49-159.45), instinct margins(OR=21.32, 95%CI 1.98-230.14) and angular or speculated margins (OR=31.33, 95%CI 2.61-376.02). The whole accuracy of this prediction model was 96.7%.

  Conclusions  We developed a breast cancer risk prediction model and proposed independent risk factors, which can help predict the risk of breast cancer in clinical practices.