Objective:

To build a comprehensive Breast Imaging Reporting and Data System (BI-RADS) predictive model of breast lesions for getting BI-RADS classification that different from single imaging.

Methods:

The images of patients undergoing preoperative ultrasound

mammography and magnetic resonance imaging (MRI) examination from August 2019 to September 2020 were retrospectively analyzed. The tumor characteristics were evaluated according to the BI-RADS lexicon. The

highest BI-RADS category of the three images was taken as the dependent variable

and the imaging characteristics and clinical indications were taken as independent variables. A comprehensive BI-RADS predictive model was constructed based on multivariate logistic regression.

Results:

The positive predictive value (PPV) of the comprehensive BI-RADS predictive model category was within the reference range of the guide ［category 3 (0.00%)

category 4A (9.61%)

category 4B (42.41%)

category 4C (88.18%)

category 5 (97.19%)］

the area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.955.

Conclusion:

It is feasible to unify the BI-RADS lexicon of the three imaging and the clinical features of the patients to obtain the comprehensive BI-RADS model

which greatly avoids the incidence of misdiagnosis.