AI Summary & Key Takeaways
AI InsightHowever, objective and clear subclassifi-cation rules are needed.
📌 3 Key Takeaways
Abstract
PURPOSE We aimed to evaluate ultrasonography (US) findings for Breast Imaging Reporting and Data System (BI-RADS) cate-gory 4 lesions using BI-RADS US lexicon and determine the positive and negative predictive values (PPV and NPV) of US with respect to biopsy results. METHODSSonograms of 186 BI-RADS 4 nonpalpable breast lesions with a known diagnosis were reviewed retrospectively. The mor-phologic features of all lesions were described using BI-RADS lexicon and the lesions were subcategorized into 4A, 4B, and 4C on the basis of the physician's level of suspicion. Lesion descriptors and biopsy results were correlated. Pathologic re-sults were compared with US features. PPVs of BI-RADS sub-categories 4A, 4B, and 4C were calculated. RESULTS Of 186 lesions, 38.7% were malignant and 61.2% were be-nign. PPVs according to subcategories 4A, 4B, and 4C were 19.5%, 41.5%, and 74.3%, respectively. Microlobulated, indistinct, and angular margins, posterior acoustic features, and echo pattern were nonspecific signs for nonpalpable BI-RADS 4 lesions. Typical signs of malignancy were irregular shape (PPV, 66%), spiculated margin (PPV, 80%) and nonpar-allel orientation (PPV, 58.9%). Typical signs of benign lesions were oval shape (NPV, 77.1%), circumscribed margin (NPV, 67.5%), parallel orientation (NPV, 70%), and abrupt interface (NPV, 67.6%). CONCLUSIONBI-RADS criteria are not sufficient for discriminating between malignant and benign lesions, and biopsy is required. Sub-categories 4A, 4B, and 4C are useful in predicting the likeli-hood of malignancy. However, objective and clear subclassifi-cation rules are needed.