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Characterization of Solid Breast Masses

Use of the Sonographic Breast Imaging Reporting and Data System Lexicon

Departments of Bio-Imaging and Radiological Sciences (M.C., P.B., R.L., L.B.), Surgery (G.F.), and Pathology (A.M.), Catholic University, Rome, Italy.

Address correspondence to Melania Costantini, MD, Department of Bio-Imaging and Radiological Sciences, Catholic University, Largo F. Vito 8, 00168 Rome, Italy. E-mail: mcostantini{at}rm.unicatt.it

Objective. The purpose of this study was to determine the reliability of sonographic American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) classification in differentiating benign from malignant breast masses. Methods. One hundred seventy-eight breast masses studied by sonography with a known diagnosis were reviewed. All lesions were classified according to the sonographic BI-RADS lexicon. Pathologic results were compared with sonographic features. Sensitivity, specificity, accuracy, and positive predictive value (PPV) and negative predictive value (NPV) for the sonographic BI-RADS lexicon were calculated. Results. Twenty-six cases were assigned to class 3, 73 to class 4, and 79 to class 5. Pathologic results revealed 105 malignant and 73 benign lesions. The sonographic BI-RADS lexicon showed 71.3% accuracy, 98.1% sensitivity, 32.9% specificity, 67.8% PPV, and 92.3% NPV. The NPV for class 3 was 92.3%. The PPVs for classes 4 and 5 were 46.6% and 87.3%. Typical signs of malignancy were irregular shape, antiparallel orientation, noncircumscribed margin, echogenic halo, and decreased sound transmission. Typical signs of benignity were oval shape and circumscribed margin. Conclusions. The sonographic BI-RADS lexicon is an important system for describing and classifying breast lesions.

Key Words: breast masses • diagnosis • sonography

Abbreviations: ACR, American College of Radiology • BI-RADS, Breast Imaging Reporting and Data System • FNAC, fine-needle aspiration cytologic examination • NPV, negative predictive value • PPV, positive predictive value

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