GNAS mutation is a highly specific test for IPMN. When GNAS testing is added to CEA and KRAS, a significantly greater overall accuracy (86.2%) is achieved.
Digital breast tomosynthesis (DBT) is rapidly emerging as an important clinical tool for both screening and diagnosis. DBT improves upon mammography by depicting breast tissue on a dynamic sequence of cross-sectional images reconstructed in planes corresponding to their mammographic planes of acquisition. DBT results in markedly reduced summation of overlapping tissue and depicts the margins of masses in far greater detail than mammography. Fat is commonly recognized in both benign and malignant breast masses at DBT, even when no fat is appreciated at mammography. In cases of encapsulated fat-containing masses, the increased detail at DBT often allows the radiologist to definitively classify a mass as benign (eg, lipoma, hamartoma, galactocele, lipid cyst) when mammographic findings alone are equivocal, thereby avoiding unnecessary biopsy or workup. However, when learning to read DBT images, many radiologists misinterpret this rule, mistaking the presence of any fat within a mass for an indication of benignity or an artifact and falsely concluding that an otherwise suspicious mass is not worrisome. If fat seen in breast masses at DBT is not appropriately analyzed, malignant breast masses may be incorrectly classified as probably or even definitely benign. With use of radiologic-pathologic correlation, the authors illustrate cases in which the presence of fat can help correctly classify a mass as benign, and pitfalls in which the presence or absence of fat within a mass is irrelevant and should not influence analysis.
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