Value of adding GNAS testing to pancreatic cyst fluid KRAS and carcinoembryonic antigen analysis for the diagnosis of intraductal papillary mucinous neoplasms.

BACKGROUND AND AIM: Molecular analysis of pancreatic cyst fluid (PCF) has been proposed as a novel method for differentiating pancreatic cystic lesions (PCL). The present study aimed to investigate the value of GNAS testing when added to KRAS and carcinoembryonic antigen (CEA) testing of PCF for the diagnosis of intraductal papillary mucinous neoplasms (IPMN). METHODS: Prospectively collected endoscopic ultrasonography fine-needle aspiration (EUS-FNA) data were analyzed retrospectively for GNAS and KRAS mutations and CEA results. IPMN were histologically confirmed or supported by imaging and EUS-FNA findings (KRAS, CEA, cytology). Performance characteristics of GNAS added to KRAS and CEA for the diagnosis of IPMN were calculated. RESULTS: The study population consisted of 197 patients with cyst fluid test results. Cysts were histologically classified in 33 patients and by clinical criteria in 164 patients. The IPMN group included 108 patients and the non-IPMN group included 89 patients. GNAS was positive in 51 patients (47.2%) with IPMN. Forty-two of these patients (82.3%) also had a KRAS mutation. Adding GNAS to KRAS increased the diagnostic accuracy from 76.6% to 79.1% (P > 0.05). Adding GNAS to CEA increased the diagnostic accuracy from 66.4% to 80.7 % (P < 0.05), but did not achieve a diagnostic superiority to KRAS testing alone (80.7% vs 76.6%, P > 0.05). The diagnostic accuracy of the triple combination was significantly better than all single tests (P < 0.05). CONCLUSION: 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 in the analysis of fat-containing lesions.

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.