title: Morphological analysis and differentiation of benign cystic neoplasms of the pancreas using computed tomography and magnetic resonance imaging.
abstract: OBJECTIVE
  To evaluate morphologic characteristics and establish a standardized diagnostic algorithm to differentiate benign cystic pancreatic tumors (CPTs) in non-pancreatitis patients using multidetector computed tomography (CT) and magnetic resonance imaging (MRI).
  
  
  METHODS
  Patients with histopathologically proven CPTs who had undergone MRI and/or CT and subsequent tumor resection in our institution were retrospectively identified. Images were analyzed for morphology and enhancement patterns by three independent blinded observers. Preoperative image findings were correlated with histopathological results. Based on the evaluated morphologic parameters, a standardized diagnostic algorithm was designed to help characterize the lesions.
  
  
  RESULTS
  A total of 62 consecutive patients with 64 CPTs were identified from the surgical database (21 intraductal papillary mucinous neoplasms; 10 mucinous cystic neoplasms; 12 serous microcystic adenomas; 3 serous oligocystic adenomas; 6 solid pseudopapillary tumors; 12 neuroendocrine neoplasms). The overall averaged accuracy for the 3 observers was 89.9 % for CT and 93.1 % for MRI with increasing overall accuracy in relation to the experience of the observer (88.2 %, 91.5 %, and 93.8 %, respectively). Overall, the generalized kappa value was 0.69 (CT, 0.64; MRI, 0.76); p < 0.001). The accuracy of the standardized diagnostic algorithm was 91.1 %.
  
  
  CONCLUSIONS
  It is possible to characterize benign CPTs with MRI and CT, while MRI appears to be superior to CT. Diagnostic accuracy depends on the observer's experience. The standardized algorithm can aid in the differential diagnosis but still needs to be tested in other patient populations.
authors.affiliation: Klinik für Radiologie, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin. christian.grieser@charite.de
authors.forename: C
authors.initials: C
authors.lastname: Grieser
authors.forename: G
authors.initials: G
authors.lastname: Heine
authors.forename: L
authors.initials: L
authors.lastname: Stelter
authors.forename: I G
authors.initials: IG
authors.lastname: Steffen
authors.forename: J H
authors.initials: JH
authors.lastname: Rothe
authors.forename: T C
authors.initials: TC
authors.lastname: Walter
authors.forename: C
authors.initials: C
authors.lastname: Fischer
authors.forename: M
authors.initials: M
authors.lastname: Bahra
authors.forename: T
authors.initials: T
authors.lastname: Denecke
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003287:Contrast Media / Q000008:administration & dosage; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods* / Q000592:standards; D010179:Pancreas / Q000473:pathology; D010181:Pancreatic Cyst / Q000473:pathology*; D010190:Pancreatic Neoplasms / Q000473:pathology*; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media