title: Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification.
abstract: OBJECTIVE
  We aimed to evaluate the multi-slice computed tomography (MSCT) features of pancreatic neuroendocrine neoplasms (P-NENs) and analyse the correlation between the MSCT features and pathological classification of P-NENs.
  
  
  METHODS
  Forty-one patients, preoperatively investigated by MSCT and subsequently operated on with a histological diagnosis of P-NENs, were included. Various MSCT features of the primary tumour, lymph node, and distant metastasis were analysed. The relationship between MSCT features and pathologic classification of P-NENs was analysed with univariate and multivariate models.
  
  
  RESULTS
  Contrast-enhanced images showed significant differences among the three grades of tumours in the absolute enhancement (P = 0.013) and relative enhancement (P = 0.025) at the arterial phase. Univariate analysis revealed statistically significant differences among the tumours of different grades (based on World Health Organization [WHO] 2010 classification) in tumour size (P = 0.001), tumour contour (P < 0.001), cystic necrosis (P = 0.001), tumour boundary (P = 0.003), dilatation of the main pancreatic duct (P = 0.001), peripancreatic tissue or vascular invasion (P < 0.001), lymphadenopathy (P = 0.011), and distant metastasis (P = 0.012). Multivariate analysis suggested that only peripancreatic tissue or vascular invasion (HR 3.934, 95 % CI, 0.426-7.442, P = 0.028) was significantly associated with WHO 2010 pathological classification.
  
  
  CONCLUSIONS
  MSCT is helpful in evaluating the pathological classification of P-NENs.
  
  
  CONCLUSIONS
  • P-NENs are potentially malignant, and classification of P-NENs carries important prognostic value. • MSCT plays an important role in the diagnosis and staging of P-NENs. • Correlations between classification of P-NENs and imaging findings have not been systematically evaluated. • MSCT could predict P-NENs classification and may be a useful prognostication tool.
authors.affiliation: Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, China, 510080, 583149136@qq.com.
authors.forename: Yanji
authors.initials: Y
authors.lastname: Luo
authors.forename: Zhi
authors.initials: Z
authors.lastname: Dong
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.forename: Tao
authors.initials: T
authors.lastname: Chan
authors.forename: Yuan
authors.initials: Y
authors.lastname: Lin
authors.forename: Minhu
authors.initials: M
authors.lastname: Chen
authors.forename: Zi-Ping
authors.initials: ZP
authors.lastname: Li
authors.forename: Shi-Ting
authors.initials: ST
authors.lastname: Feng
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods*; D018358:Neuroendocrine Tumors / Q000145:classification* / Q000175:diagnosis* / Q000601:surgery; D010180:Pancreatectomy; D010190:Pancreatic Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000601:surgery; D012189:Retrospective Studies