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Erschienen in:
01.11.2014 | Computed Tomography
Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification
Erschienen in: European Radiology | Ausgabe 11/2014
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Objectives
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.
Key Points
• 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.