4.5 Article

Pancreatic neuroendocrine tumors: Correlation between the contrast-enhanced computed tomography features and the pathological tumor grade

期刊

EUROPEAN JOURNAL OF RADIOLOGY
卷 84, 期 8, 页码 1436-1443

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ejrad.2015.05.005

关键词

Neoplasma; Neuroendocrine tumor; Pancreas; CT

资金

  1. Grants-in-Aid for Scientific Research [15K08349] Funding Source: KAKEN

向作者/读者索取更多资源

Objective: To determine whether CT features can predict the pathological tumor grades of pancreatic neuroendocrine tumors (PanNETs) according to the recent WHO classification. Materials and methods: In all, 28 patients with histologically confirmed PanNETs underwent preoperative contrast CT examinations. Thirteen tumors were classified as G1 and 15 as G2. Two radiologists independently evaluated the CT features (tumor delineation, peripancreatic vascular involvement, upstream pancreatic duct dilatation, N (regional lymph node metastasis) and M (distant metastasis) grades, tumor homogeneity, cystic or necrotic change, and tumor conspicuity). The tumor sizes and Hounsfield unit values of all PanNETs during each phase on CT were measured by one radiologist. We compared the CT features between pathological tumor grades using Fisher's exact test for nominal scales and Mann-Whitney U test for ordinal scales or continuous variables. Additionally, we evaluated the performances of the CT findings and their combinations to diagnose G2 tumors. Results: G2 tumors showed significantly larger in tumor size than G1 tumors (p = 0.029). All 4 tumors with hepatic metastases were G2. Non-hyperattenuation compared with pancreatic parenchyma during portal venous phase (PVP) was significantly associated with G2 (p = 0.016). The accuracy for G2 diagnosis of tumor size (>= 20 mm), M grade (M1), and tumor conspicuity (non-hyperattenuation during PVP) were 71%, 61%, and 71%, respectively, while the accuracy of their combination was 82%. Conclusion: Contrast-enhanced CT features (tumor size, M grade, and tumor conspicuity during PVP) can predict the pathological tumor grades of PanNETs. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

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