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Hepatic neuroendocrine tumors: Gadoxetic acid-enhanced magnetic resonance imaging findings with an emphasis on differentiation between primary and secondary tumors

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To describe the findings of magnetic resonance (MR) imaging of hepatic neuroendocrine tumors (hNET) and to identify the features that differentiate secondary from primary tumors. Methods: This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. Between August 2008 and December 2014, 50 patients with pathologically proven hNETs who underwent gadoxetic acid-enhanced MR imaging with diffusion-weighted images were included. The patients were divided into two groups according to whether they had primary (n = 17) or secondary (n = 33) hNETs. Qualitative values based on a consensus between two observers [morphologic findings, signal intensity, and enhancement pattern (poor or indeterminate; hepatocellular carcinoma-like or cholangiocarcinoma-like)], and quantitative values (apparent diffusion coefficient) were evaluated as predictors of secondary hNETs using multivariable logistic regression and receiver operating characteristic (ROC) analysis. Results: In multivariate analysis, the presence of multiple lesions (p = 0.011), a tumor size less than 6.3 cm (p = 0.001), and a hepatocellular carcinoma-like enhancement pattern (p = 0.031) were significant independent factors for differentiating secondary from primary hNETs, and achieved a sensitivity of 91%, a specificity of 82%, and an accuracy of 88%, with a value of the area under the ROC curve of 0.931. Conclusion: Using these specific MR imaging criteria, secondary hNETs could be differentiated from primary hNETs with a high degree of accuracy in patients with histopathologically proven hNETs.

Original languageEnglish
Pages (from-to)3331-3339
Number of pages9
JournalAbdominal Radiology
Volume43
Issue number12
DOIs
StatePublished - 1 Jun 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Diagnosis
  • Imaging criteria
  • Liver
  • Magnetic resonance imaging
  • Neuroendocrine tumor

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