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Updated: Jan 30, 2026

Generation of Subcutaneous and Intrahepatic Human Hepatocellular Carcinoma Xenografts in Immunodeficient Mice
Published on: September 25, 2013
Differentiating between hepatocellular carcinoma and intrahepatic cholangiocarcinoma using contrast-enhanced MRI
1Department of Radiology, Kyung Hee University Hospital, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
Aim:
To identify magnetic resonance imaging (MRI) features for differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (IHCC) and summarise their diagnostic accuracy.
Materials And Methods:
PubMed and EMBASE were searched for studies that employed MRI features to differentiate HCC from IHCC. Overlapping descriptors used to denote the same imaging finding in different studies were subsumed under a single feature. The pooled diagnostic accuracies, including the diagnostic odds ratios (DORs) and 95% confidence intervals (CIs) of the identified features, were calculated using a bivariate random-effects model.
Results:
In total, 1,370 patients with HCC and 687 patients with IHCC in 14 studies were included. Fifty-two descriptors were subsumed under 15 MRI features. Of these, 11 features were informative for differentiating HCC from IHCC. The five MRI features favouring HCC were capsule, arterial diffuse enhancement, portal venous washout, conventional washout, and intralesional fat; the six MRI features favouring IHCC were surface retraction, arterial rim enhancement, progressive enhancement, target appearance on diffusion-weighted and hepatobiliary phase (HBP) images, and bile duct dilatation. These features tended to show high specificity, but low sensitivity. Useful MRI features with high DORs (>20) were capsule (34; 95% CI, 5-215) and intralesional fat (23; 4-85) for HCC and arterial rim enhancement (31; 6-160), progressive enhancement (24; 8-73), and target appearance on HBP images (29; 3-261) for IHCC.
Conclusion:
Eleven informative MRI features for differentiating HCC from IHCC were identified. These features will assist in the accurate diagnosis of these diseases and in disease outcome prediction.
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