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Abstract #0666

Quantitative texture feature to predict Microscopic portal vein invasion of Hepatocellular carcinoma with contrast-enhanced MR images

Wu Zhou1, Qiyao Wang1, Su Yao2, Guangyi Wang3, Zaiyi Liu3, Changhong Liang3, and Lijuan Zhang1

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China, 2Department of Pathology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 3Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences

Portal vein invasion of Hepatocellular carcinoma (HCC) is a well-known prognostic factor for patients after hepatic resection or liver transplant. Typical risk factors of microscopic venous infiltration (MVI) include large tumor size, multifocality, poor histological grade and non-smooth tumor margins. In this work, we adopt a new techniue for quantitative texture feature Gray-level-run-length nonuniformity (GLRLN) to predict MVI of HCC based on the contrast-enhanced magnetic resonance images (CE-MRI). The present study showed that the proposed texture feature GLRLN in the portal vein phase of CE-MRI yielded best performance as compared with typical markers for the prediction of MVI.

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