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Abstract #4959

Differentiation of low- and high- grade hepatocellular carcinomas with texture features and a machine learning model in arterial phase of contrast-enhanced MR

Wu Zhou1, Qiyao Wang1, Guangyi Wang2, Zaiyi Liu2, Changhong Liang2, and Lijuan Zhang1

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

Texture has been a recognized feature for biological aggressiveness of hepatocellular carcinomas (HCCs). However, texture feature alone may not be optimal to characterize malignancy of HCC. Computer-aided techniques combined with multi-feature fusion may be a method of choice for the preoperative assessment of the aggressiveness of HCC. To this end, a computer-aided method in the combination with machine learning technique based on texture analysis for malignancy differentiation of HCCs was desmonrated and high classification performance(AUC>0.9) of the classifier was achieved to differentiate low- and high- grade of HCCs.

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