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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