Abstract

Info

Title:: Prediction of grade of hepatocellular carcinoma by radiomics based on ultrasound

Author(s):: ZHOU Liu1; DONG Yi2; XIA Wei3; ZHAO Xingyu3; ZHANG Qi2; WANG Wenping2; GAO Xin3; YANG Jun1; 1. Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300192, China; 2. Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032, China; 3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

Keywords:: Keywords: hepatocellular carcinoma; radiomics; differentiation grade; related feature

Abstract:: Abstract: Objective To propose a radiomics model based on gray-scale ultrasound images for solving the problem of predicting the grade of hepatocellular carcinoma (HCC). Methods Firstly, the tumor areas were segmented by an ultrasound physician, and then various features of tumor areas, including shape, the first order statistical properties and texture features were extracted by radiomics. Pearson’s correlation coefficient was used to eliminate the redundant features. Finally, univariate analysis was used for obtaining the optimal feature subset, and LASSO for constructing a model for predicting the grade of HCC. The area under the receiver operating characteristic curve (AUC) of the model was calculated by leave-one-of-cross validation so as to evaluate the prediction ability of the model. Results The radiomics model for prediction of the grade of HCC was constructed using gray-scale ultrasound images of 43 cases of HCC confirmed by operation and pathology. The obtained model was composed of 6 image features which was highly correlated with grading, and the results showed that the proposed model had preferable predication performances (AUC=0.76). Conclusion The image features based on gray-scale ultrasound images are highly correlated with the grade of HCC. The established radiomics model can be used to better predict the grade of HCC.