AUTHOR=Chen Wen , Zhang Tao , Xu Lin , Zhao Liang , Liu Huan , Gu Liang Rui , Wang Dai Zhong , Zhang Ming 

TITLE=Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.660509

DOI=10.3389/fonc.2021.660509

ISSN=2234-943X

ABSTRACT=Objectives: To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics for preoperatively discriminating high-grade from low-grade hepatocellular carcinoma (HCC).
Methods: The retrospective study was approved by institutional review board, and the informed consent requirement was waived. Data from 161 consecutive subjects with   HCC were divided into training group (n=112) and test group (n=49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to select the most valuable features to establish a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity.
Results: The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity（82.5% versus 88.0%）, and specificity (92.7% versus 95.8%), respectively.
Conclusion: The machine learning-based radiomics exhibits a superior diagnostic performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.