Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading - Chen, Wen; Zhang, Tao; Xu, Lin; Zhao, Liang; Liu, Huan; Gu, Liang Rui; Wang, Dai Zhong; Zhang, Ming (Frontiers in Oncology, 2021)
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.