Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading

• Main Authors: Wen Chen, Tao Zhang, Lin Xu, Liang Zhao, Huan Liu, Liang Rui Gu, Dai Zhong Wang, Ming Zhang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-06-01
• Series: Frontiers in Oncology
• Subjects: radiomics; machine learning; support vector machine; hepatocellular carcinoma; grading
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.660509/full

ObjectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.MethodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsThe 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.ConclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.