We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading.
- Authors
Chen, Wen; Zhang, Tao; Xu, Lin; Zhao, Liang; Liu, Huan; Gu, Liang Rui; Wang, Dai Zhong; Zhang, Ming
- Abstract
Objectives: To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. Methods: The 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. 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 reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
- Subjects
RADIOMICS; HEPATOCELLULAR carcinoma; CONTRAST-enhanced magnetic resonance imaging; SUPPORT vector machines; INSTITUTIONAL review boards
- Publication
Frontiers in Oncology, 2021, Vol 11, p1
- ISSN
2234-943X
- Publication type
Article
- DOI
10.3389/fonc.2021.660509