Back to Search
Start Over
Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading.
- Source :
- Frontiers in Oncology; 6/4/2021, Vol. 11, p1-7, 7p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2234943X
- Volume :
- 11
- Database :
- Complementary Index
- Journal :
- Frontiers in Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 150708577
- Full Text :
- https://doi.org/10.3389/fonc.2021.660509