Back to Search Start Over

Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times

Authors :
Wang Gang
Haiyang Yu
Jiao Linlin
Ruirui Zhao
Lan Yu
Xiaoming Zhou
Yuanxiang Gao
Guizhi Xu
Duan Chongfeng
Xin Wang
Shuai Zhang
Zhiming Li
Source :
BioMed Research International, Vol 2021 (2021), BioMed Research International
Publication Year :
2021
Publisher :
Hindawi Limited, 2021.

Abstract

Purpose. To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method. A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results. The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions. Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.

Details

Language :
English
ISSN :
23146141 and 23146133
Volume :
2021
Database :
OpenAIRE
Journal :
BioMed Research International
Accession number :
edsair.doi.dedup.....d1f8adaf5a5dc33d6e7259e9126ff7d9