Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients

Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients

Aim: To assess the utility of different radiomics feature selection methods in predicting transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC) patients. Materials and methods: This study employed a dataset of 136 paired MR T1-weighted contrast-enhanced abdominal images w...

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Bibliographic Details
Main Authors: Helen Zhang, Li Yang, Amanda Laguna, Jing Wu, Beiji Zou, Alireza Mohseni, Rajat S. Chandra, Tej I. Mehta, Hossam A. Zaki, Paul Zhang, Zhicheng Jiao, Ihab R. Kamel, Harrison X. Bai
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2024-03-01
Series:Meta-Radiology
Subjects:
Radiomics
Feature selection
Machine learning
Hepatocellular carcinoma
Transarterial chemoembolization
Online Access:http://www.sciencedirect.com/science/article/pii/S2950162824000201
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