Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced
magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying …

AIM To assess the feasibility of a radiomics model based on unenhanced magnetic
resonance imaging (MRI) to differentiate small hepatocellular carcinoma (S-HCC)(≤ 2 cm) …

To examine the comparative robustness of computed tomography (CT)-based conventional
radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival …

Objectives To comprehensively evaluate the reporting quality, risk of bias, and radiomics
methodology quality of radiomics models for predicting microvascular invasion in …

Purpose We aimed to develop and validate a radiomics model for differentiating
hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers …

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors.
The existing staging system has a limited budget capacity for HCC recurrence. The authors …

Purpose To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier
model based on data from gadoxetic acid-enhanced MRI. Methods This retrospective study …

Objective To evaluate the performance of a deep learning (DL)-based radiomics strategy on
contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) …

Purpose: We aimed to construct machine learning (ML) radiomics models to predict
response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC) …

Background: Radiomics has emerged as a new approach that can help identify imaging
information associated with tumor pathophysiology. We developed and validated a …