# Gadoxetic Acid–enhanced MRI Radiomics Features of Tumor Margins for Predicting High-Risk Solitary Hepatocellular Carcinoma Aggressiveness and Prognosis

## TL;DR

This study uses MRI radiomics to predict aggressive features in liver cancer by analyzing tumor margins, improving prognosis and treatment planning.

## Contribution

The novel contribution is a radiomics model using EOB-MRI tumor margin features to predict microvascular invasion and prognosis in high-risk hepatocellular carcinoma.

## Key findings

- The radiomics model achieved AUCs of 0.80 in training and 0.72 in external testing for predicting microvascular invasion.
- High optimal margin region scores correlated with extracellular matrix remodeling and M2 macrophage infiltration.
- The model effectively stratified patients by overall and disease-free survival.

## Abstract

To develop a radiomics model based on hepatobiliary phase gadolinium
ethoxybenzyl-diethylenetriaminepentaacetic acid (EOB)–enhanced
MRI features at the tumor margin to predict microvascular invasion in
high-risk solitary hepatocellular carcinoma (HR-sHCC), determine the
optimal margin region, and explore the underlying biologic
mechanisms.

This retrospective study included patients with HR-sHCC from three
medical centers between April 2015 and December 2022. Radiomics features
were extracted from 121 volumes of interest (VOIs) at the tumor margin
at EOB MRI. Nine combinations of statistical and machine learning
methods were used to construct and validate the optimal margin
region–based radiomics model. Model performance was assessed
using the area under the receiver operating characteristic curve (AUC),
and patient stratification was evaluated with Kaplan–Meier and
log-rank analyses. RNA sequencing data underwent differential expression
analysis with DESeq2, followed by Kyoto Encyclopedia of Genes and
Genomes (ie, KEGG) and Gene Ontology (ie, GO) enrichment, and immune
cell infiltration was assessed using xCell and EPIC.

A total of 436 patients (mean age, 57.7 years ± 8.8 [SD]; 352
male) were included: 254 in the training, 108 in the internal test, and
74 in the external test cohorts. Receiver operating characteristic
analysis showed AUCs of 0.80 (95% CI: 0.74, 0.86), 0.76 (95% CI: 0.66,
0.85), and 0.72 (95% CI: 0.58, 0.86), respectively. The model
effectively stratified patients by overall and disease-free survival
(all P < .05). RNA sequencing revealed
extracellular matrix remodeling, transforming growth
factor–β signaling, and M2 macrophage infiltration in high
optimal margin region–score tumors.

The optimal margin region–based radiomics model, derived from EOB
MRI, effectively captured tumor margin heterogeneity.

Keywords: MRI, Machine Learning, Radiomics, Radiogenomics,
Abdomen/GI, Liver, Surgery, High-Risk Solitary Hepatocellular Carcinoma,
Tumor Margin, Microvascular Invasion, Gd-EOB-DTPA-enhanced MRI,
OATP1B3

© The Author(s) 2026. Published by the Radiological Society of
North America under a CC BY 4.0 license.

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