Evidence map›Paper›PMID 41575341›Full record

ArticleRadiology. Imaging cancer2026

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

Can Yu, Xinxin Wang, Shuli Tang, Yan Li, Shuai Han, Qiuju Zhang, Jinrong Qu, Haitao Xu, Yang Zhou

Abstract read
In one paragraph

Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Can YuDepartment of Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Xinxin WangDepartment of Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Shuli TangDepartment of Outpatient Chemotherapy, Harbin Medical University Cancer Hospital, Harbin, China.
Yan LiDepartment of Biostatistics, Public Health School of Harbin Medical University, Harbin, China.
Shuai HanDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
Qiuju ZhangHealth Management Centre, Harbin Medical University Cancer Hospital, Harbin, China.
Jinrong QuDepartment of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.ORCID 0000-0001-8751-9988
Haitao XuDepartment of Hepatobiliary and Pancreatic Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Yang ZhouDepartment of Radiology, Harbin Medical University Cancer Hospital, Harbin, China.ORCID 0000-0001-5296-220X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose 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. Materials and Methods 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. Results 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

Indexed as

Carcinoma, HepatocellularContrast MediaGadolinium DTPALiver NeoplasmsMagnetic Resonance ImagingAgedFemaleHumansMaleMiddle AgedNeoplasm InvasivenessPrognosisRadiomicsRetrospective StudiesContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPAAbdomen/GIGd-EOB-DTPA-enhanced MRIHigh-Risk Solitary Hepatocellular CarcinomaLiverMachine LearningMicrovascular InvasionMRIOATP1B3RadiogenomicsRadiomicsSurgeryTumor Margin

Identifiers

PMID41575341
PMCPMC12862467

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.