Predictive value of an AI model integrating MRI radiomics and clinical features for early recurrence of hepatocellular carcinoma prior to radiofrequency ablation.

Beibei Zhang, Wenhua Li, Jing Tang, Ruixin Li, Xiao Wang, Xiaoming Liu, Tianran Li

Abstract read

In one paragraph

Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Beibei Zhang *Department of Radiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Wenhua Li *Department of Radiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Jing TangDepartment of Radiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Ruixin Li *Department of General Surgery, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Xiao WangDepartment of Radiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Xiaoming LiuBeijing United Imaging Intelligent Medical Technology Research Institute, Beijing, China.

Tianran LiDepartment of Radiology, the Fourth Medical Center, Chinese PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study developed an AI-driven radiomics model fusing pre-radiofrequency ablation (RFA) multi-sequence MRI features and clinical variables to predict early recurrence of hepatocellular carcinoma (HCC) after RFA. Methods: A total of 169 HCC patients who underwent pre-RFA MRI (January 2015-December 2021) were retrospectively enrolled and randomly assigned to a training set ( Results: Alpha-fetoprotein (AFP), platelet count (PLT), and tumor location were identified as independent clinical predictors (all VIF < 5). Of 11,320 extracted features, 61.5% ( Conclusion: Preoperative MRI-based radiomics features combined with clinical variables effectively predict early post-RFA HCC recurrence. The integrated radiomics-clinical model outperforms both clinical-only and radiomics-only models, with the RF classifier yielding optimal performance, providing a reliable tool for individualized treatment decision-making.

Indexed as

artificial intelligenceearly recurrencehepatocellular carcinomamagnetic resonance imagingpredictive modelradiofrequency ablationradiomics

Identifiers

PMID42682984

DOI10.3389/fradi.2026.1824091

PMCPMC13530903

What OpenQuestion holds

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Registered trials

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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.