SynthesisAbdominal radiology (New York)2026
Magnetic resonance imaging radiomics for predicting hepatocellular carcinoma recurrence following resection or ablation: a systematic review and meta-analysis.
Synthesis in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Medical Image-Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence in hepatocellular carcinoma: imaging-based subtyping and prediction of treatment response and prognosis.Abdominal radiology (New York) · 2026Review
- Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology.Oncology research · 2026Review
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3 authors.
Funding
Abstract
objectiveWe assessed predictive performance of MRI radiomics models for hepatocellular carcinoma (HCC) recurrence after curative treatments, aiming to inform future research on optimizing MRI-based radiomics for HCC management.
methodsThis study followed PRISMA and we systematically searched for studies predicting HCC recurrence and microvascular invasion (MVI). Quality assessment used Quality Assessment of Diagnostic Accuracy Studies, Radiomics Quality Score, and METhodological RadiomICs Score (METRICS) tools. Meta-analyses were performed with random-effects models, assessing heterogeneity.
resultsRadiomics models achieved a pooled concordance index (C-index) of 0.78(95%CI:0.71-0.85) in training and 0.73(95%CI:0.69-0.77) in validation cohorts, outperforming clinico-radiological models. For recurrence prediction after liver resection, radiomics models achieved pooled sensitivity/specificity of 0.81(0.74-0.87)/0.85(0.77-0.90) in training (AUC: 0.87; accuracy: 0.81[95%CI:0.76-0.86]) and 0.80(0.73-0.86)/0.74(0.68-0.80) in validation cohorts (AUC: 0.83; accuracy: 0.79[0.75-0.83]). For thermal ablation, sensitivity/specificity were 0.80(0.60-0.90)/0.79(0.74-0.84) in training (AUC: 0.79; accuracy: 0.78[0.72-0.84]) and 0.79(0.69-0.87)/0.78(0.57-0.90) in validation (AUC:0.82; accuracy:0.76[0.67-0.84]). For MVI prediction, radiomics models achieved 0.87(0.78-0.92)/0.88(0.80-0.93) in training (AUC: 0.93; accuracy: 0.86[0.80-0.91]), and 0.88(0.80-0.94)/0.79(0.72-0.84) in validation cohorts (AUC: 0.87; accuracy: 0.82[0.77-0.86]). Subgroup analysis revealed significantly improved specificity for models using PyRadiomics and LASSO for feature extraction and selection, respectively.
conclusionMRI radiomics models outperformed routine clinico-radiological models, with potentially enhanced clinical decision-making. Future studies should focus on cost-effectiveness and external validation to support broader clinical integration. ADVANCES IN KNOWLEDGE: This study demonstrates that MRI radiomics models significantly enhance predictive accuracy for HCC recurrence and MVI, highlighting their potential as noninvasive tools to guide personalized treatment planning.
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