ArticleFrontiers in oncology2024
Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Non-Invasive Assessment of Microvascular Invasion Risk in Hepatocellular Carcinoma Using Liquid Biopsy: Translational Insights and Clinical Implications.Diagnostics (Basel, Switzerland) · 2026Review
- Radiogenomics and machine learning in hepatocellular carcinoma: from foundations to clinical translation.World journal of surgical oncology · 2026Review
- Review
- Transformer-Based Deep Learning for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Cancers · 2025Article
- MiR-10a-5p suppresses hepatocellular carcinoma progression and microvascular invasion by targeting TFR1-STAT3-CD24 signaling axis.Frontiers in oncology · 2025Article
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13 authors.
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Abstract
Purpose: This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs). Materials and methods: This cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful. Results: The independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21). Conclusion: The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.
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