ArticleFrontiers in oncology2022
Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CT radiomics and topology-based intratumoral heterogeneity for preoperative prediction of nuclear β-catenin expression in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Beyond Diagnosis: Magnetic Resonance Imaging-Based Imaging Biomarkers for Tumor Biology, Prognosis, and Clinical Decision-Making in Hepatocellular Carcinoma.Korean journal of radiology · 2026Review
- Imaging for molecular and pathological subtyping of hepatocellular carcinoma-a critical appraisal and future directions.European radiology · 2026Review
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.Frontiers in oncology · 2024Review
- Exploring the Impact of the β-Catenin Mutations in Hepatocellular Carcinoma: An In-Depth Review.Cancer control : journal of the Moffitt Cancer CenterReview
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11 authors.
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Abstract
Objective: To compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC). Methods: Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models. Results: Among all 15 types of radiomics models, the model with the best performance was seen in the R Conclusion: The R
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