ArticleQuantitative imaging in medicine and surgery2024
Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI-based radiomics for noninvasive prediction of GPC3 expression in hepatocellular carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Multi-scale deep learning models based on MRI for predicting pathological differentiation and evaluating its association with recurrence-free survival in hepatocellular carcinoma: an explainable machine learning study.Journal of gastrointestinal oncology · 2026Article
- Dual-Energy CT-Derived Parameters: A Promising Tool for Noninvasive Prediction of Glypican-3 in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Explainable machine learning-based multiphase contrast-enhanced CT radiomics for noninvasively predicting GPC3 expression in hepatocellular carcinoma: a bicentric study.American journal of translational research · 2026Article
- Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.Journal of translational medicine · 2025Article
- Targeting glypican-3 as a new frontier in liver cancer therapy.World journal of hepatology · 2025Review
- Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma.World journal of hepatology · 2025Article
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6 authors.
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Abstract
Background: Hepatocellular carcinoma (HCC) is often associated with the overexpression of multiple proteins and genes. For instance, patients with HCC and a high expression of the glypican-3 ( Methods: We conducted a retrospective analysis of 143 patients with HCC, including 123 cases from our hospital and 20 cases from The Cancer Genome Atlas (TCGA) or The Cancer Imaging Archive (TCIA) public databases. We used preoperative multisequence MRI images of the patients for the radiomics analysis. We extracted and screened the imaging histologic features using fivefold cross-validation, Pearson correlation coefficient, and the least absolute shrinkage and selection operator (LASSO) analysis method. We used logistic regression (LR) to construct a radiomics model, developed nomograms based on the radiomics scores and clinical parameters, and evaluated the predictive performance of the nomograms using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: Our multivariate analysis results revealed that tumor morphology (P=0.015) and microvascular (P=0.007) infiltration could serve as independent predictors of Conclusions: Our study findings highlight the close association of multisequence MRI imaging and radiomic features with
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