ArticleAbdominal radiology (New York)2023
DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Construction of a novel prediction model based on albumin-hemoglobin score and serum microRNA-497-5p for prognosis of patients with stage II-III colorectal cancer.Oncology letters · 2026Article
- A generative vision-language model for holistic pathological assessment using preoperative imaging in hepatocellular carcinoma.EBioMedicine · 2025Article
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Comparison of dynamic contrast-enhanced-magnetic resonance imaging parameters and serum markers in preoperative rectal cancer evaluation: Combined diagnostic value.World journal of gastrointestinal oncology · 2025Article
- Advances in imaging techniques for tumor microenvironment evaluation in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.Journal of hepatocellular carcinoma · 2025Article
- Harnessing big data for precision medicine: radiomics based application of nanomaterials in MRI enhancement and multimodal therapy of hepatocellular carcinoma.Frontiers in immunology · 2025Review
- Radiomic analysis based on magnetic resonance imaging for the prediction of VEGF expression in hepatocellular carcinoma patients.Abdominal radiology (New York) · 2024Article
- Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.Frontiers in oncology · 2024Review
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7 authors at 2 institutions in 1 country.
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Abstract
backgroundHepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics.
methodsThe data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.
resultsAmong the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit.
conclusionThe DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.
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