ArticleFrontiers in oncology2022
Combined clinical features and MRI parameters for the prediction of VEGFR2 in hepatocellular carcinoma patients.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine Learning (ML) and Molecular Dynamics-Driven Optimization of VEGFR2 Ligands against Hepatocellular Carcinoma.Oncology research · 2026Article
- Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype.Journal of neuro-oncology · 2024Article
- Chronic Administration of Diethylnitrosamine and 2-Acetylaminofluorene Induces Hepatocellular Carcinoma in Wistar Rats.International journal of molecular sciences · 2023Article
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Authors and funding
9 authors.
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Abstract
Purpose: To develop a prediction model for estimating the expression of vascular endothelial growth factor receptor 2 (VEGFR2) in hepatocellular carcinoma (HCC) patients using clinical features and the contrast-enhanced MRI Liver Imaging Reporting and Data System (LI-RADS). Methods: A total of 206 HCC patients were subjected to preoperative contrast-enhanced MRI, radical resection, and VEGFR2 immunohistochemistry labeling. The intensity of VEGFR2 expression was used to split patients into either the positive group or the negative group. For continuous data, the Mann-Whitney U test was employed, and for categorical variables, the χ2 test was utilized. Results: VEGFR2-positivity was identified in 41.7% (86/206) of the patients. VEGFR2-positive HCCs were confirmed by higher serum alpha-fetoprotein (AFP) levels, larger tumor dimensions (either on MRI or upon final pathology), and a higher LI-RADS score (all p < 0.001). LI-RADS scores and AFP levels were independent predictors for high VEGFR2 expression. These two parameters were used to establish a VEGFR2-positive risk nomogram, which was validated to possess both good discrimination and calibration. The area under the curve was 0.830 (sensitivity 83.6%, specificity 72.5%) and the mean absolute error was 0.021. The threshold probabilities ranged between 0.07 and 0.95, and usage of the model contributed net benefits. Conclusion: A nomogram including clinical features and contrast-enhanced MRI parameters was developed and was demonstrably effective at predicting VEGFR2 expression in HCC patients.
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