ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019
Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction.AJNR. American journal of neuroradiology · 2023Pooled it
- MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.Medical physics · 2026Article
- Subarachnoid hemorrhage: epidemiology, risk factors, pathogenesis, and clinical therapies.Molecular biomedicine · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Review
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype.Journal of neuro-oncology · 2024Article
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study.Journal for immunotherapy of cancer · 2023Article
- An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.Brain : a journal of neurology · 2022Article
- A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone.Frontiers in oncology · 2022Article
- Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges.Frontiers in oncology · 2022Review
- A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature.Frontiers in oncology · 2022Article
- Challenges and opportunities for artificial intelligence in oncological imaging.Clinical radiology · 2021Review
- Review
- Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.Frontiers in oncology · 2021Article
- Immune-Related Gene SERPINE1 Is a Novel Biomarker for Diffuse Lower-Grade GliomasFrontiers in oncology · 2021Article
- Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery.Frontiers in oncology · 2020Article
- Role of molecular biomarkers in glioma resection: a systematic review.Chinese neurosurgical journal · 2020Review
- Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma.Journal of computer assisted tomographyArticle
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Authors and funding
10 authors.
Funding
Abstract
objectiveTo predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. MATERIALS AND
methodsPreoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed.
resultsNine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups.
conclusionsRadiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.
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