ArticleFrontiers in oncology2023
The combination of radiomics features and VASARI standard to predict glioma grade.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance.AJNR. American journal of neuroradiology · 2024Pooled it
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Development and validation of radiopathomics models for predicting molecular subtypes and WHO grades in adult-type diffuse gliomas: a multicenter study.Journal of translational medicine · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Histopathological grades as prognostic factors of H3 K27-altered diffuse midline glioma and their prediction using multiparametric MR imaging.Neuroradiology · 2025Article
- Nomogram prediction of molecular characteristics in WHO grade 3-4 diffuse gliomas based on fractal analysis and VASARI features.Scientific reports · 2025Article
- Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors.AJNR. American journal of neuroradiology · 2024Article
- Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors.The neuroradiology journal · 2024Article
- Article
- Beyond invasive biopsies: using VASARI MRI features to predict grade and molecular parameters in gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- VASARI 2.0: a new updated MRI VASARI lexicon to predict grading andFrontiers in oncology · 2024Article
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- Predictive machine learning models based on VASARI features for WHO grading, isocitrate dehydrogenase mutation, and 1p19q co-deletion status: a multicenter study.American journal of cancer research · 2024Article
- Assessment of Ki-67 expression levels in IDH-wildtype glioblastoma using logistic regression modelling of VASARI features.Neurosurgical review · 2023Article
- Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis.Radiologia brasileiraArticle
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.
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