ArticleJournal of imaging2023
Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 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
- Visual versus region-of-interest based diffusion evaluation and their diagnostic impact in adult-type diffuse gliomas.Neuroradiology · 2026Article
- GL-Net: A knowledge-guided Gaussian-gated and layered refinement network for 3D MRI segmentation of brain gliomas.PloS one · 2026Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- IDO2-AhR axis as central regulator of the kynurenine pathway in glioblastoma.Journal of neuro-oncology · 2025Article
- Medical Digital Twin: A Review on Technical Principles and Clinical Applications.Journal of clinical medicine · 2025Review
- Association between dichotomized VASARI feature and overall survival in glioblastoma patients: a single-institution propensity score matching analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors.The neuroradiology journal · 2024Article
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients.BMC cancer · 2024Article
- Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review.Cancers · 2024Article
- Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma.Quantitative imaging in medicine and surgery · 2024Article
- Imaging biobanks: operational limits, medical-legal and ethical reflections.Frontiers in digital health · 2024Review
- 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
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- VASARI 2.0: a new updated MRI VASARI lexicon to predict grading andFrontiers in oncology · 2024Article
- Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas.Scientific reports · 2023Article
- Understanding highand low-grade gliomas: VASARI criteria and MRI features.Radiologia brasileiraArticle
- Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis.Radiologia brasileiraArticle
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.
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