VASARI 2.0: a new updated MRI VASARI lexicon to predict grading and

Introduction: Precision medicine refers to managing brain tumors according to each patient's unique characteristics when it was realized that patients with the same type of tumor differ greatly in terms of survival, responsiveness to treatment, and toxicity of medication. Precision diagnostics can now be advanced through the establishment of imaging biomarkers, which necessitates quantitative image acquisition and processing. The VASARI (Visually AcceSAble Rembrandt Images) manual annotation methodology is an ideal and suitable way to determine the accurate association between genotype and imaging phenotype. Our work proposes an updated version of the VASARI score that is derived by changing the evaluation ranges of its components in an effort to increase the diagnostic accuracy of the VASARI manual annotation system and to find neuroimaging biomarkers in neuro-oncology with increasing reliability. Materials and methods: We gathered the histological grade and molecular status of 126 patients with glioma (Men/Women = 75/51; mean age: 55.30) by a retrospective analysis. Two residents and three neuroradiologists blindedly examined each patient using all 25 VASARI characteristics, after having appropriately modified the reference ranges in order to implement an innovative VASARI lexicon (VASARI 2.0). It was determined how well the observers agreed. A box plot and a bar plot were used in a statistical analysis to assess the distribution of the observations. After that, we ran a Wald test and univariate and multivariate logistic regressions. To find cutoff values that are predictive of a diagnosis, we also computed the odds ratios, confidence intervals, and evaluation matrices using receiver operating characteristic curves for each variable. Finally, we performed a Pearson correlation test to evaluate whether the variable grades and Results: An excellent Intraclass Correlation Coefficient (ICC) estimate was obtained. In this study, five features were part of the predictive model for determining glioma grade: F4, enhancement quality [area under the curve (AUC): 0.87]; F5, tumor-enhancing proportion (AUC: 0.70); F6, tumor-non-enhancing proportion (AUC: 0.89); F7, necrosis proportion (AUC: 0.79); and F17, diffusion characteristics (AUC: 0.75). Furthermore, six features were found to predict Discussion and conclusion: Glioma grade and isocitrate dehydrogenase (