VASARI 2.0: a new updated MRI VASARI lexicon to predict grading and status in brain glioma.

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 were correlated.

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 mutation status: F4, enhancement quality (AUC: 0.904); F5, tumor-enhancing proportion (AUC: 0.73); F6, tumor-non-enhancing proportion (AUC: 0.91); F7, necrosis proportion (AUC: 0.84); F14, proportion of edema (AUC: 0.75); and diffusion characteristics F17 (AUC: 0.79). VASARI 2.0 models showed good performances according to the AUC values, which are also compared with traditional VASARI scores.

Discussion And Conclusion: Glioma grade and isocitrate dehydrogenase () status can be predicted using specific magnetic resonance imaging (MRI) features, which have significant prognostic consequences. The accuracy of texture-derived metrics from preoperative MRI gliomas and machine learning analysis for predicting grade, status, and their correlation can be enhanced by the suggested new and updated VASARI manual annotation system. To help with therapy selection and enhance patient care, we intend to create prediction models that incorporate these MRI findings with additional clinical data.