Alberto Negro ^1* Laura Gemini ¹ Mario Tortora ² Gianvito Pace ¹ Raffaele Iaccarino ¹ Mario Marchese ³ Andrea Elefante ² Fabio Tortora ² Vincenzo D'Agostino ¹

• ¹ Ospedale del Mare, Centro Sanitario Locale Napoli 1 Centro, Napoli, Italy
• ² Federico II University Hospital, Naples, Campania, Italy
• ³ Department of Medicine and Health Sciences Vincenzo Tiberio, University of Molise, Campobasso, Italy

In Neuro-Oncology precision medicine refers to managing brain tumors according to each patient's unique characteristics. Precision diagnostics can now be advanced through the establishment of imaging biomarkers. The VASARI 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 in an effort to increase its diagnostic accuracy. We gathered the histological grade and molecular status of 126 glioma patients (M/F = 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 cut-off values that are predictive of a diagnosis, we also computed the odds ratios, confidence intervals, and evaluation matrices using receiver operating characteristic (ROC) curves for each variable.Finally, we performed a Pearson correlation test to evaluate if the variables grade and IDH were correlated.An excellent ICC estimate was obtained. In this study, five features were part of the predictive model for determining glioma grade: F4, enhancement quality (AUC: 0.87), F5, tumor enhancing proportion (AUC: 0.70), F6, tumor no enhancing proportion (AUC: 0.89), F7, necrosis proportion (AUC: 0.79), F17, diffusion characteristics (AUC: 0.75). Furthermore, six features were found to predict IDH mutation status: F4, enhancement quality (AUC: 0.904); F5, tumor enhancing proportion (AUC: 0.73); F6, tumor no enhancing proportion (AUC: 0.91); F7, necrosis proportion (AUC: 0.84); F14, proportion of edema (AUC:0.75); diffusion characteristics F17 (AUC: 0.79). VASARI 2.0 models based showed good performances according to the area under the curve (AUC) values, also compared with traditional VASARI scores.Glioma grade and IDH status can be predicted using specific MRI features, which has significant prognostic consequences.