P04.90 Observational prospective multi-centric study of delta radiomics Features Analysis for the prediction of patients outcomes in GLIoblastoma multifome: GLI.F.A. Project. (19th September 2018) - Lupattelli, M; Chiesa, S; Beghella Bartoli, F; Palumbo, I; Gatta, R; Tarducci, R; Dinapoli, N; Balducci, M; Aristei, C; Valentini, V
Abstract: Background: Despite decades of research, glioblastoma (GBM) remains the most malignant primary brain tumor. Different investigations have focused on the GBM's heterogeneous features to develop an individualized patient management. A multi-institutional study, the GLI.F.A. (Glioblastoma: advanced Imaging Features Analysis) Project, was performed for a comprehensive analysis of GBM heterogeneity in order to create a multidimensional map for predictive models (PM) and decision support systems (DSS) in GBM. Material and Methods: Adult patient with newly diagnosed GBM, that undergo to surgery and chemo-radiotherapy according to EORTC 26981-22981-NCIC trial, were analyzed in this first phase of the study. Gross Tumor Volume (GTV) was contoured in the T1 post contrast and T2-FLAIR weighted images. A brain ontology and a platform for sharing and combining multiple datasets (SPIDER BOA System for Patient Individual Data Entry and Recording Beyond Ontology Awareness) were created in order to standardize data collection. Preoperative MRI features were extracted by the MODDICOM software. Wilcoxon Mann Whitney test, Log-rank test for Kaplan-Meier curves were utilized to evaluate the significance of the radiomic features on the T2-Flair and T1 images. The main outcomes we considered were overall survival, progression free survival and response to radio-chemotherapy (RTCT). The median value of the features was used to categorize the continue variables. Results: Twenty-seven patients, treated from July 2014 to February 2018, were enrolled in this study. Median age was 61 years (range 45–75) and 20 patients are still alive. The MODDICOM software analyzed 94 image features. Significant features divided by MRI sequence and outcomes are reported in Tab1. The first order features, describing the statistical characteristics of images, resulted significant for OS on the T2-weighted MRI and for response to RTCT on the T1 weighted MRI; the second order features, describing the spatial correlation between images voxels, showed the most significant result for OS and PFS on contrast-enhanced T1. Conclusion: This preliminary univariate analysis suggests that the radiomic features relates to survival and clinical outcomes and that is possible to stratify patients according MR based quantitative imaging. A higher number of patients, multivariate analysis and external validation are next steps for getting reliable predictive models.