Ricerc@Sapienza

Background: Glioblastoma Multiforme (GBM) is the most lethal primary brain tumor of the adult, with dismissal overall survival. Patient genetics play a crucial role in planning therapeutic intervention, with demonstrated effects on survival. This includes well-known biomarkers such as MGMT or IDH, but also GLUT1 and 3, which may be targeted by future therapies to restore a more normal metabolism in GBM. The gold-standard procedure for histologic and genetic diagnosis of GBM is pathological sampling through brain biopsy or surgery. Along with the risk for complications, high costs and misinterpretation rate, biopsy-based methods may face incomplete sampling due to spatial heterogeneity of GBMs. Artificial intelligence may help overcoming these drawbacks, by providing non-invasive reliable and reproducible predictive models for patient outcome.

Aim: Our aim is to build an artificial intelligence model predictive for the diagnosis of GBM, molecular type and a comprehensive panel of genetic alterations, including MGMT promoter methylation, IDH mutation, EGFR amplification, P53 mutation, Ki-67 status, ATRX loss and GLUT 1/3 expression. We intend to build this model from MR data and digitalized histology slides, by employing machine learning and deep learning techniques.

Methods: We will extract radiomic features from three volumes of interest MR images of GBM: enhancing tumor, non-enhancing tumor and necrosis. We will extract deep features from digitalized histology slides by means of a convoluted neural network. After feature selection, data will be fed to multiple machine learning classifiers to achieve best performance.