Radiogenomics and Machine Learning Predict Oncogenic Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. Despite standard therapies, the survival rate remains low, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in assessing GBM. Disruptions in various oncogenic signalling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signalling, Phosphoinositide 3- Kinases (PI3Ks), tumour protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumour types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients. Data from MRI scans and signaling pathways were collected, radiomic features were extracted, and ML models were trained and evaluated using cross-validation techniques. Our results showed a positive association between most signalling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes. We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning (ML) models. This research contributes to the advancement of precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to better understand tumor behavior and treatment response.