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This GitHub repository aims to provide an educational prototype for conducting radiogenomics research, particularly focusing on the interplay between imaging features and genomic profiles in glioblastoma patients. This repository includes a computational framework to integrate and analyze multimodal datasets comprising volumetric radiomics parameters (such as tumor volume, surface area, and intensity metrics extracted from MRI scans) and high-throughput RNA sequencing data. Inputs to this analytical pipeline include pre-processed, anonymized patient-specific datasets: one delineating quantitative imaging features indicative of tumor morphology and physiology and another detailing gene expression levels that reflect the underlying molecular landscape of the tumor. The integration of these datasets enables the investigation of correlations between radiographic characteristics and genomic alterations, facilitating the identification of radiogenomics signatures predictive of clinical outcomes. This pr prototype is an exemplar for leveraging computational methods to bridge radiology and genomics, providing insights into tumor biology and potentially guiding personalized therapeutic strategies.