Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.

Abstract

Glioblastoma (GB), the most aggressive primary brain tumor, is characterized by profound inter- and intratumoral heterogeneity and a highly immunosuppressive tumor microenvironment (TME), both of which contribute to its poor prognosis and resistance to conventional therapies. The dynamic interplay between malignant cells and diverse TME constituents including immune cells, neural elements, and extracellular matrix components drives tumor progression, clonal evolution, and therapeutic failure. Traditional treatment modalities such as surgery, radiotherapy, and chemotherapy often fall short due to their inability to address the spatial and temporal complexity of the TME. Recent advances in artificial intelligence (AI) and cellular MRI profiling offer promising avenues for decoding the GB microenvironment at unprecedented resolution. By integrating AI-driven analysis of cellular MRI signatures, researchers can identify distinct microenvironmental niches and resistant subclones, enabling the development of targeted therapies that simultaneously disrupt tumor cells and their supportive ecosystems. This approach holds potential to overcome current therapeutic limitations and pave the way for personalized, microenvironment-informed interventions in GB management.