<p dir="ltr">Human brain tumors present significant challenges due to their heterogeneous nature, known as intra-tumoral heterogeneity (ITH), which evolves over space and time, leading to treatment resistance and poor patient outcomes. Current diagnostic methods rely on pre-surgical imaging and single biopsy samples, providing only a partial understanding of the tumor microenvironment (TME) and often resulting in incomplete targeting of tumor mutations, leaving residual disease vulnerable to recurrence. Our hypothesis proposes a novel approach: utilizing multimodal and multiparametric <i>in vivo</i> imaging to map the cellular and molecular characteristics of the TME. By correlating imaging signatures with underlying somatic and genomic aberrations, we aim to develop a predictive model guiding personalized targeted therapies to effectively address the heterogeneity of brain tumors.</p><p dir="ltr">To achieve this goal, we designed, tested, and validated a predictive model through a pilot study using clinical MRI scans and one stereotactic biopsy sample. Subsequently, we optimized a multimodal and multiparametric imaging protocol including MRI and PET scans, to acquire comprehensive morphological, functional, and molecular data from the TME. Additionally, we established a detailed pipeline for subject recruitment, data collection, and post-processing to ensure the robustness and reliability of our model.</p><p dir="ltr">This innovative approach has the potential to overcome the limitations of current diagnostic methods by providing a comprehensive understanding of the TME using minimally-invasive imaging techniques. By correlating imaging data with ground truth pathology and genomics, this model will enhance brain tumor diagnosis and facilitate the implementation of targeted therapies, ultimately improving treatment response and patient outcomes.</p>