Insight into the metabolism of gliomas by MRI and MRS

Gliomas are biologically and metabolically heterogeneous brain tumors whose clinical behavior is strongly influenced by lineage-defining genomic alterations such as isocitrate dehydrogenase (IDH1/2) mutation, 1p/19q-codeletion, ATRX loss, TERT promoter mutation, etc. Conventional magnetic resonance imaging (MRI) and tumor tissue biopsy are the clinical standards for diagnosis, tumor grading, and treatment planning. The conventional MRI/ magnetic resonance spectroscopy (MRS) identifies structural and vascular changes, and easy-to-measure high concentration metabolites. However, conventional MRS method suffers from spectral overlap and fails to provide a pure resonance, thus, it has limited application for estimation of difficult-to-identify and clinically relevant metabolic pool and flux changes in the tumors. Metabolic imaging using in vivo molecule tailored MRS provides a quantitative, highly precise non-invasive approach to measure tumor biochemistry in real time for ascertaining clinically relevant signatures for tumor diagnostication and prognostication. Recent developments in spectroscopic editing methods have provided quantitative MRS approaches to measure challenging metabolites such as glycine (Gly), glutamine (Gln), glutamate (Glu), 2-hydroxyglutarate (2HG), cystathionine and glutathione (GSH), in addition to routine MRS based measurements of metabolites, such as N-acetylaspartate (NAA), choline (Cho), creatine (Cr), lactate (Lac), etc. The pool of 2HG reflects epigenetic and redox remodeling in IDH-mutant tumors, whereas elevated glycine is associated with increased nucleotide demand and proliferative activity. Furthermore, molecule tailored MRS provide insights into cysteine-centered transsulfuration, antioxidant reprogramming and GSH-mediated radioresistance. Notably, these metabolic alterations often precede visible changes seen on anatomical MRI. Thus, metabolic profiling using molecule tailored MRS helps to find early tumor biomarkers for better diagnosis, along with the MRI. Future directions should include standardizing MRS protocols across imaging platforms, integrating metabolic markers with radio-genomics and machine-learning frameworks. Further, multi-center clinical trials and incorporating metabolic endpoints are necessary for adaptive therapy strategies. Together, MRI and MRS provide a comprehensive view of glioma biology that supports precision diagnosis, risk stratification, and individualized treatment planning, thereby advancing the goal of routine clinical adoption of metabolic imaging in neuro-oncology.