From metabolite mapping to precision imaging: advances in in vivo MR spectroscopy of brain tumours.
Abstract
Magnetic resonance spectroscopy (MRS) provides non-invasive in vivo assessment of brain tumour metabolism and complements conventional magnetic resonance imaging (MRI) by adding biochemical information beyond structural imaging. This narrative state-of-the-art review summarises current clinical and emerging applications of proton MRS and magnetic resonance spectroscopic imaging (MRSI) in brain tumours, with emphasis on metabolic biomarkers, technical developments, clinical translation, and current limitations. Established spectroscopic patterns, particularly elevated choline, reduced N-acetylaspartate, and variable lactate and lipid signals, support lesion characterisation, glioma grading, and differentiation of tumour recurrence from treatment-related effects. MRSI further enables spatial mapping of metabolic heterogeneity, helping to identify infiltrative and non-enhancing tumour regions relevant to biopsy targeting, surgical planning, and radiotherapy guidance. Molecularly informative metabolites, especially 2-hydroxyglutarate in isocitrate dehydrogenase-mutant gliomas, extend the role of MRS toward non-invasive molecular imaging, while emerging markers such as alanine and glycine remain promising but require further validation. Advances in localisation, acceleration, spectral editing, automated quantification, and artificial intelligence-assisted analysis have improved feasibility and reproducibility, although variability in acquisition protocols, quantification methods, quality control, and reporting continues to limit widespread clinical adoption. Hyperpolarised 13C and deuterium-based methods offer dynamic assessment of tumour metabolism, but largely remain translational or research-oriented techniques. Overall, MRS and MRSI are increasingly positioned as useful components of multiparametric neuro-oncology imaging, provided that future work continues to prioritise protocol harmonisation, multicentre validation, clinically actionable thresholds, and integration into routine diagnostic and treatment-planning workflows.