Role of Advanced MR Imaging in Diagnosis of Neurological Malignancies: Current Status and Future Perspective

Lesions of the central nervous system (CNS) can present with numerous and overlapping radiographical and clinical features that make diagnosis difficult based exclusively on history, physical examination, and traditional imaging modalities. Given that there are significant differences in optimal treatment protocols for these various CNS lesions, rapid and non-invasive diagnosis could lead to improved patient care. Recently, various advanced magnetic resonance imaging (MRI) techniques showed promising methods to differentiate between various tumors and lesions that conventional MRI cannot define by comparing their physiologic characteristics, such as vascularity, permeability, oxygenation, and metabolism. These advanced MRI techniques include dynamic susceptibility contrast MRI (DSC), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) MRI, Golden-Angle Radial Sparse Parallel imaging (GRASP), Blood oxygen level-dependent functional MRI (BOLD fMRI), and arterial spin labeling (ASL) MRI. In this article, a narrative review is used to discuss the current trends in advanced MRI techniques and potential future applications in identifying difficult-to-distinguish CNS lesions. Advanced MRI techniques were found to be promising non-invasive modalities to differentiate between paraganglioma, schwannoma, and meningioma. They are also considered promising methods to differentiate gliomas from lymphoma, post-radiation changes, pseudoprogression, demyelination, and metastasis. Advanced MRI techniques allow clinicians to take advantage of intrinsic biological differences in CNS lesions to better identify the etiology of these lesions, potentially leading to more effective patient care and a decrease in unnecessary invasive procedures. More clinical studies with larger sample sizes should be encouraged to assess the significance of each advanced MRI technique and the specificity and sensitivity of each radiologic parameter.