Gliomas: An Advanced MRI Techniques for the Preoperative Diagnosis and Imaging.

Abstract

Background: Gliomas are primary brain tumors originating from glial cells and are classified based on histological and molecular markers. The WHO 2021 updated classification system has integrated genetic factors such as IDH mutations and 1p/19q co-deletion, refining glioma subtyping. Advanced magnetic resonance imaging (MRI) techniques have emerged as promising non-invasive tools for assessing glioma characteristics, including their molecular subtypes, without the need for biopsies.

Aim: This review aims to explore the application of advanced MRI techniques in preoperative glioma diagnosis, highlighting their role in characterizing tumor molecular subtypes and aiding clinical decision-making.

Methods: The review synthesizes recent advancements in MRI technologies, including perfusion imaging, diffusion MRI, and magnetic resonance spectroscopy (MRS). It also evaluates the validation and clinical applications of methods such as dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), and MR elastography (MRE), based on expert consensus and available literature.

Results: Various MRI modalities, including DSC-MRI for cerebral blood volume (rCBV) measurement and MR spectroscopy, have shown promise in non-invasively predicting glioma subtypes as per the 2021 WHO classification. These advanced techniques can enhance tumor characterization, helping to identify glioma grades and predict patient outcomes. However, the clinical integration of these methods faces challenges such as standardization, validation, and the need for specialized equipment and expertise.

Conclusion: Advanced MRI techniques offer significant potential for the preoperative assessment of gliomas. They can non-invasively predict molecular subtypes and tumor behavior, facilitating more precise treatment planning. However, widespread clinical adoption is hindered by the need for further validation, standardized protocols, and overcoming practical barriers related to equipment and operator expertise.