Abstract
Simple SummaryAdvanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
Highlights
DTI is a diffusion MRI (dMRI) technique that evaluates the orientation of water diffusion in biologic tissues as a result of constraints represented by oriented cell membranes and myelin [15], and it provides a number of metrics, including p, q, fractional anisotropy (FA), and mean diffusivity (MD)
In the current neuro-oncology scenario, tumor classifications are constantly updated in order to match the latest pieces of evidence regarding the different prognosis and treatment response of tumor entities. aMRI quantitative analyses are a noninvasive source of numerous in vivo biomarkers providing unprecedented insights regarding neoplastic tissue biology and pathophysiology, including tumor microstructure, microvasculature, metabolism, and electrolyte homeostasis
Several studies have assessed the reliability of hand-drawn region of interest (ROI) to sample tumoral and peritumoral aMRI-metrics in order to orient the diagnosis toward a specific molecular type
Summary
PWI includes three main techniques (dynamic susceptibility contrast—DSC, dynamic contrast-enhanced—DCE, and arterial spin labeling—ASL) that are variously based on exogenous (DSC, DCE) or endogenous (ASL) tracers, and that provide insightful metrics reflecting tissue perfusion/permeability: blood volume (DSC-cerebral blood volume (CBV)), blood flow (DSCand ASL-cerebral blood flow (CBF)), intravascular tracer (DCE-Vp ), and parameters describing the tracer exchange between intravascular and extracellular space While dMRI and PWI infer tumor pathophysiological features by assessing microstructural and perfusion/permeability tissue properties, novel magnetic resonance spectroscopy (MRS) approaches enable the detection of specific diagnostic molecules, as in the case of 2-hydroxyglutarate (2HG), which accumulates in brain tumors as a result of IDH-mutation [19], representing the first instance of MRI directly measuring a mutation marker in brain tumors. In the first part of this review, we discuss the diagnostic performance of quantitative data derived from PWI, ADC, and MRS in the molecular profiling of brain tumors, including recently defined peculiar tumor entities. We conclude by discussing the clinical applicability and future directions
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