Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging

Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats

BACKGROUND

This study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).

METHODS

Eighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used.

RESULTS

At day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035).

CONCLUSION

Tensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury.

RELEVANCE STATEMENT

Tensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI.

KEY POINTS

The alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury. Changing q values were observed in the impact site and remote WM. The lower q values in the remote WM were associated with astrogliosis.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced 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.

Abstract

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.

Increased Tau Expression Correlates With IDH Mutation in Infiltrating Gliomas and Impairs Cell Migration

Although the microtubule-associated protein tau is well studied in human neurodegeneration, the role of tau in neoplastic brain diseases is not well understood. Recently, studies have shown tau mRNA expression is associated with improved survival in human infiltrating gliomas. However, the biologic basis of this association is largely unexplored. Using 2 independent publicly available mRNA databases, we show that high tau mRNA expression is associated with improved patient survival in infiltrating gliomas. Higher tau protein expression is also associated with improved patient prognosis in infiltrating gliomas by immunohistochemical staining of tissue microarrays. This prognostic association is in part due to higher tau mRNA and protein expression in IDH-mutant infiltrating astrocytomas. Expression of tau in an IDH-wildtype glioblastoma cell line selectively impairs cell migration in assays designed to mimic tumor invasion. These findings suggest that tau expression is not only associated with IDH mutation status but also may contribute to improved patient outcomes by impairing tumor invasion.

Emerging Techniques in Imaging of Glioma Microenvironment

Magnetic resonance imaging (MRI) has been the cornerstone of imaging of brain tumors in the past 4 decades. Conventional MRI remains the workhorse for neuro-oncologic imaging, not only for basic information such as location, extent, and navigation but also able to provide information regarding proliferation and infiltration, angiogenesis, hemorrhage, and more. More sophisticated MRI sequences have extended the ability to assess and quantify these features; for example, permeability and perfusion acquisitions can assess blood-brain barrier disruption and angiogenesis, diffusion techniques can assess cellularity and infiltration, and spectroscopy can address metabolism. Techniques such as fMRI and diffusion fiber tracking can be helpful in diagnostic planning for resection and radiation therapy, and more sophisticated iterations of these techniques can extend our understanding of neurocognitive effects of these tumors and associated treatment responses and effects. More recently, MRI has been used to go beyond such morphological, physiological, and functional characteristics to assess the tumor microenvironment. The current review highlights multiple recent and emerging approaches in MRI to characterize the tumor microenvironment.