Advanced magnetic resonance imaging in glioblastoma: a review

Abstract

Glioblastoma, the most common and most rapidly progressing primary malignant tumor of the central nervous system, continues to portend a dismal prognosis, despite improvements in diagnostic and therapeutic strategies over the last 20 years. The standard of care radiographic characterization of glioblastoma is magnetic resonance imaging (MRI), which is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma. Basic MRI modalities available from any clinical scanner, including native T1-weighted (T1w) and contrast-enhanced (T1CE), T2-weighted (T2w), and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences, provide critical clinical information about various processes in the tumor environment. In the last decade, advanced MRI modalities are increasingly utilized to further characterize glioblastomas more comprehensively. These include multi-parametric MRI sequences, such as dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE), higher order diffusion techniques such as diffusion tensor imaging (DTI), and MR spectroscopy (MRS). Significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. Functional MRI (fMRI) and tractography are increasingly being used to identify eloquent cortices and important tracts to minimize postsurgical neuro-deficits. A contemporary review of the application of standard and advanced MRI in clinical neuro-oncologic practice is presented here.

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

“…Their approach leverages the integrated analysis of advanced multiparametric MRI (Bakas et al, 2016) and biophysical tumor growth modeling (Akbari et al, 2018). Their findings were corroborated by experiments in vitro and in vivo in animal models, contributing to the discovery of a potential molecular target and presenting an opportunity for potential therapeutic development (Shukla et al, 2017). Another study (Bakas et al, 2017a) found an imaging signature in radiology images of the most prevalent mutation of EGFR, namely, EGFRvIII, revealing a complex yet distinct macroscopic GBM radiographic phenotype.…”

“…Their experimental results show that threedimensional radiomic features computed from radiology images could be used to differentiate pseudo-progression from true cancer progression in GBM patients. Binder et al (Shukla et al, 2017) identified radiographic signatures of extracellular domain missense mutants (i.e., A289V) of the epidermal growth factor receptor (EGFR) suggestive of an invasive and proliferative phenotype, and associated with shorter patient survival. Their approach leverages the integrated analysis of advanced multiparametric MRI (Bakas et al, 2016) and biophysical tumor growth modeling (Akbari et al, 2018).…”

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

“…Among the just-mentioned sources of variability, segmentation approaches used for ROI delineation remained tricky considering that the T1-contrast enhancement sequence highlights more perfused tumoral regions, reducing the intratumoral heterogeneity sampling. On the other side, ROI outlining based on FLAIR sequences are affected by peritumoural oedema that can reduce the significance of the findings due to the inclusion of unaffected components in the analyses [ 19 , 85 ]. The variability in patient treatment is particularly noticeable in studies on R-GBM patients, and this is largely due to the lack of a standard of care and the limited efficacy of the therapeutic options, which also justify the larger presence of clinical trials among studies on R-GBM than those on ND-GBM [ 86 ].…”

Biomimetic Nanocomposites Cloaked with Bioorthogonally Labeled Glioblastoma Cell Membrane for Targeted Multimodal Imaging of Brain Tumors

“…Nonetheless, MRI is not only limited by the costly instrumentation and relatively long data acquisition process, but also unable to show the exact location and the extent of microscopic tumor infiltrates. [ 8 ] As such, the current practice of incomplete tumor excision results in the inevitable recurrence and thus low survival rate. For the ease of intraoperative microscopic identification and resection, surgeons have adopted fluorescence imaging (FI) to differentiate tumor tissues by color‐codes.…”