| Literature DB >> 32190946 |
Fabrício Guimarães Gonçalves1, Sanjeev Chawla2, Suyash Mohan2.
Abstract
Glioblastoma is the most common and most malignant primary brain tumor. Despite aggressive multimodal treatment, its prognosis remains poor. Even with continuous developments in MRI, which has provided us with newer insights into the diagnosis and understanding of tumor biology, response assessment in the posttherapy setting remains challenging. We believe that the integration of additional information from advanced neuroimaging techniques can further improve the diagnostic accuracy of conventional MRI. In this article, we review the utility of advanced neuroimaging techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, proton magnetic resonance spectroscopy, and chemical exchange saturation transfer in characterizing and evaluating treatment response in patients with glioblastoma. We will also discuss the existing challenges and limitations of using these techniques in clinical settings and possible solutions to avoiding pitfalls in study design, data acquisition, and analysis for future studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:978-997.Entities:
Keywords: arterial spin labeling; artificial intelligence; chemical exchange saturation transfer; diffusion weighted imaging; dynamic contrast enhancement; dynamic susceptibility contrast; glioblastoma; perfusion weighted imaging; proton MR spectroscopy
Mesh:
Year: 2020 PMID: 32190946 PMCID: PMC7492394 DOI: 10.1002/jmri.27105
Source DB: PubMed Journal: J Magn Reson Imaging ISSN: 1053-1807 Impact factor: 4.813