Gliomas constitute the most common primary brain neoplasms in adults and comprise a diverse spectrum of tumors, histologically ranging from low-grade, indolent neoplasms to high-grade, aggressive lesions. Accurate, prospective grading of primary cerebral gliomas is critical for therapeutic planning and determining prognosis. Unfortunately, histopathologic grading of these lesions, either by stereotactic biopsy or surgery, is not without error; stereotactic biopsy is subject to sampling errors, and surgical tissue sampling cannot assess regions of residual tumor that were not removed during cytoreductive surgery.

Unlike surgical methods, MR imaging is not inherently limited by incomplete sampling and has the benefit of imaging the entire lesion. Conventional MR imaging does provide valuable information on several features suggestive of a high-grade, aggressive lesion, including breakdown of the blood-brain barrier and enhancement, central necrosis, hemorrhage, mass effect, and satellite lesions. Unfortunately, there can be significant overlap in the imaging appearance of the different glioma grades, with high-grade lesions occasionally lacking enhancement, prominent mass effect, or necrosis, and appearing similar to low-grade lesions. Similarly, low-grade lesions can sometimes demonstrate aggressive features, including enhancement, necrosis, or hemorrhage. Furthermore, conventional MR imaging does not provide reliable information on physiologic parameters central to tumor grading, including angiogenesis, metabolism, and microvascularity.

Unlike conventional MRI, perfusion MRI is not dependent on breakdown of the blood-brain barrier and can demonstrate important features essential in tumor grading, including microvascularity and angiogenesis. In this issue of the AJNR News Digest, we focus on the work of authors whose research has advanced the use of MR perfusion in the grading of primary gliomas.

Dynamic susceptibility contrast (DSC) perfusion imaging was the first perfusion imaging technique to show a reliable correlation between the relative cerebral blood volume (rCBV), tumor grade, and increased tumor vascularity on histology. However, reproducibility of rCBV measurements has been a concern, which is an issue of increasing importance, given

repeated measurements may be useful to assess response to therapy. Law et al found performing histogram analysis of the rCBV in the region of the tumor provided a reproducible method for quantifying rCBV, with a higher specificity for detecting high-grade gliomas than traditional region-of-interest methods.

Since the advent of DSC perfusion imaging, numerous other perfusion methods have been applied to the problem of imaging grading of cerebral gliomas. Noguchi et al utilized arterial spin-labeling perfusion imaging (ASL-PI), an MR imaging technique that uses electromagnetically labeled arterial blood water as a freely diffusible intrinsic tracer. They found ASL-PI may reflect the histopathologic vascularity of gliomas, and this may aid in distinguishing high- and low-grade lesions.

Lu et al applied a novel technique that uses the difference between the precontrast and postcontrast images to determine the cerebral blood volume and vascular permeability, termed vascular-space occupancy (VASO) MR imaging. They found that VASO imaging could distinguish between low-grade (WHO grade II) and higher grade (WHO grades III and IV) tumors with high sensitivity and specificity.

More recently, Federau et al addressed the problem of tumor grading using an MR technique that measures microvascular perfusion, termed intravoxel incoherent motion (IVIM) imaging. IVIM measures the incoherent motion in voxels that results from thermal diffusion when blood moves in the microvasculature. They demonstrated that a histogram of the IVIM perfusion fraction in high-grade lesions demonstrated a more platykurtotic curve than histograms in low-grade lesions.

Similarly, Jung et al utilized histogram analysis of dynamic contrast-enhanced (DCE) perfusion imaging to help determine glioma grade. DCE uses dynamic changes in signal intensity (signal-time curves) to calculate pharmacokinetic parameters, such as cerebral blood volume, cerebral blood flow, vascular permeability, and extravascular extracellular volume, which can be used to characterize gliomas. They found that several parameters, including vascular permeability, extravascular extracellular space volume, and blood plasma volume were helpful in determining glioma grade.

Given the numerous types of perfusion imaging that can be applied to grading gliomas, the question remains whether preoperative grading of gliomas offers a significant clinical benefit. Johnson et al sought to determine whether perfusion imaging has a diagnostic or therapeutic impact on clinical management of patients with gliomas. They found that perfusion imaging had both the ability to increase clinical confidence in a treatment plan or change management to a more appropriate treatment course. These findings, along with the increasing number of methods for measuring tumor perfusion, assure us that there will be an ever growing role for MR perfusion in the preoperative assessment of brain tumors.

 

Image modified from: Geer CP, Simonds J, Johnson AJ, et al. Does MR perfusion imaging impact management decisions for patients with brain tumors? A prospective study.