Pamela Schaefer

During the last decade we have seen marked progress in brain tumor imaging. With the development of advanced MRI techniques, we have moved from anatomic to physiologic imaging of brain tumors. For example, perfusion MRI has allowed determination of tumor grade and differentiation of recurrent tumor from necrosis with elevated cerebral blood volume correlating with neovascularity. MR spectroscopy has allowed establishment of metabolite patterns that differentiate high- from low-grade gliomas and recurrent tumor from necrosis. Diffusion imaging, with restricted diffusion correlating with increased cellularity, has also allowed the differentiation of high- from low-grade tumors.

In the last few years, we have seen even more exciting advances in brain tumor imaging as more sophisticated histologic analysis is being developed and the genetics of primary glial neoplasms are being determined at a rapid rate. Many institutions have the ability to quantify microvessel density and area and test for genetic markers such as MGMT methylation, IDH1 mutation and EGFR amplification.

In this third issue of AJNR News Digest we highlight the original publications of seven authors who are defining the relationship between brain tumor genetics, advanced histologic analysis, anatomic imaging,

physiologic imaging, response to treatment, and survival.

Two articles address the relation between glioblastoma location, genetic markers, age, conventional imaging findings, and survival prognosis. Three articles highlight relationships between perfusion MR imaging parameters, microvessel quantification, molecular/genetic angiogenic markers, and survival. Two articles assess the relationship between diffusion characteristics, genetic markers, and tumor biology. One article highlights the correlation between PET imaging, tumor grade, and gene expression. One article highlights the use of spectroscopy to assess the metabolic response of glioblastoma to intra-arterial bevacizumab infusion. Finally, one article introduces the concept of data mining of multiple anatomic, clinical, and physiologic variables to predict survival in patients with glioblastoma.

The authors, in their own words, describe their original research and its potential  impact on the assessment and treatment of brain tumors. The full articles can be downloaded at AJNR.org.

Image modified from: Ellingson BM, Lai A, Harris RJ, et al. Probabilistic Radiographic Atlas of Glioblastoma Phenotypes.