Diffusion Tensor Imaging (DTI) to Characterize Glial Tumor Progression to Malignant Glioma: A 3-T Study of a Cohort of Pretreatment Patients

• Main Authors: Perez, Augustus J., Huang, Hao
• Format: Conference Proceeding
• Language: English
• ISSN: 1531-5010; 1532-0065
• DOI: 10.1055/s-2011-1274284

Introduction: Diffusion tensor imaging (DTI) is an imaging modality that has come to the forefront of CNS tumor investigation due to its ability to follow the motion of water molecules and track neural fiber trajectory. The application of DTI to malignant tumors such as glioblastoma multiforme (GBM) includes exposition of pathological architecture, growth and invasion, and comparison of characteristics to lower-grade gliomas. Purpose/Aims: The aim of this study is to retrospectively determine whether full tensor analysis of 3-T DTI can longitudinally and spatially characterize low-grade, WHO grade III, and WHO grade IV gliomas and yield predictable patterns of development and progression to high-grade gliomas. Metrics used to parameterize these data will include fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD), in addition to primary Eigen vector mapping. Previous studies on rat models have demonstrated unique structural patterns in and around these tumors, and we speculate based on early analysis that such patterns may exist in human subjects. Methods: Review board approval was obtained and informed consent was waived. For this study, DTI will be performed on a population of at least 16 patients with histologically or clinically proven gliomas and who have had no prior treatment. The various metrics will be compared between tumor regions defined by a semiautomated program that uniformly segments each tumor based on investigator-defined regions of interest. Statistical analysis will be performed to compare the obtained metric values between tumor types and between tumor regions. Conclusions: Such information can be instrumental to improving clinical tumor grading accuracy in addition to improving tumor boundary definition during neurosurgical planning and implementation, leading to better-tailored treatment strategies.