Integration of preoperative anatomic and metabolic physiologic imaging of newly diagnosed glioma

Abstract

To integrate standard anatomic magnetic resonance imaging in conjunction with uniformly acquired physiologic imaging biomarkers of untreated glioma with different histological grades with the goal of generating an algorithm that can be applied for patient management. A total of 143 patients with previously untreated glioma were scanned immediately before surgical resection using conventional anatomical MR imaging, and with uniform acquisition of perfusion-weighted imaging, diffusion-weighted imaging, and proton MR spectroscopic imaging. Regions of interest corresponding to anatomic and metabolic lesions were identified to assess tumor burden. MR parameters that had been found to be predictive of survival in patients with grade IV glioma were evaluated as a function of tumor grade and histological sub-type. Based on these finding both anatomic and physiologic imaging parameters were then integrated to generate an algorithm for management of patients with newly diagnosed presumed glioma. Histological analysis indicated that the population comprised 56 patients with grade II, 31 with grade III, and 56 with grade IV glioma. Based on standard anatomic imaging, the presence of hypointense necrotic regions in post-Gadolinium T1-weighted images and the percentage of the T2 hyperintense lesion that was either enhancing or necrotic were effective in identifying patients with grade IV glioma. The individual parameters of diffusion and perfusion parameters were significantly different for patients with grade II astrocytoma versus oligodendroglioma sub-types. All tumors had regions with elevated choline to N-acetylasparate index (CNI). Lactate was higher for grade III and grade IV glioma and lipid was significantly elevated for grade IV glioma. These results were integrated into a proposed management algorithm for newly diagnosed glioma that will need to be prospectively tested in future studies. Metabolic and physiologic imaging characteristics provide information about tumor heterogeneity that may be important for assisting the surgeon to ensure acquisition of representative histology. Correlation of these integrated MR parameters with clinical features will need to be assessed with respect to their role in predicting outcome and stratifying patients into risk groups for clinical trials. Future studies will use image directed tissue sampling to confirm the biological interpretation of these parameters and to assess how they change in response to therapy.