NIMG-78. DEVELOPMENT OF A PRECLINICAL GLIOBLASTOMA MURINE MODEL FOR THE ASSESSMENT OF RADIATION NECROSIS USING DIFFUSION KURTOSIS IMAGING

Abstract

Abstract Glioblastoma (GB) patients often present with either radiation-induced necrosis (RN) or develop tumor recurrence (TR). Radiologically, differentiating between these two disease states is particularly difficult, especially utilizing traditional MRI techniques. Accurately distinguishing these separate states is important, as failure to do so can ultimately lead to unnecessary surgical intervention or early cessation of appropriate radiation therapy. Currently, distinction between these pathologies is made using best clinical judgement by the neuro-oncology team. Therefore, there is a need for developing novel non-invasive techniques that can reliably distinguish between radiation necrosis and tumor recurrence. Primary data was collected utilizing orthotopically transplanted GL261 mouse GB cells in a C57BL/6 mouse. Tumor induction was verified by MRI after two weeks. RN and TR were initiated using an aggressive radiation dose fractionation (12 Gy or 60 Gy). This was completed using a 4 mm radiation cone such that one portion of the tumor received 100% dose of 12 Gy or 60 Gy fraction which is sufficient to cause RN, whereas the tumor edge received only 50% of the dose, allowing for tumor recurrence. Our data demonstrated mice tumor recurrence and radiation necrosis on MRI imaging. Axial T2-weighted MRI and DKI sequence at 2 weeks following implantation demonstrated edema compatible with tumor recurrence. One week later, the T2-weighted MRI and DKI sequences demonstrated continued tumor progression. In a separate mouse with orthotopic glioblastoma implantation with high dose (60 Gy) radiation treatment, T2 imaging and DKI demonstrated areas of tumor and suspected central radiation necrosis. DKI imaging biomarkers was compared by liquid biopsy, H&E staining, and Immunohistochemistry (IHC) analysis of sacrificed mice. Our H&E staining results showed typical pathological features of GB and RN, and TR in each case. This result demonstrated that our proposed model for radiation induction was effective at achieving its goal.