Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Abstract

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

“…However, these differences were minimal (GL261 Red-FLuc: 17.7 ± 16.3 mm 3 vs. 19.0 ± 17.0 mm 3 ; TRP-mCF: 64.4 ± 19.0 mm 3 vs. 64.4 ± 19.1 mm 3 for manual and AI calculations, respectively) and could be attributed to factors such as the small sample size ( n = 7–8 tumors) or limitations of AI in accurately discerning necrotic regions, which may resemble glassy backgrounds or dark brain regions with a high cellular density resembling the tumor tissue. Furthermore, calculated tumor volumes differed significantly when determined by histopathology and MRI; however, this discrepancy is consistent with previous studies comparing brain tumor volumes using these methods [ 17 , 49 , 50 ]. These differences could be due to various factors, including resolution differences, tissue processing with either shrinkage artifacts, or delicate tumor fragments prone to detachment, tumor edema, or imaging artifacts.…”

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

“…However, these differences were minimal (GL261 Red-FLuc: 17.7±16.3 mm3 vs. 19.0±17.0 mm3; TRP-mCF: 64.4±19.0 mm3 vs. 64.4±19.1 mm3 for manual and AI calculation, respectively) and could be attributed to factors such as the small sample size (n=7-8 tumors) or limitations of AI in accurately discerning necrotic regions, which may resemble glassy backgrounds or dark brain regions with high cellular density resembling tumor tissue. Furthermore, calculated tumor volumes differed significantly when determined by histopathology and MRI; however, this discrepancy is consistent with previous studies comparing brain tumor volumes using these methods [17,40,41]. These differences could be due to various factors, including resolution differences, tissue processing with either shrinkage artifacts or delicate tumor fragments prone to detachment, tumor edema, or imaging artifacts.…”

DC vaccines loaded with glioma cells killed by photodynamic therapy induce Th17 anti-tumor immunity and provide a four-gene signature for glioma prognosis

“…To assess the dynamics of intracranial tumor growth in the therapeutic model, ex vivo MRI was performed on 7-T micro-MRI (PharmaScan 70/16, Bruker BioSpin, Ettlingen, Germany) as previous described [67].…”