Stereotaxic Implantation of F98 Cells in Fischer Rats: A Syngeneic Model to Investigate Photodynamic Therapy Response in Glioma

An improved F98 glioblastoma rat model to evaluate novel treatment strategies incorporating the standard of care

Glioblastoma (GB) is the most common and malignant primary brain tumor in adults with a median survival of 12-15 months. The F98 Fischer rat model is one of the most frequently used animal models for GB studies. However, suboptimal inoculation leads to extra-axial and extracranial tumor formations, affecting its translational value. We aim to improve the F98 rat model by incorporating MRI-guided (hypo)fractionated radiotherapy (3 x 9 Gy) and concomitant temozolomide chemotherapy, mimicking the current standard of care. To minimize undesired tumor growth, we reduced the number of inoculated cells (starting from 20 000 to 500 F98 cells), slowed the withdrawal of the syringe post-inoculation, and irradiated the inoculation track separately. Our results reveal that reducing the number of F98 GB cells correlates with a diminished risk of extra-axial and extracranial tumor growth. However, this introduces higher variability in days until GB confirmation and uniformity in GB growth. To strike a balance, the model inoculated with 5000 F98 cells displayed the best results and was chosen as the most favorable. In conclusion, our improved model offers enhanced translational potential, paving the way for more accurate and reliable assessments of novel adjuvant therapeutic approaches for GB.

Perfusion-weighted software written in Python for DSC-MRI analysis

Introduction

Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.

Methods

The DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood-brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.

Results

A total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland-Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.

Conclusion

An open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.