Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Interstitial Fluid Flow and Transport in Glioblastoma and Surrounding Parenchyma in Patients

Version 1 : Received: 28 December 2020 / Approved: 31 December 2020 / Online: 31 December 2020 (11:36:28 CET)

How to cite: Chatterjee, K.; Atay, N.; Abler, D.; Bhargava, S.; Sahoo, P.; Rockne, R.; Munson, J. Interstitial Fluid Flow and Transport in Glioblastoma and Surrounding Parenchyma in Patients. Preprints 2020, 2020120789 (doi: 10.20944/preprints202012.0789.v1). Chatterjee, K.; Atay, N.; Abler, D.; Bhargava, S.; Sahoo, P.; Rockne, R.; Munson, J. Interstitial Fluid Flow and Transport in Glioblastoma and Surrounding Parenchyma in Patients. Preprints 2020, 2020120789 (doi: 10.20944/preprints202012.0789.v1).

Abstract

Background: Glioblastoma is the deadliest, yet most common, brain tumor in adults, with poor survival and response to aggressive therapy. Therapeutic failure results from a number of causes inherent to these tumors. Imaging, computational, and drug delivery approaches can aid in the quest to access and kill each tumor cell in patients. One factor, interstitial fluid flow, is a driving force therapeutic delivery. However, convective and diffusive transport mechanisms are un-der-studied. In this study, we examine the application of a novel image analysis method to meas-ure fluid flow and diffusion in glioblastoma patients with MRI and compare to patient outcomes. Methods: Building on a prior imaging methodology tested and validated in vitro, in silico and in preclinical models of disease, here we apply our analysis method to archival patient data from the Ivy GAP dataset. Results: We characterize interstitial fluid flow and diffusion patterns in patients. We find strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Looking at overall magnitudes, there is significant correlation with both age and survival in this patient cohort. Additionally, we find that tumor size nor resection significantly alter the velocity magnitude. Last, we map the flow pathways in patient tumors and find variability in degree of directionality that we hypothesize in future studies may lead to information concerning treatment, invasive spread, and progression. Conclusions: Analysis of standard DCE-MRI in patients with glioblastoma offers more infor-mation regarding transport within and around tumor, can be measured post-resection and mag-nitudes correlate with patient prognosis.

Subject Areas

glioblastoma; DCE-MRI; interstitial flow; convection; diffusion; The Cancer Imaging Archive

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