4.4 Article

MRI analysis to map interstitial flow in the brain tumor microenvironment

期刊

APL BIOENGINEERING
卷 2, 期 3, 页码 -

出版社

AIP Publishing
DOI: 10.1063/1.5023503

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资金

  1. NIH [1R01CA222563-01]
  2. NSF-GFRP
  3. National Cancer Institute of the National Institutes of Health [P30CA033572]
  4. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant [753878]
  5. [ACS-IRG-81-001-29]
  6. Marie Curie Actions (MSCA) [753878] Funding Source: Marie Curie Actions (MSCA)

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Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to non-invasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom tumor system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI. (C) 2018 Author(s).

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