Investigating Low-Velocity Fluid Flow in Tumors with Convection-MRI

Walker‐Samuel, Simon; Roberts, Thomas A.; Ramasawmy, Rajiv; Burrell, Jake S.; Johnson, Sean Peter; Siow, Bernard; Richardson, S.; Gonçalves, Miguel R.; Pendsé, Douglas; Robinson, Simon P.; Pedley, R. Barbara; Lythgoe, Mark F.

doi:10.1158/0008-5472.can-17-1546

Cited by 37 publications

(32 citation statements)

References 51 publications

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“…Measuring IFP and solid stress in itself represents a major clinical challenge, as current approaches for measuring both are invasive. Clinical measurement of IFP with invasive wick-in-needles only permits very discrete sampling of the tissue and is unreliable, and innovative preclinical MRI methods shown to correlate with IFP in vivo would be difficult to routinely implement in a clinical setting (51,55).…”

Section: Discussionmentioning

confidence: 99%

Investigating the Contribution of Collagen to the Tumor Biomechanical Phenotype with Noninvasive Magnetic Resonance Elastography

et al. 2019

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Increased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors in vivo and thus has clear clinical applications. Herein, we used MR elastography, coupled with computational histopathology, to interrogate the contribution of collagen to the tumor biomechanical phenotype and to evaluate its sensitivity to collagenase-induced stromal modulation. Elasticity (G d) and viscosity (G l) were significantly greater for orthotopic BT-474 (G d ¼ 5.9 AE 0.2 kPa, G l ¼ 4.7 AE 0.2 kPa, n ¼ 7) and luc-MDA-MB-231-LM2-4 (G d ¼ 7.9 AE 0.4 kPa, G l ¼ 6.0 AE 0.2 kPa, n ¼ 6) breast cancer xenografts, and luc-PANC1 (G d ¼ 6.9 AE 0.3 kPa, G l ¼ 6.2 AE 0.2 kPa, n ¼ 7) pancreatic cancer xenografts, compared with tumors associated with the nervous system, including GTML/Trp53 KI/KI medulloblastoma (G d ¼ 3.5 AE 0.2 kPa, G l ¼ 2.3 AE 0.2 kPa, n ¼ 7), orthotopic luc-D-212-MG (G d ¼ 3.5 AE 0.2 kPa, G l ¼ 2.3 AE 0.2 kPa, n ¼ 7), luc-RG2 (G d ¼ 3.5 AE 0.2 kPa, G l ¼ 2.3 AE 0.2 kPa, n ¼ 5), and luc-U-87-MG (G d ¼ 3.5 AE 0.2 kPa, G l ¼ 2.3 AE 0.2 kPa, n ¼ 8) glioblastoma xenografts, intracranially propagated luc-MDA-MB-231-LM2-4 (G d ¼ 3.7 AE 0.2 kPa, G l ¼ 2.2 AE 0.1 kPa, n ¼ 7) breast cancer xenografts, and Th-MYCN neuroblastomas (G d ¼ 3.5 AE 0.2 kPa, G l ¼ 2.3 AE 0.2 kPa, n ¼ 5). Positive correlations between both elasticity (r ¼ 0.72, P < 0.0001) and viscosity (r ¼ 0.78, P < 0.0001) were determined with collagen fraction, but not with cellular or vascular density. Treatment with collagenase significantly reduced G d (P ¼ 0.002) and G l (P ¼ 0.0006) in orthotopic breast tumors. Texture analysis of extracted images of picrosirius red staining revealed significant negative correlations of entropy with G d (r ¼ À0.69, P < 0.0001) and G l (r ¼ À0.76, P < 0.0001), and positive correlations of fractal dimension with G d (r ¼ 0.75, P < 0.0001) and G l (r ¼ 0.78, P < 0.0001). MR elastography can thus provide sensitive imaging biomarkers of tumor collagen deposition and its therapeutic modulation. Significance: MR elastography enables noninvasive detection of tumor stiffness and will aid in the development of ECM-targeting therapies.

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Section: Discussionmentioning

confidence: 99%

Investigating the Contribution of Collagen to the Tumor Biomechanical Phenotype with Noninvasive Magnetic Resonance Elastography

et al. 2019

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“…Current limitations include, for example, our stochastic approach to 624 simulating vascular blood flow which was implemented due to the in-availability of 625 experimental data. Recent in vivo methods provide a step forward in approximating conditions by discerning global tumour pressure gradients through observations of tissue 627 fluid velocity [26], which could lead to greater accuracy when assigning boundary 628 conditions specific to a tumour. Similarly, parameter values such as the vascular 629 conductance and interstitial hydraulic conductivity were assigned using previous 630 literature values since these tissue-specific measurements can be challenging to procure 631 through experimentation.…”

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confidence: 99%

“…For example, interstitial conductivity values across normal 632 tissue have been reported to span four orders of magnitude [17]. Future work could seek 633 to predict spatially heterogeneous maps of interstitial hydraulic conductivity using 634 REANIMATE and new experimental data [26]. 635 There are also opportunities to expand the computational model to incorporate more 636 complex biological phenomena.…”

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Modelling the transport of fluid through heterogeneous, whole tumours in silico

Sweeney

d’Esposito

Walker‐Samuel

et al. 2019

Preprint

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It is critically important to understand and predict fluid transport within both physiological and pathological tissues in order to develop effective treatment strategies.Recent advances in high-resolution optical imaging allow the acquisition of whole tumour vascular networks which can be used to parameterise computational models to predict the fluid dynamics at all length scales across the tissue. This enables hypothesis testing around the role of the tumour microenvironment in determining transport characteristics, which would otherwise be unavailable using traditional experiments.In this study, we present a novel computational framework for the efficient simulation of vascular blood flow and interstitial fluid transport based on complete three-dimensional, whole tumour vasculature obtained using high-resolution optical imaging. This framework comprises a Poiseuille flow model which simulates vascular blood flow within the vessel network, coupled via point sources of flux to a porous medium model describing interstitial fluid transport. We develop a computational algorithm for prescription of network boundary conditions and validation of tissue-scale fluid transport against measured in vivo perfusion data acquired using biomedical imaging tools. We present simulations of the model on orthoptic murine glioma and December 24, 2018 1/36 human colorectal carcinoma xenograft data (GL261 and LS147T, respectively), and perform sensitivity analysis on key unknown parameters relating to the tissue microenvironment, to understand their impact in predicting vascular and interstitial flow. Finally, we simulate radially varying vascular normalisation in a LS147T tumour and hypothesise that uniform normalisation is required to lower tumour interstitial fluid pressure.Our computational framework permits predictions of whole tumour fluid dynamics which incorporate the inherent architectural heterogeneities appearing at the micron-scale, and outputs three-dimensional spatial maps detailing these flow properties from micro to macro length scales. This provides vital information on the tumour microenvironment which could enable the design and delivery of future anti-cancer therapies. Author summaryThe structure of tumours varies widely, with dense and chaotically-formed networks of blood vessels that differ between each individual tumour and even between different regions of the same tumour. This atypical environment can inhibit the delivery of anti-cancer therapies. Computational tools are urgently required which incorporate micron-scale tumour biomechanics to predict tissue-scale fluid dynamics, and consequently the efficacy of cancer therapies.We have developed a computational framework which integrates the complex tumour vascular architecture to predict fluid transport across all lengths scales in whole tumours. This enables computationally efficient hypothesis testing of cancer therapies which manipulate the tumour microenvironment in order to improve drug delivery to tumours. Introduction 1 Architectural heterogeneities in...

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“…Soon after the first implementation of NMR velocity imaging in humans by Moran in 1982 [21], a number of clinical applications were found [49], including measuring cerebral [25,50] and aortic [23,24,51,52,53] blood flow as well as cerebrospinal fluid (CSF) pulsation [54,55,56]. One exception was a study of slow velocity fluid flow in tumors by Walker-Samuel et al [57]. They noted that the velocity encoding parameter v enc (discussed below) needed to be larger than predicted to avoid "crushing" the signal.…”

Section: Introduction To Phase Contrast Velocimetrymentioning

confidence: 99%

Limits to flow detection in phase contrast MRI

Williamson

Komlosh

Benjamini

et al. 2020

Preprint

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Velocity detection with single pulsed gradient spin echo (PGSE) NMR is known to be limited by diffusion smearing the phase coherence and obscuring dispersive attenuation. We present an equation for the lowest detectable velocity with phase contrast velocimetry which incorporates the signal-to-noise ratio. We show how to best sample q-space when approaching this limit. We confirm that the apparent diffusion/dispersion coefficient can be used to infer fluid velocity for Péclet number Pé > 1. When measuring flow in biological systems we determined that with realistic experimental protocols the limits are roughly 1 µm/s for velocimetry and 300 µm/s for diffusometry.There is a strong interest to measure slow flows in vivo, e.g., measuring microcirculation of blood in capillary networks [6] and advective transport in the glymphatic system [7,8]. Knowledge of the lower limit of velocity resolution, as well as how to best measure slow velocities with PGSE MRI may open the door for these and other new applications. General TheoryOne beautiful aspect of magnetic resonance is the multitude of information which can be encoded into its complex signal. Pulsed gradient spin echo (PGSE) NMR exemplifies this in the solution to the Bloch-Torrey equations for the normalized complex signal E(q) arising from a fluid undergoing self-diffusion and coherent flow [9, 10, 11, 1],in which q sensitizes the signal to displacement during an observation time ∆. With rectangular gradient pulse shapes of amplitude g and duration δ, in the case that δ << ∆, q = γgδ where γ is the gyromagnetic ratio of the nuclei. Equation (1) shows that the phase shift between the real and imaginary channels and the attenuation of the signal intensity provide the ability to measure both mean coherent velocity, v, and random diffusion,

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Investigating Low-Velocity Fluid Flow in Tumors with Convection-MRI

Cited by 37 publications

References 51 publications

Investigating the Contribution of Collagen to the Tumor Biomechanical Phenotype with Noninvasive Magnetic Resonance Elastography

Investigating the Contribution of Collagen to the Tumor Biomechanical Phenotype with Noninvasive Magnetic Resonance Elastography

Modelling the transport of fluid through heterogeneous, whole tumours in silico

Limits to flow detection in phase contrast MRI

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