Estimation of Tumor Interstitial Fluid Pressure (TIFP) Noninvasively

Liu, Long Jian; Brown, Stephen L.; Ewing, James R.; Ala, Brigitte D.; Schneider, Kenneth; Schlesinger, M.

doi:10.1371/journal.pone.0140892

Cited by 31 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examining the tumour radial IFP profiles, the LS147T network exhibited similar configurations as observed both experimentally in LS147T [17], in other cell lines [3, 24, 25], and in computational studies [31, 32, 36, 75], with elevated IFP at the tumour core (see Fig 4b and 4d). In addition, the LS147T network displayed a typical IFV profile radially, with an increasing IFV range towards the tumour surface due to the steeper pressure gradients at the periphery of the tumour.…”

Section: Resultssupporting

confidence: 75%

“…This approach has produced solutions which are highly consistent with experimental measurements in the same tumours [40]. In this study, baseline vascular flow solutions across all tumour simulations are in good agreement with the perfusion data, alongside mean flows [76], velocities and vessel wall shear stresses [37], and fluid pressure in the interstitium [3, 17, 24, 25]. This provided validation that our model produces physiologically realistic results, providing a platform to investigate the tumour microenvironment.…”

Section: Discussionsupporting

confidence: 72%

“…However, this method disturbs the local microenvironment and only provides an IFP measurement at individual locations. Non-invasive methods have also been developed to estimate tumour IFP [24, 25]. For example, convection-MRI, which measures low-velocity flow in tumours at a resolution of ∼ 250 μ m in vivo [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

Cancers exhibit spatially heterogeneous, unique vascular architectures across individual samples, cell-lines and patients. This inherently disorganised collection of leaky blood vessels contribute significantly to suboptimal treatment efficacy. Preclinical tools are urgently required which incorporate the inherent variability and heterogeneity of tumours to optimise and engineer anti-cancer therapies. In this study, we present a novel computational framework which incorporates whole, realistic tumours extracted ex vivo to efficiently simulate vascular blood flow and interstitial fluid transport in silico for validation against in vivo biomedical imaging. Our model couples Poiseuille and Darcy descriptions of vascular and interstitial flow, respectively, and incorporates spatially heterogeneous blood vessel lumen and interstitial permeabilities to generate accurate predictions of tumour fluid dynamics. Our platform enables highly-controlled experiments to be performed which provide insight into how tumour vascular heterogeneity contributes to tumour fluid transport. We detail the application of our framework to an orthotopic murine glioma (GL261) and a human colorectal carcinoma (LS147T), and perform sensitivity analysis to gain an understanding of the key biological mechanisms which determine tumour fluid transport. Finally we mimic vascular normalization by modifying parameters, such as vascular and interstitial permeabilities, and show that incorporating realistic vasculatures is key to modelling the contrasting fluid dynamic response between tumour samples. Contrary to literature, we show that reducing tumour interstitial fluid pressure is not essential to increase interstitial perfusion and that therapies should seek to develop an interstitial fluid pressure gradient. We also hypothesise that stabilising vessel diameters and permeabilities are not key responses following vascular normalization and that therapy may alter interstitial hydraulic conductivity. Consequently, we suggest that normalizing the interstitial microenvironment may provide a more effective means to increase interstitial perfusion within tumours.

show abstract

Section: Resultssupporting

confidence: 75%

Section: Discussionsupporting

confidence: 72%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Since drug delivery relies on the vascular system, an abnormal vasculature affects the deposition of drug molecules in a tumor through blood vessels. The presence of the high interstitial pressure in the tumor also hinders the drug delivery [ 3 , 4 ]. The drug molecules are extravasated from blood vessels, and their transport in the interstitium is driven by diffusion and convection effects.…”

Section: Introductionmentioning

confidence: 99%

Numerical modeling of nanodrug distribution in tumors with heterogeneous vasculature

et al. 2017

View full text Add to dashboard Cite

The distribution and accumulation of nanoparticle dosage in a tumor are important in evaluating the effectiveness of cancer treatment. The cell survival rate can quantify the therapeutic effect, and the survival rates after multiple treatments are helpful to evaluate the efficacy of a chemotherapy plan. We developed a mathematical tumor model based on the governing equations describing the fluid flow and particle transport to investigate the drug transportation in a tumor and computed the resulting cumulative concentrations. The cell survival rate was calculated based on the cumulative concentration. The model was applied to a subcutaneous tumor with heterogeneous vascular distributions. Various sized dextrans and doxorubicin were respectively chosen as the nanodrug carrier and the traditional chemotherapeutic agent for comparison. The results showed that: 1) the largest nanoparticle drug in the current simulations yielded the highest cumulative concentration in the well vascular region, but second lowest in the surrounding normal tissues, which implies it has the best therapeutic effect to tumor and at the same time little harmful to normal tissue; 2) on the contrary, molecular chemotherapeutic agent produced the second lowest cumulative concentration in the well vascular tumor region, but highest in the surrounding normal tissue; 3) all drugs have very small cumulative concentrations in the tumor necrotic region, where drug transport is solely through diffusion. This might mean that it is hard to kill tumor stem cells hiding in it. The current model indicated that the effectiveness of the anti-tumor drug delivery was determined by the interplay of the vascular density and nanoparticle size, which governs the drug transport properties. The use of nanoparticles as anti-tumor drug carriers is generally a better choice than molecular chemotherapeutic agent because of its high treatment efficiency on tumor cells and less damage to normal tissues.

show abstract

“…Another approach used by several works is dynamic contrast enhanced MRI where fluid flow velocity at the tumour surface is estimated by examining contrast agent over time [12][13][14] . This velocity can then be used to estimate IFP by assuming or estimating hydraulic conductivity and related parameters 15,16 . Alternatively, this velocity can be used as a stand-in for IFP as strong correlation has been shown in some cases 17 and recent work showed it may be a prognostic factor in cervical cancer 18 .…”

mentioning

confidence: 99%

Towards noninvasive estimation of tumour pressure by utilising MR elastography and nonlinear biomechanical models: a simulation and phantom study

Fovargue

Fiorito

Capilnasiu

et al. 2020

Sci Rep

View full text Add to dashboard Cite

The solid and fluid pressures of tumours are often elevated relative to surrounding tissue. This increased pressure is known to correlate with decreased treatment efficacy and potentially with tumour aggressiveness and therefore, accurate noninvasive estimates of tumour pressure would be of great value. We present a proof-of-concept method to infer the total tumour pressure, that is the sum of the fluid and solid parts, by examining stiffness in the peritumoural tissue with MR elastography and utilising nonlinear biomechanical models. The pressure from the tumour deforms the surrounding tissue leading to changes in stiffness. Understanding and accounting for these biases in stiffness has the potential to enable estimation of total tumour pressure. Simulations are used to validate the method with varying pressure levels, tumour shape, tumour size, and noise levels. Results show excellent matching in low noise cases and still correlate well with higher noise. Percent error remains near or below 10% for higher pressures in all noise level cases. Reconstructed pressures were also calculated from experiments with a catheter balloon embedded in a plastisol phantom at multiple inflation levels. Here the reconstructed pressures generally match the increases in pressure measured during the experiments. Percent errors between average reconstructed and measured pressures at four inflation states are 17.9%, 52%, 23.2%, and 0.9%. Future work will apply this method to in vivo data, potentially providing an important biomarker for cancer diagnosis and treatment.

show abstract

Estimation of Tumor Interstitial Fluid Pressure (TIFP) Noninvasively

Cited by 31 publications

References 23 publications

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

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

Numerical modeling of nanodrug distribution in tumors with heterogeneous vasculature

Towards noninvasive estimation of tumour pressure by utilising MR elastography and nonlinear biomechanical models: a simulation and phantom study

Contact Info

Product

Resources

About