Validation and parameter optimization of a hybrid embedded/homogenized solid tumor perfusion model

The goal of this paper is to investigate the validity of a hybrid embedded/homogenized in-silico approach for modeling perfusion through solid tumors. The rationale behind this novel idea is that only the larger blood vessels have to be explicitly resolved while the smaller scales of the vasculature are homogenized. As opposed to typical discrete or fully-resolved 1D-3D models, the required data can be obtained with in-vivo imaging techniques since the morphology of the smaller vessels is not necessary. By contrast, the larger vessels, whose topology and structure is attainable non-invasively, are resolved and embedded as one-dimensional inclusions into the three-dimensional tissue domain which is modeled as a porous medium. A sound mortar-type formulation is employed to couple the two representations of the vasculature. We validate the hybrid model and optimize its parameters by comparing its results to a corresponding fully-resolved model based on several well-defined metrics. These tests are performed on a complex data set of three different tumor types with heterogeneous vascular architectures. The correspondence of the hybrid model in terms of mean representative elementary volume blood and interstitial fluid pressures is excellent with relative errors of less than 4%. Larger, but less important and explicable errors are present in terms of blood flow in the smaller, homogenized vessels. We finally discuss and demonstrate how the hybrid model can be further improved to apply it for studies on tumor perfusion and the efficacy of drug delivery. This article is protected by copyright. All rights reserved.

[1]  Barbara I. Wohlmuth,et al.  Hybrid Models for Simulating Blood Flow in Microvascular Networks , 2018, Multiscale Model. Simul..

[2]  R. Jain Normalization of Tumor Vasculature: An Emerging Concept in Antiangiogenic Therapy , 2005, Science.

[3]  R. K. Jain,et al.  Intratumoral infusion of fluid: estimation of hydraulic conductivity and implications for the delivery of therapeutic agents. , 1998, British Journal of Cancer.

[4]  P. Carmeliet,et al.  Angiogenesis in cancer and other diseases , 2000, Nature.

[5]  Paolo Zunino,et al.  A computational study of cancer hyperthermia based on vascular magnetic nanoconstructs , 2016, Royal Society Open Science.

[6]  V. Ntziachristos,et al.  Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy , 2020, Light: Science & Applications.

[7]  R K Jain,et al.  Barriers to drug delivery in solid tumors. , 1994, Scientific American.

[8]  Nicolas P Smith,et al.  Estimation of Blood Flow Rates in Large Microvascular Networks , 2012, Microcirculation.

[9]  Eva Bezak,et al.  A review of the development of tumor vasculature and its effects on the tumor microenvironment , 2017, Hypoxia.

[10]  Barbara I. Wohlmuth,et al.  Optimal A Priori Error Estimates for an Elliptic Problem with Dirac Right-Hand Side , 2014, SIAM J. Numer. Anal..

[11]  Rebecca J Shipley,et al.  Multiscale Modeling of Fluid Transport in Tumors , 2008, Bulletin of mathematical biology.

[12]  Carlo D'Angelo,et al.  Finite Element Approximation of Elliptic Problems with Dirac Measure Terms in Weighted Spaces: Applications to One- and Three-dimensional Coupled Problems , 2012, SIAM J. Numer. Anal..

[13]  Alfio Quarteroni,et al.  Cardiovascular mathematics : modeling and simulation of the circulatory system , 2009 .

[14]  Yohan Davit,et al.  Homogenization via formal multiscale asymptotics and volume averaging: How do the two techniques compare? , 2013 .

[15]  A Ziemys,et al.  A composite smeared finite element for mass transport in capillary systems and biological tissue. , 2017, Computer methods in applied mechanics and engineering.

[16]  Laura Cattaneo,et al.  Computational models for fluid exchange between microcirculation and tissue interstitium , 2014, Networks Heterog. Media.

[17]  Timothy W. Secomb,et al.  Transport of drugs from blood vessels to tumour tissue , 2017, Nature Reviews Cancer.

[18]  A. Quarteroni Cardiovascular mathematics , 2000 .

[19]  Timothy W. Secomb,et al.  Green's Function Methods for Analysis of Oxygen Delivery to Tissue by Microvascular Networks , 2004, Annals of Biomedical Engineering.

[20]  V. Ntziachristos,et al.  Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy , 2020, Light, science & applications.

[21]  D. McDonald,et al.  Cellular abnormalities of blood vessels as targets in cancer. , 2005, Current opinion in genetics & development.

[22]  T. Secomb,et al.  Simulation of O2 transport in skeletal muscle: diffusive exchange between arterioles and capillaries. , 1994, The American journal of physiology.

[23]  Barbara Wohlmuth,et al.  Mathematical modelling, analysis and numerical approximation of second order elliptic problems with inclusions , 2018 .

[24]  Axel R Pries,et al.  A hybrid discrete-continuum approach for modelling microcirculatory blood flow. , 2019, Mathematical medicine and biology : a journal of the IMA.

[25]  Barbara I. Wohlmuth,et al.  A Mortar Finite Element Method Using Dual Spaces for the Lagrange Multiplier , 2000, SIAM J. Numer. Anal..

[26]  S. Walker-Samuel,et al.  Modelling the transport of fluid through heterogeneous, whole tumours in silico , 2019, bioRxiv.

[27]  D Ambrosi,et al.  The role of the microvascular tortuosity in tumor transport phenomena. , 2015, Journal of theoretical biology.

[28]  R J Ordidge,et al.  The measurement of diffusion and perfusion in biological systems using magnetic resonance imaging , 2000 .

[29]  Alfio Quarteroni,et al.  Multiscale homogenization for fluid and drug transport in vascularized malignant tissues , 2015 .

[30]  B. Wohlmuth,et al.  A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks , 2020, International journal for numerical methods in biomedical engineering.

[31]  H. Maeda,et al.  A new concept for macromolecular therapeutics in cancer chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. , 1986, Cancer research.

[32]  P Zunino,et al.  Modelling mass and heat transfer in nano-based cancer hyperthermia , 2015, Royal Society Open Science.

[33]  R K Jain,et al.  Transport of fluid and macromolecules in tumors. I. Role of interstitial pressure and convection. , 1989, Microvascular research.

[34]  Wolfgang A. Wall,et al.  Unified computational framework for the efficient solution of n-field coupled problems with monolithic schemes , 2016, 1605.01522.

[35]  Wolfgang A. Wall,et al.  A dual mortar approach for 3D finite deformation contact with consistent linearization , 2010 .

[36]  Kristian Pietras,et al.  High interstitial fluid pressure — an obstacle in cancer therapy , 2004, Nature Reviews Cancer.

[37]  Rakesh K Jain,et al.  Abnormalities in pericytes on blood vessels and endothelial sprouts in tumors. , 2002, The American journal of pathology.

[38]  R. Penta,et al.  The role of the microvascular network structure on diffusion and consumption of anticancer drugs , 2017, International journal for numerical methods in biomedical engineering.

[39]  Bernhard A. Schrefler,et al.  A monolithic multiphase porous medium framework for (a-)vascular tumor growth. , 2018, Computer methods in applied mechanics and engineering.

[40]  Bernhard A Schrefler,et al.  An approach for vascular tumor growth based on a hybrid embedded/homogenized treatment of the vasculature within a multiphase porous medium model , 2019, International journal for numerical methods in biomedical engineering.

[41]  Christophe Geuzaine,et al.  Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities , 2009 .

[42]  Rebecca J. Shipley,et al.  Multiscale Modelling of Fluid and Drug Transport in Vascular Tumours , 2010, Bulletin of mathematical biology.

[43]  Matthias Mayr,et al.  A mortar-type finite element approach for embedding 1D beams into 3D solid volumes , 2019, Computational Mechanics.

[44]  M. Lythgoe,et al.  Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours , 2018, Nature Biomedical Engineering.

[45]  A. Pries,et al.  Microvascular blood viscosity in vivo and the endothelial surface layer. , 2005, American journal of physiology. Heart and circulatory physiology.

[46]  Tod A. Laursen,et al.  Two dimensional mortar contact methods for large deformation frictional sliding , 2005 .

[47]  J F Gross,et al.  Morphologic and hemodynamic comparison of tumor and healing normal tissue microvasculature. , 1989, International journal of radiation oncology, biology, physics.

[48]  B. Schrefler,et al.  Extension of a multiphase tumour growth model to study nanoparticle delivery to solid tumours , 2020, PloS one.

[49]  Mauro Ferrari,et al.  Transport Barriers and Oncophysics in Cancer Treatment. , 2018, Trends in cancer.

[50]  Mauro Ferrari,et al.  Frontiers in cancer nanomedicine: directing mass transport through biological barriers. , 2010, Trends in biotechnology.

[51]  Brett J Tully,et al.  Cerebral water transport using multiple-network poroelastic theory: application to normal pressure hydrocephalus , 2010, Journal of Fluid Mechanics.

[52]  C. Dangelo,et al.  Multiscale modelling of metabolism and transport phenomena in living tissues , 2007 .

[53]  R. Jain,et al.  Angiogenesis, microvascular architecture, microhemodynamics, and interstitial fluid pressure during early growth of human adenocarcinoma LS174T in SCID mice. , 1992, Cancer research.

[54]  P. Zunino,et al.  A computational model of drug delivery through microcirculation to compare different tumor treatments , 2014, International journal for numerical methods in biomedical engineering.

[55]  Phaedon-Stelios Koutsourelakis,et al.  A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime , 2019, J. Comput. Phys..

[56]  A. Quarteroni,et al.  On the coupling of 1D and 3D diffusion-reaction equations. Applications to tissue perfusion problems , 2008 .

[57]  G. Yancopoulos,et al.  New model of tumor angiogenesis: dynamic balance between vessel regression and growth mediated by angiopoietins and VEGF , 1999, Oncogene.