Imaging-based characterization of convective tissue properties

References

• Korangath P, Barnett JD, Sharma A, et al. Nanoparticle interactions with immune cells dominate tumor retention and induce t cell-mediated tumor suppression in models of breast cancer. Sci Adv. 2020;6(13):eaay1601.
   PubMed Web of Science ®Google Scholar
• Ernsting MJ, Murakami M, Roy A, et al. Factors controlling the pharmacokinetics, biodistribution and intratumoral penetration of nanoparticles. J Control Release. 2013;172(3):782–794.
   PubMed Web of Science ®Google Scholar
• Decuzzi P, Pasqualini R, Arap W, et al. Intravascular delivery of particulate systems: does geometry really matter? Pharm Res. 2009;26(1):235–243.
   PubMed Web of Science ®Google Scholar
• Gustafson HH, Holt-Casper D, Grainger DW, et al. Nanoparticle uptake: the phagocyte problem. Nano Today. 2015;10(4):487–510.
   PubMed Web of Science ®Google Scholar
• Kievit FM, Zhang M. Cancer nanotheranostics: improving imaging and therapy by targeted delivery across biological barriers. Adv Mater. 2011;23(36):H217–H247.
   PubMed Web of Science ®Google Scholar
• Chithrani BD, Ghazani AA, Chan WCW. Determining the size and shape dependence of gold nanoparticle uptake into mammalian cells. Nano Lett. 2006;6(4):662–668.
   PubMed Web of Science ®Google Scholar
• Soetaert F, Korangath P, Serantes D, et al. Cancer therapy with iron oxide nanoparticles: Agents of thermal and immune therapies. Adv Drug Delivery Rev. 2020. doi:10.1016/j.addr.2020.06.025
   PubMed Web of Science ®Google Scholar
• Sindhwani S, Syed AM, Ngai J, et al. The entry of nanoparticles into solid tumours. Nat Mater. 2020;19(5):566–575.
   PubMed Web of Science ®Google Scholar
• Liu LY, Ma XZ, Ouyang B, et al. Nanoparticle uptake in a spontaneous and immunocompetent Woodchuck Liver Cancer Model. ACS Nano. 2020;14(4):4698–4715.
   PubMed Web of Science ®Google Scholar
• Kim SH, Kamaya A, Willmann JK. Ct perfusion of the liver: principles and applications in oncology. Radiology. 2014;272(2):322–344.
   PubMed Web of Science ®Google Scholar
• Ippolito D, Fior D, Bonaffini PA, et al. Quantitative evaluation of ct-perfusion map as indicator of tumor response to transarterial chemoembolization and radiofrequency ablation in hcc patients. Eur J Radiol. 2014;83(9):1665–1671.
   PubMed Web of Science ®Google Scholar
• Axel L. Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis. Radiology. 1980;137(3):679–686.
   PubMed Web of Science ®Google Scholar
• Bischoff KB, Dedrick RL, Zaharko DS, et al. Methotrexate pharmacokinetics. J Pharm Sci. 1971;60(8):1128–1133.
   PubMed Web of Science ®Google Scholar
• Frieboes HB, Wu M, Lowengrub J, et al. A computational model for predicting nanoparticle accumulation in tumor vasculature. PLoS One. 2013;8(2):e56876.
   PubMed Web of Science ®Google Scholar
• Gilkey MJ, Krishnan V, Scheetz L, et al. Physiologically based pharmacokinetic modeling of fluorescently labeled block copolymer nanoparticles for controlled drug delivery in leukemia therapy. CPT Pharmacometrics Syst. Pharmacol. 2015;4(3):167–174.
   Google Scholar
• Li M, Panagi Z, Avgoustakis K, et al. Physiologically based pharmacokinetic modeling of plga nanoparticles with varied mpeg content. Int J Nanomedicine. 2012;7(5):1345–1356.
   PubMedGoogle Scholar
• Elgrabli D, Beaudouin R, Jbilou N, et al. Biodistribution and clearance of TiO2 Nanoparticles in Rats after Intravenous Injection. PLoS One. 2015;10(4):e0124490.
   PubMed Web of Science ®Google Scholar
• Duan C, Kallehauge JF, Bretthorst GL, et al. Are complex dce-mri models supported by clinical data? Magn Reson Med. 2017;77(3):1329–1339.
   PubMed Web of Science ®Google Scholar
• Chikui T, Obara M, Simonetti AW, et al. The principal of dynamic contrast enhanced mri, the method of pharmacokinetic analysis, and its application in the head and neck region. Int J Dent. 2012;2012:480659.
   PubMedGoogle Scholar
• Fuentes D, Fahrenholtz SJ, Guo C, et al. Mathematical modeling of mass and energy transport for thermoembolization. Int J Hyperthermia. 2020;37(1):356–365.
   PubMed Web of Science ®Google Scholar
• Soltani M, Chen P. Numerical modeling of fluid flow in solid tumors. PloS One. 2011;6(6):e20344.
   PubMed Web of Science ®Google Scholar
• Khaled A-RA, Vafai K. The role of porous media in modeling flow and heat transfer in biological tissues. Int J Heat Mass Transf. 2003;46(26):4989–5003.
   Web of Science ®Google Scholar
• Salama A, El-Amin MF, Abbas I, et al. On the viscous dissipation modeling of thermal fluid flow in a porous medium. Arch Appl Mech. 2011;81(12):1865–1876.
   Web of Science ®Google Scholar
• Tapani E, Vehmas T, Voutilainen P. Effect of injection speed on the spread of ethanol during experimental liver ethanol injections. Acad Radiol. 1996;3(12):1025–1029.
   PubMed Web of Science ®Google Scholar
• Boucher Y, Brekken C, Netti PA, et al. Intratumoral infusion of fluid: estimation of hydraulic conductivity and implications for the delivery of therapeutic agents. Br J Cancer. 1998;78(11):1442–1448.
   PubMed Web of Science ®Google Scholar
• Magdoom KN, Pishko GL, Rice L, et al. Mri-based computational model of heterogeneous tracer transport following local infusion into a mouse hind limb tumor. PloS One. 2014;9(3):e89594.
   PubMed Web of Science ®Google Scholar
• Barauskas R, Gulbinas A, Barauskas G. Finite element modeling and experimental investigation of infiltration of sodium chloride solution into nonviable liver tissue. Medicina (Kaunas). 2007;43(5):399–411.
   PubMed Web of Science ®Google Scholar
• Honarpour M, Mahmood SM. Relative-permeability measurements: an overview. J Petrol Technol. 1988;40(08):963–966.
   Google Scholar
• Chauhan VP, Stylianopoulos T, Martin JD, et al. Normalization of tumour blood vessels improves the delivery of nanomedicines in a size-dependent manner. Nat Nanotechnol. 2012;7(6):383–388.
   PubMed Web of Science ®Google Scholar
• Xi G, Robinson E, Mania-Farnell B, et al. Convection-enhanced delivery of nanodiamond drug delivery platforms for intracranial tumor treatment. Nanomedicine. 2014;10(2):381–391.
   PubMed Web of Science ®Google Scholar
• Fuentes D, Elliott A, Weinberg JS, et al. An inverse problem approach to recovery of in vivo nanoparticle concentrations from thermal image monitoring of mr-guided laser induced thermal therapy. Ann Biomed Eng. 2013;41(1):100–111.
   PubMed Web of Science ®Google Scholar
• Lowekamp BC, Chen DT, Yaniv Z, et al. Scalable simple linear iterative clustering (sslic) using a generic and parallel approach. 2018. https://arxiv.org/abs/1806.08741
   Google Scholar
• Peaceman DW. Fundamentals of numerical reservoir simulation. New York (NY): Elsevier; 1977.
   Google Scholar
• Faust CR, Mercer JW. Geothermal reservoir simulation: 1. mathematical models for liquid-and vapor-dominated hydrothermal systems. Water Resour Res. 1979;15(1):23–30.
   Web of Science ®Google Scholar
• Kee RJ, Coltrin ME, Glarborg P. Chemically reacting flow: theory and practice. Hoboken (NJ): John Wiley & Sons; 2005.
   Google Scholar
• Oden JT, Hawkins A, Prudhomme S. General diffuse-interface theories and an approach to predictive tumor growth modeling. Math Models Methods Appl Sci. 2010;20(03):477–517.
   Web of Science ®Google Scholar
• Maurer CR, Qi R, Raghavan V. A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell. 2003;25(2):265–270.
   Web of Science ®Google Scholar
• Yingxuan Z, Andrey Fedorov SPL, John Evans MGH, et al. Pkmodeling module–3d slicer. [cited 2015 Apr 17]. Available from: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling.
   Google Scholar
• Chengyue W, Hormuth DA, Oliver TA, et al. Patient-specific characterization of breast cancer hemodynamics using image-guided computational fluid dynamics. IEEE Trans Med Imaging. 2020;39(9):2760–2771.
   Google Scholar
• Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993;10(7):1093–1095.
   PubMed Web of Science ®Google Scholar
• Doriot PA, Dorsaz PA, Dorsaz L, et al. Is the indicator dilution theory really the adequate base of many blood flow measurement techniques? Med Phys. 1997;24(12):1889–1898.
   PubMed Web of Science ®Google Scholar
• Lee T-Y. Functional ct: physiological models. Trends Biotechnol. 2002;20(8):S3–S10.
   PubMed Web of Science ®Google Scholar
• Brix G, Zwick S, Griebel J, et al. Estimation of tissue perfusion by dynamic contrast-enhanced imaging: Simulation-based evaluation of the steepest slope method. Eur Radiol. 2010;20(9):2166–2175.
   PubMed Web of Science ®Google Scholar
• Goetti R, Leschka S, Desbiolles L, et al. Quantitative computed tomography liver perfusion imaging using dynamic spiral scanning with variable pitch: feasibility and initial results in patients with cancer metastases. Investig Radiol. 2010;45(7):419–426.
   PubMed Web of Science ®Google Scholar
• Miles KA, Hayball MP, Dixon AK. Functional images of hepatic perfusion obtained with dynamic ct. Radiology. 1993;188(2):405–411.
   PubMed Web of Science ®Google Scholar
• Wang Y, Hobbs BP, Ng CS. Ct perfusion characteristics identify metastatic sites in liver. Biomed Res Int. 2015;2015:120749.
   PubMed Web of Science ®Google Scholar
• Van Beers BE, Leconte I, Materne R, et al. Hepatic perfusion parameters in chronic liver disease: dynamic ct measurements correlated with disease severity. Am J Roentgenol. 2001;176(3):667–673.
   PubMed Web of Science ®Google Scholar
• Kim KA, Choi SY, Kim MU, et al. The efficacy of cone-beam ct–based liver perfusion mapping to predict initial response of hepatocellular carcinoma to transarterial chemoembolization. J Vasc Interven Radiol. 2019;30(3):358–369.
   PubMed Web of Science ®Google Scholar
• Goh V, Halligan S, Daley F, et al. Colorectal tumor vascularity: quantitative assessment with multidetector ct–do tumor perfusion measurements reflect angiogenesis? Radiology. 2008;249(2):510–517.
   PubMed Web of Science ®Google Scholar
• Tamandl D, Waneck F, Sieghart W, et al. Early response evaluation using ct-perfusion one day after transarterial chemoembolization for hcc predicts treatment response and long-term disease control. Eur J Radiol. 2017;90:73–80.
   PubMed Web of Science ®Google Scholar
• Kikinis R, Steve D P, Vosburgh KG. 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz F, editor. Intraoperative imaging and image-guided therapy. New York (NY): Springer; 2014. p. 277–289.
   Google Scholar
• Jacob B. Dynamics of fluids in porous media. New York (NY): Courier Dover Publications; 2013.
   Google Scholar
• Oden JT, Lima EABF, Almeida RC, et al. Toward predictive multiscale modeling of vascular tumor growth. Arch Computat Methods Eng. 2016;23(4):735–745.
   Web of Science ®Google Scholar
• Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674.
   PubMed Web of Science ®Google Scholar
• Mukherjee D, Padilla J, Shadden SC. Numerical investigation of fluid-particle interactions for embolic stroke. Theor Comput Fluid Dyn. 2016;30(1–2):23–39.
   Web of Science ®Google Scholar
• Basciano CA, Kleinstreuer C, Kennedy AS, et al. Computer modeling of controlled microsphere release and targeting in a representative hepatic artery system. Ann Biomed Eng. 2010;38(5):1862–1879.
   PubMed Web of Science ®Google Scholar