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International Journal for Multiscale Computational Engineering

Published 6 issues per year

ISSN Print: 1543-1649

ISSN Online: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

A Multiphysics Model of Myoma Growth

Volume 7, Issue 1, 2009, pp. 17-27
DOI: 10.1615/IntJMultCompEng.v7.i1.40
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ABSTRACT

We present a first attempt to create an in silico model of a uterine leiomyoma, a typical exponent of a common benign tumor. We employ a finite element model to investigate the interaction between nutrient-driven growth of the pathology and the mechanical response of the surrounding healthy tissue. The model includes neoplastic tissue growth, oxygen and growth factor transport, and angiogenic sprouting. Neovascularisation is addressed implicitly by modeling proliferation of endothelial cells and their migration up the gradients of the angiogenic growth factor, produced in hypoxic regions of the tumor. The response of the surrounding healthy tissue in our model is that of a viscoelastic material, whereby a stress exerted by the expanding neoplasm is slowly dissipated. The model parameters are estimated based on data from the available literature. By incorporating the interplay of a few underlying processes in one multiphysics simulation, we are able to explain some experimental findings on the pathology’s phenotype. The model has the potential to become a computer simulation tool to study various growing conditions and treatment strategies and to predict posttreatment conditions of a benign tumor.

REFERENCES
  1. Mencaglia, L. and Hamou, J. E., Manual of Gynecological Hysteroscopy.

  2. Walocha, J. A., Litwin, J. A., and Miodonski, A. J., Vascular system of intramural leiomyomata revealed by corrosion casting and scanning electron microscopy. DOI: 10.1093/humrep/deg213

  3. Szczerba, D. and Szekely, G., Simulating vascular systems in arbitrary anatomies. DOI: 10.1007/11566489_79

  4. Szczerba, D., Szekely, G., and Kurz, H., A multiphysics model of capillary growth and remodeling. DOI: 10.1007/11758525_12

  5. Lloyd, B., Szczerba, D., and Szekely, G., A coupled finite element model of tumor growth and vascularization. DOI: 10.1007/978-3-540-75759-7_106

  6. Lloyd, B. A., Szczerba, D., Rudin, M., and Szekely, G., A computational framework for modelling solid tumour growth. DOI: 10.1098/rsta.2008.0092

  7. Gordon, V. D., Valentine, M. T., Gardel, M. L., Andor-Ardo, D., Dennison, S., Bogdanov, A. A., Weitz, D. A., and Deisboeck, T. S., Measuring the mechanical stress induced by an expanding multicellular tumor system: A case study. DOI: 10.1016/S0014-4827(03)00256-8

  8. Ausprunk, D. H. and Folkman, J., Migration and proliferation of endothelial cells in preformed and newly formed blood vessels during tumor angiogenesis. DOI: 10.1016/0026-2862(77)90141-8

  9. Cristini, V., Lowengrub, J., and Nie, Q., Nonlinear simulation of tumor growth. DOI: 10.1007/s00285-002-0174-6

  10. Graziano, L. and Preziosi, L., Mechanics in tumor growth. DOI: 10.1007/978-0-8176-4411-6_7

  11. Huang, C.-Y., Soltz, M. A., Kopacz, M., Mow, V. C., and Ateshian, G. A., Experimental verification of the roles of intrinsic matrix viscoelasticity and tension-compression nonlinearity in the biphasic response of cartilage. DOI: 10.1115/1.1531656

  12. Humphrey, J. D. and DeLange, S., An Introduction to Biomechanics: Solids and Fluids, Analysis and Design.

  13. Freyer, J. P. and Sutherland, R. M., A reduction in the in situ rates of oxygen and glucose consumption of cells in EMT6/Ro spheroids during growth. DOI: 10.1002/jcp.1041240323

  14. Frieboes, H. B., Zheng, X., Sun, C.-H., Tromberg, B., Gatenby, R., and Cristini, V., An integrated computational/experimental model of tumor invasion. DOI: 10.1158/0008-5472.CAN-05-3166

  15. Salathe, E. P. and Xu, Y. H., Non-linear phenomena in oxygen transport to tissue. DOI: 10.1007/BF00160332

  16. Ji, J. W., Tsoukias, N. M., Goldman, D., and Popel, A. S., A computational model of oxygen transport in skeletal muscle for sprouting and splitting modes of angiogenesis. DOI: 10.1016/j.jtbi.2005.11.019

  17. Vaupel, P., Schlenger, K., Knoop, C., and Hockel, M., Oxygenation of human tumors: evaluation of tissue oxygen distribution in breast cancers by computerized O2 tension measurements.

  18. MacGabhann, F. and Popel, A. S., Interactions of VEGF isoforms with VEGFR-1, VEGFR-2, and neuropilin in vivo: a computational model of human skeletal muscle. DOI: 10.1152/ajpheart.00637.2006

  19. Anderson, A. R. A. and Chaplain, M. A. J., Continuous and discrete mathematical models of tumor-induced angiogenesis. DOI: 10.1006/bulm.1998.0042

  20. Stokes, C. L., Rupnick, M. A., Williams, S. K., and Lauffenburger, D. A., Chemotaxis of human microvessel endothelial cells in response to acidic fibroblast growth factor.

  21. Ursino, M., Giammarco, P. D., and Belardinelli, E., A mathematical model of cerebral blood flow chemical regulation–Part I: Diffusion processes. DOI: 10.1109/10.16465

  22. McMahan, C. A., Maxwell, L. C., and Shepherd, A. P, Estimation of the distribution of blood vessel diameters from the arteriovenous passage of microspheres.

  23. Baserga, R., The relationship of the cell cycle to tumor growth and control of cell division: A review.

  24. Chaplain, M. A. J., Graziano, L., and Preziosi, L., Mathematical modelling of the loss of tissue compression responsiveness and its role in solid tumour development. DOI: 10.1093/imammb/dql009

  25. Shtengold, E. and Godin, E. A., F266: The influence of ischemia on viscoelastic tissue properties. DOI: 10.1016/0006-355X(95)92378-N

  26. Kerdok, A. E., Ottensmeyer, M. P., and Howe, R. D., Effects of perfusion on the viscoelastic characteristics of liver. DOI: 10.1016/j.jbiomech.2005.07.005

  27. Kuroiwa, T., Yamada, I., Katsumata, N., Endo, S., and Ohno, K., Ex vivo measurement of brain tissue viscoelasticity in postischemic brain edema. DOI: 10.1007/3-211-30714-1_54

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