[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Investigation of phase-field models of tumor growth based on a reduced-order meshless Galerkin method

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The current paper concerns to develop a new numerical formulation to simulate the tumor growth. The used numerical method is based on the meshless Galerkin technique in which the test and trial functions have been selected from the shape functions of moving Taylor approximation. The main mathematical model to describe the tumor growth is defined as a nonlinear system of equations. Thus, to get acceptable results from the Galerkin weak form, a two-grid algorithm is employed. The first step of the two-grid algorithm computes the corresponding approximated scheme in a coarse mesh by solving a nonlinear algebraic system of equations. Then, the obtained solution in the previous step has been used to solve the corresponding approximated scheme in a fine mesh, such that in the second step, a linear algebraic system of equations is solved. On the other hand, to access more accurate results, the number of nodes in the computational domain must be increased which causes the matrix to become larger. Therefore, the proper orthogonal decomposition is used to reduce size of the algebraic system of equations. Finally, some test problems are tested to confirm the efficiency and accuracy of the proposed numerical formulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data

All data supporting the findings of this study are available within the article.

References

  1. Anderson AR, Quaranta V (2008) Integrative mathematical oncology. Nat Rev Cancer 8(3):227–234

    Google Scholar 

  2. Antonarakis ES, Carducci MA (2012) Targeting angiogenesis for the treatment of prostate cancer. Expert Opin Ther Targets 16(4):365–376

    Google Scholar 

  3. Benzekry S, Hahnfeldt P (2013) Maximum tolerated dose versus metronomic scheduling in the treatment of metastatic cancers. J Theor Biol 335:235–244

    Google Scholar 

  4. Bogdanska M, Bodnar M, Belmonte-Beitia J, Murek M, Schucht P, Beck J, Perez-Garcia V (2017) A mathematical model of low grade gliomas treated with temozolomide and its therapeutical implications. Math Biosci 288:1–13

    MathSciNet  Google Scholar 

  5. Chaturantabut S (2009) Dimension reduction for unsteady nonlinear partial differential equations via empirical interpolation methods. proquest

  6. Chaturantabut S, Sorensen DC (2012) A state space error estimate for POD-DEIM nonlinear model reduction. SIAM J Numer Anal 50(1):46–63

    MathSciNet  Google Scholar 

  7. Chaturantabut S (2017) Temporal localized nonlinear model reduction with a priori error estimate. Appl Numer Math 119:225–238

    MathSciNet  Google Scholar 

  8. Chaturantabut S, Sorensen DC (2010) Nonlinear model reduction via discrete empirical interpolation. SIAM J Sci Comput 32(5):2737–2764

    MathSciNet  Google Scholar 

  9. Chaturantabut S, Sorensen DC (2011) Application of POD and DEIM on dimension reduction of non-linear miscible viscous fingering in porous media. Math Comput Model Dyn Syst 17(4):337–353

    MathSciNet  Google Scholar 

  10. Colli P, Gomez H, Lorenzo G, Marinoschi G, Reali A, Rocca E (2020) Mathematical analysis and simulation study of a phase-field model of prostate cancer growth with chemotherapy and antiangiogenic therapy effects. Math Models Methods Appl Sci 30(7):1253–1295

    MathSciNet  Google Scholar 

  11. Cordier L, Abou El Majd B, Favier J (2010) Calibration of POD reduced-order models using Tikhonov regularization. Int J Numer Methods Fluids 63:269–296

    MathSciNet  Google Scholar 

  12. Couplet M, Basdevant C, Sagaut P (2005) Calibrated reduced-order POD-Galerkin system for fluid flow modelling. J Comput Phys 207:192–220

    MathSciNet  Google Scholar 

  13. Corwin D, Holdsworth C, Rockne RC, Trister AD, Mrugala MM, Rockhill JK, Stewart RD, Phillips M, Swanson KR (2013) Toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma. PLoS ONE 8(11):e79115

    Google Scholar 

  14. Das P, Mukherjee S, Das P, Banerjee S (2020) Characterizing chaos and multifractality in noise-assisted tumor-immune interplay. Nonlinear Dyn 101:675–685

    Google Scholar 

  15. Dimitriu G, Stefanescu R, Navon IM (2015) POD-DEIM approach on dimension reduction of a multi-species host-parasitoid system. Acad Rom Sci 7(1):173–188

    MathSciNet  Google Scholar 

  16. Du J, Navon IM, Steward JL, Alekseev AK, Luo Z (2012) Reduced-order modeling based on POD of a parabolized Navier–Stokes equation model I: forward model. Int J Numer Methods Fluids 69:710–730

    MathSciNet  Google Scholar 

  17. Du J, Navon IM, Zhu J, Fang F, Alekseev AK (2013) Reduced order modeling based on POD of a parabolized Navier–Stokes equations model II: trust region POD 4D var data assimilation. Comput Math Appl 65:380–394

    MathSciNet  Google Scholar 

  18. Fang F, Zhang T, Pavlidis D, Pain CC, Buchan AG, Navon IM (2014) Reduced order modelling of an unstructured mesh air pollution model and application in 2D/3D urban street canyons. Atmos Environ 96:96–106

    Google Scholar 

  19. Fang F, Pain CC, Navon IM, Gorman GJ, Piggott MD, Allison PA, Farrell PE, Goddard AJH, Pod A (2009) reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows. Ocean Model 28:127–136

    Google Scholar 

  20. Gallaher JA, Enriquez-Navas PM, Luddy KA, Gatenby RA, Anderson AR (2018) Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies. Cancer Res 78(8):2127–2139

    Google Scholar 

  21. Kalb VL, Deane AE (2007) An intrinsic stabilization scheme for proper orthogonal decomposition based low-dimensional models. Phys Fluids 19:054106

    Google Scholar 

  22. Kerschen G, Golinval J, Vakakis AF, Bergman LA (2005) The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview. Nonlinear Dyn 41(1–3):147–169

    MathSciNet  Google Scholar 

  23. Lin Z, Xiao D, Fang F, Pain CC, Navon IM (2017) Non-intrusive reduced order modelling with least squares fitting on a sparse grid. Int J Numer Methods Fluids 83:291–306

    MathSciNet  Google Scholar 

  24. Lorenzo G, Hughes TJR, Dominguez-Frojan P, Reali A, Gomez H (2019) Computer simulations suggest that prostate enlargement due to benign prostatic hyperplasia mechanically impedes prostate cancer growth. Proc Natl Acad Sci USA 116(4):1152–1161

    Google Scholar 

  25. Lorenzo G, Scott MA, Tew K, Hughes TJR, Gomez H (2017) Hierarchically refined and coarsened splines for moving interface problems, with particular application to phase-field models of prostate tumor growth. Comput Methods Appl Mech Eng 319:515–548

    MathSciNet  Google Scholar 

  26. Lorenzo G, Scott MA, Tew K, Hughes TJR, Zhang YJ, Liu L, Vilanova G, Gomez H (2016) Tissue-scale, personalized modeling and simulation of prostate cancer growth. Proc Natl Acad Sci USA 113(48):E7663–E7671

    Google Scholar 

  27. Luo Z, Li H, Sun P, An J, Navon IM (2013) A reduced-order finite volume element formulation based on POD method and numerical simulation for two-dimensional solute transport problems. Math Comput Simul 89:50–68

    MathSciNet  Google Scholar 

  28. Luo Z, Li H, Zhou Y, Xie Z (2012) A reduced finite element formulation based on POD method for two-dimensional solute transport problems. J Math Anal Appl 385:371–383

    MathSciNet  Google Scholar 

  29. Luo Z, Chen J, Navon IM, Yang X (2008) Mixed finite element formulation and error estimate based on proper orthogonal decomposition for the nonstationary Navier–Stokes equations. SIAM J Numer Anal 47:1–19

    MathSciNet  Google Scholar 

  30. Luo Z, Chen J, Zhu J, Wang R, Navon IM (2007) An optimizing reduced order FDs for the tropical pacific ocean reduced gravity model. Int J Numer Methods Fluids 55:143–161

    MathSciNet  Google Scholar 

  31. Iollo A, Lanteri S, Desideri J (2000) Stability properties of POD-Galerkin approximations for the compressible Navier–Stokes equations. Theor Comput Fluid Dyn 13:377–396

    Google Scholar 

  32. Hinow P, Gerlee P, McCawley LJ, Quaranta V, Ciobanu M, Wang S, Graham JM, Ayati BP, Claridge J, Swanson KR et al (2009) A spatial model of tumor-host interaction: application of chemotherapy. Math Biosci Eng 6(3):521–546

    MathSciNet  Google Scholar 

  33. Ravindran S (2000) Reduced-order adaptive controllers for fluid flows using POD. J Sci Comput 15(4):457–478

    MathSciNet  Google Scholar 

  34. Ravindran SS (2000) A reduced-order approach for optimal control of fluids using proper orthogonal decomposition. Int J Numer Methods Fluids 34(5):425–448

    MathSciNet  Google Scholar 

  35. San O, Iliescu T (2013) Proper orthogonal decomposition closure models for fluid flows: Burgers equation. Int J Numer Anal Model Ser B 1:1–18

    Google Scholar 

  36. Stefanescu R, Navon IM (2013) POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model. J Comput Phys 237:95–114

    MathSciNet  Google Scholar 

  37. Stewart JM, Broadbridge P, Goard JM (2002) Symmetry analysis and numerical modelling of invasion by malignant tumour tissue. Nonlinear Dyn 28:175–193

    MathSciNet  Google Scholar 

  38. Perret L, Collin E, Delville J (2006) Polynomial identification of POD based low-order dynamical system. J Turbul 7:1–15

    MathSciNet  Google Scholar 

  39. Powathil G, Kohandel M, Sivaloganathan S, Oza A, Milosevic M (2007) Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy. Phys Med Biol 52(11):3291–3306

    Google Scholar 

  40. Wang Y, Navon IM, Wang X, Cheng Y (2016) 2D Burgers equation with large Reynolds number using POD/DEIM and calibration. Int J Numer Methods Fluids 82(12):909–931

    MathSciNet  Google Scholar 

  41. Wazwaz AM (2004) The tanh method for traveling wave solutions of nonlinear equations. Appl Math Comput 154(3):713–723

    MathSciNet  Google Scholar 

  42. Wazwaz AM (2004) A sine–cosine method for handling nonlinear wave equations. Math Comput Model 40(5–6):499–508

    MathSciNet  Google Scholar 

  43. Wazwaz AM (2009) Partial differential equations and solitary waves theory. Higher Education Press/Springer, Beijin/Berlin

    Google Scholar 

  44. Wazwaz AM (2001) Constructions of soliton solutions and periodic solutions of the Boussinesq equation by the modified decomposition method. Chaos Solitons Fract 12:1549–1556

    MathSciNet  Google Scholar 

  45. Wazwaz AM (2006) Compactons and solitary wave solutions for the Boussinesq wave equation and its generalized form. Appl Math Comput 182:529–535

    MathSciNet  Google Scholar 

  46. Wazwaz AM, El-Tantawy SA (2017) Solving the (3+1)-dimensional KP-Boussinesq and BKP-Boussinesq equations by the simplified Hirota’s method. Nonlinear Dyn 88:3017–3021

    MathSciNet  Google Scholar 

  47. Xiao D, Fang F, Buchan AG, Pain CC, Navon IM, Du J, Hu GD (2014) Non-linear model reduction for the Navier–Stokes equations using residual DEIM method. J Comput Phys 263:1–18

    MathSciNet  Google Scholar 

  48. Xiao D, Fang F, Buchan AG, Pain CC, Navon IM, Muggeridge A (2015) Non-intrusive reduced order modelling of the Navier–Stokes equations. Comput Methods Appl Mech Eng 293:522–541

    MathSciNet  Google Scholar 

  49. Xiao D, Fang F, Du J, Pain CC, Navon IM, Buchan AG, ElSheikh AH, Hu GD (2013) Non-linear Petrov–Galerkin methods for reduced order modelling of the Navier–Stokes equations using a mixed finite element pair. Comput Methods Appl Mech Eng 255:147–157

    MathSciNet  Google Scholar 

  50. Xiao D, Fang F, Pain CC, Hu GD (2015) Non-intrusive reduced-order modelling of the Navier–Stokes equations based on RBF interpolation. Int J Numer Methods Fluids 79:580–595

    MathSciNet  Google Scholar 

  51. Fu R, Xiao D, Navon IM, Fang F, Yang L, Wang C, Cheng S (2023) A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods. Int J Numer Methods Eng 24:3087–3111

  52. Xiao D, Yang P, Fang F, Xiang J, Pain CC, Navon IM, Chen M (2017) A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting. J Comput Phys 330:221–244

    MathSciNet  Google Scholar 

  53. Xiang S, Fu X, Zhou J, Wang Y, Zhang Y, Hu X, Xu J, Liu H, Liu J, Ma J, Tao S (2021) Non-intrusive reduced order model of urban airflow with dynamic boundary conditions. Build Environ 187:107397

    Google Scholar 

  54. Xu J, Vilanova G, Gomez H (2016) A mathematical model coupling tumor growth and angiogenesis. PLoS ONE 11(2):e0149422

    Google Scholar 

  55. Wang X, Liu Y, Ouyang J (2020) A meshfree collocation method based on moving Taylor polynomial approximation for high order partial differential equations. Eng Anal Bound Elem 116:77–92

    MathSciNet  Google Scholar 

  56. Yang H, Tan Y (2021) Dynamic behavior of prostate cancer cells under antitumor immunity and pulse vaccination in a random environment. Nonlinear Dyn 105:2645–2664

    Google Scholar 

  57. Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V (2013) Clinically relevant modeling of tumor growth and treatment response. Sci Transl Med 5(187):187ps9

    Google Scholar 

  58. Zhang P, Zhang XH, Xiang H, Song L (2016) A fast and stabilized meshless method for the convection-dominated convection–diffusion problems. Numer Heat Transf Part A Appl 70(4):420–431

    Google Scholar 

  59. Zhang X, Xiang H (2015) A fast meshless method based on proper orthogonal decomposition for the transient heat conduction problems. Int J Heat Mass Transf 84:729–739

    Google Scholar 

  60. Zhang T, Zhong H, Zhao J (2011) A full discrete two-grid finite-volume method for a nonlinear parabolic problem. Int J Comput Math 88:1644–1663

    MathSciNet  Google Scholar 

  61. Mohammadi V, Dehghan M, De Marchi S (2021) Numerical simulation of a prostate tumor growth model by the RBF-FD scheme and a semi-implicit time discretization. J Comput Appl Math 388:113314

  62. Mohammadi V, Dehghan M, Khodadadian A, Noii N, Wick T (2022) An asymptotic analysis and numerical simulation of a prostate tumor growth model via the generalized moving least squares approximation combined with semi-implicit time integration, Appl Math Model 104:826-849

  63. Abbaszadeh M, Dehghan M (2020) A meshless numerical investigation based on the RBF-QR approach for elasticity problems. AUT J Math Comput 1(1):1–15

  64. Dehghan M, Abbaszadeh M (2019) The simulation of some chemotactic bacteria patterns in liquid medium which arises in tumor growth with blow-up phenomena via a generalized smoothed particle hydrodynamics (GSPH) method. Eng Comput 35:875–892. https://doi.org/10.1007/s00366-018-0638-y

  65. Zamani-Gharaghoshi H, Dehghan M, Abbaszadeh M (2023) A meshless collocation method based on Pascal polynomial approximation and implicit closest point method for solving reaction–diffusion systems on surfaces. Eng Comp. https://doi.org/10.1007/s00366-023-01794-y

Download references

Acknowledgements

We would like to express our heartfelt gratitude to three reviewers, for their suggestions and guidance throughout the revision process.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mostafa Abbaszadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbaszadeh, M., Dehghan, M. & Xiao, D. Investigation of phase-field models of tumor growth based on a reduced-order meshless Galerkin method. Engineering with Computers 40, 2331–2347 (2024). https://doi.org/10.1007/s00366-023-01892-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-023-01892-x

Keywords

Navigation