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A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread

  • Special Issue : Mathematical Oncology
  • Published:
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Abstract

Gliomas are primary brain tumours arising from the glial cells of the nervous system. The diffuse nature of spread, coupled with proximity to critical brain structures, makes treatment a challenge. Pathological analysis confirms that the extent of glioma spread exceeds the extent of the grossly visible mass, seen on conventional magnetic resonance imaging (MRI) scans. Gliomas show faster spread along white matter tracts than in grey matter, leading to irregular patterns of spread. We propose a mathematical model based on Diffusion Tensor Imaging, a new MRI imaging technique that offers a methodology to delineate the major white matter tracts in the brain. We apply the anisotropic diffusion model of Painter and Hillen (J Thoer Biol 323:25–39, 2013) to data from 10 patients with gliomas. Moreover, we compare the anisotropic model to the state-of-the-art Proliferation–Infiltration (PI) model of Swanson et al. (Cell Prolif 33:317–329, 2000). We find that the anisotropic model offers a slight improvement over the standard PI model. For tumours with low anisotropy, the predictions of the two models are virtually identical, but for patients whose tumours show higher anisotropy, the results differ. We also suggest using the data from the contralateral hemisphere to further improve the model fit. Finally, we discuss the potential use of this model in clinical treatment planning.

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References

  • Alexander AL, Lee JE, Lazar M, Field AS (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4(3):316–329

    Article  Google Scholar 

  • Ambrosi D, Preziosi L (2002) On the closure of mass balance models for tumor growth. Math Model Method Appl Sci 12(5):737–754

    Article  MathSciNet  MATH  Google Scholar 

  • American Brain Tumor Association. http://www.abta.org/. Accessed 2016

  • Belmonte-Beitia J, Woolley TE, Scott JG, Maini PK, Gaffney EA (2013) Modelling biological invasions: individual to population scales at interfaces. J Theor Biol 334:1–12

    Article  MathSciNet  Google Scholar 

  • Bondiau PY, Clatz O, Sermesant M, Marcy PY, Delingette H, Frenay M, Ayache N (2008) Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging. Phys Med Biol 53:879–893

    Article  Google Scholar 

  • Burnet NG, Thomas SJ, Burton KE, Jefferies SJ (2004) Defining the tumour and target volumes for radiotherapy. Cancer Imaging 4:153–161

    Article  Google Scholar 

  • Clatz O, Sermesant M, Bondiau PY, Delignette H, Warfield SK, Malandain G, Ayache N (2005) Realistic simulation of the 3D growth of brain tumors in MRI images coupling diffusion with biomechanical deformation. IEEE Trans Med Imaging 24(10):1334–1346

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Diaz I, Boulanger P, Greiner R, Hoehn B, Rowe L, Murtha A (2013) An automatic brain tumor segmentation tool. Conf Proc IEEE Eng Med Biol Soc pp 3339–3342

  • Engwer C, Hillen T, Knappitsch MP, Surulescu C (2015) A DTI-based multiscale model for glioma growth including cell-ECM interactions. J Math Biol 71(3):551–582

    Article  MathSciNet  MATH  Google Scholar 

  • Engwer C, Knappitsch MP, Surulescu C (2016) A multiscale model for glioma spread including cell-fibre interactions and proliferation. Math Biosci Eng 15(2):443–460

    MATH  Google Scholar 

  • Giese A, Westphal M (1996) Glioma invasion in the central nervous system. Neurosurgery 39(2):235–252

    Article  Google Scholar 

  • Gritsenko PG, Ilina O, Friedl P (2012) Interstitial guidance of cancer invasion. J Pathol 226:185–199

    Article  Google Scholar 

  • Gu S, Chakraborty G, Champley K, Alessio AA, Claridge J, Rockne R, Muzi M, Krohn KA, Spence AM, Alvord EC Jr, Anderson ARA, Kinahan PE, Swanson KR (2012) Applying a patient-specific bio-mathematical model of glioma growth to develop virtual [18F]-FMISO-PET images. Math Med Biol 29(1):31–48

    Article  MATH  Google Scholar 

  • Hillen T (2003) Transport equations with resting phases. Eur J Appl Math 14(5):613–636

    Article  MathSciNet  MATH  Google Scholar 

  • Hillen T (2006) \({M}^5\) mesoscopic and macroscopic models for mesenchymal motion. J Math Biol 53(4):585–616

    Article  MathSciNet  MATH  Google Scholar 

  • Hillen T, Hinow P, Wang ZA (2010) Mathematical analysis of kinetic models for cell movement in network tissues. Discret Contin Dyn Syst 14(3):1055–1080

    Article  MathSciNet  MATH  Google Scholar 

  • Hillen T, Painter K (2012) Transport models for movement in oriented habitats and anisotropic diffusion. In: Lewis MA, Maini P, Petrovskii S (eds) Dispersal, individual movement and spatial ecology: A mathematical perspective. Springer, Heidelberg, 46 pages

  • Hillen T, Painter K, Swan A, Murtha A (2017) Moments of von Mises and Fisher distributions and applications. Math Biosci Eng 14(3):673–694

    Article  MathSciNet  MATH  Google Scholar 

  • Hogea C, Davatzikos C, Biros G (2007) Modeling glioma growth and mass effect in 3D MR images of the brain. In: Ayache N, Ourselin S, Maeder A (eds) Medical image computing and computer-assisted intervention – MICCAI 2007. Lecture notes in computer science, vol 4791. Springer, Berlin, Heidelberg

  • Hogea C, Davatzikos C, Biros G (2008) An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects. J Math Biol 56(6):793–825

    Article  MathSciNet  MATH  Google Scholar 

  • Jackson PR, Juliano J, Hawkins-Daarud A, Rockne RC, Swanson KR (2015) Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice. Bull Math Biol 77:846–856

    Article  MathSciNet  MATH  Google Scholar 

  • Jbabdi A, Mandonnet E, Duffau H, Capelle L, Swanson KR, Pelegrini-Issac M, Guillevin R, Benali H (2005) Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn Reson Med 54:616–624

    Article  Google Scholar 

  • Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S (2006) Dtistudio: resource program for diffusion tensor computation and finer bundle tracking. Comput Method Program Biomed 81:106–116

    Article  Google Scholar 

  • Jones DK, Basser PJ (2004) “Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data. Magn Reson Med 52:979–993

    Article  Google Scholar 

  • Jones DK, Leemans A (2011) Diffusion tensor imaging. Method Mol Biol 711:127–144

    Article  Google Scholar 

  • Kingsley PB (2006) Introduction to diffusion tensor imaging mathematics: part II. anisotropy, diffusion-weighting factors, and gradient encoding schemes. Concept Magn Reson Part A 28A(2):123–154

    Article  Google Scholar 

  • Kleihues P, Soylemezoglu F, Schäuble B, Scheithauer BW, Burger PC (1995) Histopathology, classification, and grading of gliomas. Glia 15:211–221

    Article  Google Scholar 

  • Kolb B, Whishaw IQ (2003) Fundamentals of human neuropsychology, 5th edn. Worth Publishers, New York, NY

  • Konukoglu E, Clatz O, Bondiau PY, Delignette H, Ayache N (2006) Extrapolating tumor invasion margins for physiologically determined radiotherapy regions. Med Image Comput Comput Assist Interv 9(1):338–346

    Google Scholar 

  • Konukoglu E, Clatz O, Bondiau PY, Delignette H, Ayache N (2010) Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins. Med Image Anal 14:111–125

    Article  Google Scholar 

  • Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546

    Article  Google Scholar 

  • Lebel C, Beaulieu C (2011) Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 31(30):10937–10947

    Article  Google Scholar 

  • Macklin P, Lowengrub J (2007) Nonlinear simulation of the effect of microenvironment on tumor growth. J Theor Biol 245:677–704

    Article  MathSciNet  Google Scholar 

  • Marusic M, Bajzer Z, Freyer JP, Vuk-Pavlovic S (1994) Analysis of growth of multicellular tumour spheroids by mathematical models. Cell Prolif 27:73–94

    Article  MATH  Google Scholar 

  • Mori S, van Zijl PCM (2012) Fiber tracking: principles and strategies—a technical review. NMR Biomed 15:468–480

    Article  Google Scholar 

  • Mosayebi P, Cobzas D, Murtha A, Jagersand M (2012) Tumor invasion margin on the Riemannian space of brain fibers. Med Image Anal 16(2):361–373

    Article  Google Scholar 

  • Neal ML, Tanner AD, Cloke T, Sodt R, Ahn S, Baldock AL, Bridge CA, Lai A, Cloughesy TF, Mrugala MM, Rockhill JK, Rockne RC, Swanson KR (2013) Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric. PLoS one 8(1):1–7

    Article  Google Scholar 

  • Okubo A, Levin SA (2001) Diffusion and ecological problems: modern perspectives. Springer, New York

    Book  MATH  Google Scholar 

  • Othmer HG, Stevens A (1997) Aggregation, blowup and collapse: the ABC’s of taxis in reinforced random walks. SIAM J Appl Math 57(4):1044–1081

    Article  MathSciNet  MATH  Google Scholar 

  • Painter KJ, Hillen T (2013) Mathematical modelling of glioma growth: the use of diffusion tensor imagining DTI data to predict the anisotropic pathways of cancer invasion. J Theor Biol 323:25–39

    Article  MATH  Google Scholar 

  • Popuri K, Cobzas D, Mutrtha A, Jagersand M (2012) 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int J Comput Assist Radiol Surg 7(4):493–506

    Article  Google Scholar 

  • Preziosi L, Tosin A (2008) Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications. J Math Biol 58:625–656

    Article  MathSciNet  MATH  Google Scholar 

  • Purves D, Augustine GJ, Fitzpatrick D, Hall WC, LaMantia AS, McNamara JO, White LE (2008) Neuroscience, 4th edn. Sinauer Associates, Sunderland, MA

    Google Scholar 

  • Rao JS (2003) Molecular mechanisms of glioma invasiveness: the role of proteases. Nat Rev Cancer 3:489–501

    Article  Google Scholar 

  • Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, Lai A, Cloghesy T, Alvord EC Jr, Swanson KR (2010) Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach. Phys Med Biol 55:3271–3285

    Article  Google Scholar 

  • Salah MB, Diaz I, Greiner R, Boulanger P, Hoehn B, Murtha A (2013) Fully Automated Brain Tumor Segmentation using two MRI Modalities. Chapter in: Advances in Visual Computing, Springer, Berlin, 27:30–39

  • Swanson KR, Bridge C, Murray JD, Alvord EC Jr (2003) Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sci 216:1–10

    Article  Google Scholar 

  • Swanson KR, Alvord EC Jr, Murray JD (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 33:317–329

    Article  Google Scholar 

  • Swanson KR, Rostomily RC, Alvord EC Jr (2008) A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br J Cancer 98:113–119

    Article  Google Scholar 

  • Wang CH, Rockhill JK, Mrugala M, Peacock DL, Lai A, Jusenius K, Wardlaw JM, Cloughesy T, Spence AM, Rockne R, Alvord EC Jr, Swanson KR (2009) Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice. Cancer Res 69(23):846–856

    Article  Google Scholar 

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Acknowledgements

AS would like to acknowledge funding from an NSERC CGS D3 scholarship and an Alberta Innovates Technology Futures Graduate Student Scholarship. Both TH and JB are supported through NSERC discovery Grants. The DTI images were acquired through an Alberta Cancer Foundation sponsored grant aimed at using DTI imaging to help predict the pattern of glioma growth.

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Correspondence to Amanda Swan.

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Swan, A., Hillen, T., Bowman, J.C. et al. A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 80, 1259–1291 (2018). https://doi.org/10.1007/s11538-017-0271-8

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  • DOI: https://doi.org/10.1007/s11538-017-0271-8

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