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Quantifying the Role of Anisotropic Invasion in Human Glioblastoma

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Computational Surgery and Dual Training

Abstract

Gliomas are highly invasive primary brain tumors, notorious for their recurrence after treatment, and are considered uniformly fatal. Confounding progress is the fact that there is a diffuse extent of tumor cell invasion well beyond what is visible on routine clinical imaging such as MRI. By incorporating diffusion tensor imaging (DTI) which shows the directional orientation of fiber tracts in the brain, we compare patient-specific model simulations to observed tumor growth for two patients, visually, volumetrically and spatially to quantify the effect of anisotropic diffusion on the ability to predict the actual shape and diffuse invasion of tumor as observed on MRI. The ultimate goal is the development of the best patient-specific tool for predicting brain tumor growth and invasion in individual patients, which can aid in treatment planning.

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References

  1. Burnet NG, Lynch AG, Jefferies SJ, Price SJ, Jones PH, Antoun NM, Xuereb JH, Pohl U (2007) High grade glioma: imaging combined with pathological grade defines management and predicts prognosis. Radiother Oncol 85(3):371–378

    Article  Google Scholar 

  2. Fisher RA (1937) The wave of advance of advantageous genes. Ann Eugenics 7:355–369

    Article  Google Scholar 

  3. Harpold HL, Alvord EC, Swanson KR (2007) The evolution of mathematical modeling of glioma proliferation and invasion. J Neuropathol Exp Neurol 66(1):1–9

    Article  Google Scholar 

  4. Horsfield MA, Jones DK (2002) Applications of diffusion-weighted and diffusion tensor mri to white matter diseases—a review. NMR Biomed 15(7–8):570–577

    Article  Google Scholar 

  5. Jbabdi S, Mandonnet E, Duffau H, Capelle L, Swanson KR, Pélégrini-Issac, M, Guillevin R, Benali H (2005) Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn Reson Med 54(3):616–624

    Article  Google Scholar 

  6. 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 Imag 13(4):534–546

    Article  Google Scholar 

  7. LeVeque RJ (2007) Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems. Society for Industrial and Applied Mathematics, Philadelphia, PA, 2007.

    Book  Google Scholar 

  8. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 who classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109

    Article  Google Scholar 

  9. MATLAB (2010) The MathWorks, Inc. https://www.mathworks.com. Accessed May 2010

  10. Mori S (2011) In-vivo human database –adam dti. https://cmrm.med.jhmi.edu. Accessed May 2010

  11. Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, Fahlbusch R (2005) Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 56(1):130–137; Discussion 138

    Google Scholar 

  12. Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, Lai A, Cloughesy T, Alvord EC, Swanson KR (2010) Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach. Phys Med Biol 55(12):3271–385

    Article  Google Scholar 

  13. Stadlbauer A, Pólking E, Prante O, Nimsky C, Buchfelder M, Kuwert T, Linke R, Doelken M, Ganslandt O (2009) Detection of tumour invasion into the pyramidal tract in glioma patients with sensorimotor deficits by correlation of (18)f-fluoroethyl-l: -tyrosine pet and magnetic resonance diffusion tensor imaging. Acta Neurochir (Wien) 151(9):1061–1069

    Article  Google Scholar 

  14. Statistical Parametric Mapping 8 (SPM8) (2010) Wellcome trust centre for neuroimaging. http:www.fil.ion.ucl.ac.uk/spm

  15. Swanson KR (1999) Mathematical Modeling of the Growth and Control of Tumors. Phd Thesis, University of Washington

    Google Scholar 

  16. Swanson KR, Alvord EC, Murray JD (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif, 33(5):317–329

    Article  Google Scholar 

  17. Swanson KR, Alvord EC, Murray JD (2002) Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy. Br J Canc 86(1): 14–18

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Swanson KR, Chakraborty G, Wang CH, Rockne R, Harpold HL, Muzi M, Adamsen TC, Krohn KA, Spence AM (2009) Complementary but distinct roles for mri and 18f-fluoromisonidazole pet in the assessment of human glioblastomas. J Nucl Med 50(1):36–44

    Article  Google Scholar 

  20. Szeto MD, Chakraborty G, Hadley J, Rockne R, Muzi M, Alvord EC, Krohn KA, Spence AM, Swanson KR (2009) Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by mri with hypoxia assessed by fmiso-pet in newly diagnosed glioblastomas. Canc Res 69(10):4502–4509

    Article  Google Scholar 

  21. Tracqui P, Cruywagen GC, Woodward DE, Bartoo GT, Murray JD, Alvord EC (1995) A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth. Cell Prolif 28(1):17–31

    Article  Google Scholar 

  22. Wang CH, Rockhill JK, Mrugala M, Peacock DL, Lai A, Jusenius K, Wardlaw JM, Cloughesy T, Spence AM, Rockne R, Alvord EC, Swanson KR (2009) Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. Canc Res 69(23):9133–9140

    Article  Google Scholar 

  23. Witwer BP, Moftakhar R, Hasan KM, Deshmukh P, Haughton V, Field A, Arfanakis K, Noyes J, Moritz CH, Meyerand ME, Rowley HA, Alexander AL, Badie B (2002) Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 97(3):568–575

    Article  Google Scholar 

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Acknowledgments

We gratefully acknowledge the generous and timely support of the McDonnell Foundation, the Dana Foundation, the Academic Pathology Fund, the NIH/NINDS R01 NS060752 and the NIH/NCI Moffitt-UW Physical Sciences Oncology Center U54 CA143970.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Washington, 1959 NE Pacific, St. Seattle, WA, 98195, USA

    R. Sodt

  2. Department of Neurological Surgery, Northwestern University, Feinberg School of Medicine, 676 N. St. Clair Street Suite 1300, Chicago, IL, 60611, USA

    R. Rockne & K R. Swanson

  3. Department of Applied Mathematics, University of Washington, Washington, USA

    R. Rockne & K R. Swanson

  4. Department of Pathology, Department of Medical Education and Biomedical Informatics, University of Washington, Washington, USA

    M. L. Neal

  5. Department of Radiation Oncology and Department of Medical Education and Biomedical Informatics, University of Washington, Washington, USA

    I. Kalet

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  1. R. Sodt
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  2. R. Rockne
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  3. M. L. Neal
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  4. I. Kalet
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  5. K R. Swanson
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Corresponding author

Correspondence to K R. Swanson .

Editor information

Editors and Affiliations

  1. Dept of Computer Science, University of Houston, Houston, Texas, USA

    Marc Garbey

  2. Dept. Surgery, The Methodist Hospital Research Institut, Houston, Texas, USA

    Barbara Lee Bass

  3. Department of Surgery, University of Florida, Gainesville, Florida, USA

    Scott Berceli

  4. Labo. Sciences de l'Images, de l'Informatique et de la Télédétection, Université Strasbourg I ENSPS, Illkirch, France

    Christophe Collet

  5. Biomedical Engineering Department, Politecnico di Milano, Milano, Italy

    Pietro Cerveri

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Sodt, R., Rockne, R., Neal, M.L., Kalet, I., Swanson, K.R. (2014). Quantifying the Role of Anisotropic Invasion in Human Glioblastoma. In: Garbey, M., Bass, B., Berceli, S., Collet, C., Cerveri, P. (eds) Computational Surgery and Dual Training. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8648-0_20

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  • DOI: https://doi.org/10.1007/978-1-4614-8648-0_20

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8647-3

  • Online ISBN: 978-1-4614-8648-0

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