A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread

被引:47
作者
Swan, Amanda [1 ]
Hillen, Thomas [2 ]
Bowman, John C. [1 ]
Murtha, Albert D. [3 ]
机构
[1] Univ Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada
[2] Univ Alberta, Dept Math & Stat Sci, Ctr Math Biol, Edmonton, AB T6G 2G1, Canada
[3] Cross Canc Inst, Edmonton, AB T6G 1Z2, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Mathematical medicine; Gliomas; Partial differential equations; Mathematical modelling; Anisotropic diffusion; GLIOMA GROWTH; INVASION MARGIN; SIMULATION; GLIOBLASTOMA; RADIOTHERAPY;
D O I
10.1007/s11538-017-0271-8
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:1259 / 1291
页数:33
相关论文
共 49 条
[1]   Diffusion tensor imaging of the brain [J].
Alexander, Andrew L. ;
Lee, Jee Eun ;
Lazar, Mariana ;
Field, Aaron S. .
NEUROTHERAPEUTICS, 2007, 4 (03) :316-329
[2]   On the closure of mass balance models for tumor growth [J].
Ambrosi, D ;
Preziosi, L .
MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES, 2002, 12 (05) :737-754
[3]   Modelling biological invasions: Individual to population scales at interfaces [J].
Belmonte-Beitia, J. ;
Woolley, T. E. ;
Scott, J. G. ;
Maini, P. K. ;
Gaffney, E. A. .
JOURNAL OF THEORETICAL BIOLOGY, 2013, 334 :1-12
[4]   Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging [J].
Bondiau, Pierre-Yves ;
Clatz, Olivier ;
Sermesant, Maxime ;
Marcy, Pierre-Yves ;
Delingette, Herve ;
Frenay, Marc ;
Ayache, Nicholas .
PHYSICS IN MEDICINE AND BIOLOGY, 2008, 53 (04) :879-893
[5]  
Burnet Neil G, 2004, Cancer Imaging, V4, P153, DOI 10.1102/1470-7330.2004.0054
[6]   Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation [J].
Clatz, O ;
Sermesant, M ;
Bondiau, PY ;
Delingette, H ;
Warfield, SK ;
Malandain, G ;
Ayache, N .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2005, 24 (10) :1334-1346
[7]   Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma [J].
Corwin, David ;
Holdsworth, Clay ;
Rockne, Russell C. ;
Trister, Andrew D. ;
Mrugala, Maciej M. ;
Rockhill, Jason K. ;
Stewart, Robert D. ;
Phillips, Mark ;
Swanson, Kristin R. .
PLOS ONE, 2013, 8 (11)
[8]  
Diaz I, 2013, IEEE ENG MED BIO, P3339, DOI 10.1109/EMBC.2013.6610256
[9]   A MULTISCALE MODEL FOR GLIOMA SPREAD INCLUDING CELL-TISSUE INTERACTIONS AND PROLIFERATION [J].
Engwer, Christian ;
Knappitsch, Markus ;
Surulescu, Christina .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2016, 13 (02) :443-460
[10]   Glioma follow white matter tracts: a multiscale DTI-based model [J].
Engwer, Christian ;
Hillen, Thomas ;
Knappitsch, Markus ;
Surulescu, Christina .
JOURNAL OF MATHEMATICAL BIOLOGY, 2015, 71 (03) :551-582