Literature DB >> 28493055

A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread.

Amanda Swan1, Thomas Hillen2, John C Bowman3, Albert D Murtha4.   

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.

Entities:  

Keywords:  Anisotropic diffusion; Gliomas; Mathematical medicine; Mathematical modelling; Partial differential equations

Mesh:

Year:  2017        PMID: 28493055     DOI: 10.1007/s11538-017-0271-8

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  14 in total

Review 1.  Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data.

Authors:  David A Hormuth; Angela M Jarrett; Ernesto A B F Lima; Matthew T McKenna; David T Fuentes; Thomas E Yankeelov
Journal:  JCO Clin Cancer Inform       Date:  2019-02

2.  Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Authors:  Jana Lipkova; Panagiotis Angelikopoulos; Stephen Wu; Esther Alberts; Benedikt Wiestler; Christian Diehl; Christine Preibisch; Thomas Pyka; Stephanie E Combs; Panagiotis Hadjidoukas; Koen Van Leemput; Petros Koumoutsakos; John Lowengrub; Bjoern Menze
Journal:  IEEE Trans Med Imaging       Date:  2019-02-27       Impact factor: 10.048

3.  Estimating intratumoral heterogeneity from spatiotemporal data.

Authors:  E M Rutter; H T Banks; K B Flores
Journal:  J Math Biol       Date:  2018-05-08       Impact factor: 2.259

4.  Lesion Dynamics Under Varying Paracrine PDGF Signaling in Brain Tissue.

Authors:  Susan Christine Massey; Andrea Hawkins-Daarud; Jill Gallaher; Alexander R A Anderson; Peter Canoll; Kristin R Swanson
Journal:  Bull Math Biol       Date:  2019-02-22       Impact factor: 1.758

5.  Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.

Authors:  Adrianne L Jenner; Munisha Smalley; David Goldman; William F Goins; Charles S Cobbs; Ralph B Puchalski; E Antonio Chiocca; Sean Lawler; Paul Macklin; Aaron Goldman; Morgan Craig
Journal:  iScience       Date:  2022-05-13

6.  Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results.

Authors:  Angela M Jarrett; David A Hormuth; Stephanie L Barnes; Xinzeng Feng; Wei Huang; Thomas E Yankeelov
Journal:  Phys Med Biol       Date:  2018-05-17       Impact factor: 3.609

7.  Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study.

Authors:  Markos Antonopoulos; Dimitra Dionysiou; Georgios Stamatakos; Nikolaos Uzunoglu
Journal:  BMC Bioinformatics       Date:  2019-08-27       Impact factor: 3.169

8.  From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.

Authors:  Jill A Gallaher; Susan C Massey; Andrea Hawkins-Daarud; Sonal S Noticewala; Russell C Rockne; Sandra K Johnston; Luis Gonzalez-Cuyar; Joseph Juliano; Orlando Gil; Kristin R Swanson; Peter Canoll; Alexander R A Anderson
Journal:  PLoS Comput Biol       Date:  2020-02-26       Impact factor: 4.475

9.  Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity.

Authors:  M-E Oraiopoulou; E Tzamali; G Tzedakis; E Liapis; G Zacharakis; A Vakis; J Papamatheakis; V Sakkalis
Journal:  Sci Rep       Date:  2018-11-01       Impact factor: 4.379

10.  Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling.

Authors:  David A Hormuth; Angela M Jarrett; Thomas E Yankeelov
Journal:  Radiat Oncol       Date:  2020-01-02       Impact factor: 3.481

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