Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas

Coupling time series of MR Images with reaction-diffusion-based models has provided interesting ways to better understand the proliferative-invasive aspect of glial cells in tumors. In this paper, we address a different formulation of the inverse problem: from a single time point image of a non-swollen brain tumor, estimate the tumor source location and the diffusivity ratio between white and gray matter, while exploring the possibility to predict the further extent of the observed tumor at later time points in low-grade gliomas. The synthetic and clinical results show the stability of the located source and its varying distance from the tumor barycenter and how the estimated ratio controls the spikiness of the tumor.

[1]  L. Deangelis,et al.  Brain Tumors , 2019, Imaging Gliomas After Treatment.

[2]  C. Vecht,et al.  The management of brain edema in brain tumors , 2004, Current opinion in oncology.

[3]  J. Murray,et al.  A quantitative model for differential motility of gliomas in grey and white matter , 2000, Cell proliferation.

[4]  Peter Canoll,et al.  Magnetic Resonance Imaging Characteristics of Glioblastoma Multiforme: Implications for Understanding Glioma Ontogeny , 2010, Neurosurgery.

[5]  S Torquato,et al.  Emergence of a Subpopulation in a Computational Model of Tumor Growth , 2022 .

[6]  Christos Davatzikos,et al.  Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model , 2011, MICCAI.

[7]  Hervé Delingette,et al.  A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling , 2007, IPMI.

[8]  Kristin R. Swanson,et al.  The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion , 2007, Journal of neuropathology and experimental neurology.

[9]  Hervé Delingette,et al.  Predicting the Location of Glioma Recurrence after a Resection Surgery , 2012, STIA.

[10]  M. Tovi,et al.  MR imaging in cerebral gliomas analysis of tumour tissue components. , 1993, Acta radiologica. Supplementum.

[11]  Angelo Iollo,et al.  SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS , 2012 .

[12]  Olivier Clatz,et al.  Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications , 2007 .

[13]  Christos Davatzikos,et al.  Deformable Registration of Glioma Images Using EM Algorithm and Diffusion Reaction Modeling , 2011, IEEE Transactions on Medical Imaging.

[14]  H Duffau,et al.  Correlations between molecular profile and radiologic pattern in oligodendroglial tumors , 2004, Neurology.

[15]  S. McWeeney,et al.  Cancer stem cell tumor model reveals invasive morphology and increased phenotypical heterogeneity. , 2010, Cancer research.

[16]  Sylvia Drabycz,et al.  An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging , 2010, NeuroImage.

[17]  A. Anderson,et al.  A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion , 2005 .

[18]  Hervé Delingette,et al.  Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation , 2005, IEEE Transactions on Medical Imaging.

[19]  Hervé Delingette,et al.  Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations , 2010, IEEE Transactions on Medical Imaging.

[20]  W. Marsden I and J , 2012 .

[21]  Albert Lai,et al.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. , 2009, Cancer research.

[22]  J. Murray,et al.  A mathematical model of glioma growth: the effect of chemotherapy on spatio‐temporal growth , 1995, Cell proliferation.

[23]  Hervé Delingette,et al.  Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins , 2010, Medical Image Anal..

[24]  M. Westphal,et al.  Migration of human glioma cells on myelin. , 1996, Neurosurgery.

[25]  Laurent Capelle,et al.  Continuous growth of mean tumor diameter in a subset of grade II gliomas , 2003, Annals of neurology.

[26]  R. Guillevin,et al.  Simulation of anisotropic growth of low‐grade gliomas using diffusion tensor imaging , 2005, Magnetic resonance in medicine.

[27]  H. Frieboes,et al.  Computer simulation of glioma growth and morphology , 2007, NeuroImage.

[28]  Hervé Delingette,et al.  Towards an Identification of Tumor Growth Parameters from Time Series of Images , 2007, MICCAI.

[29]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[30]  J. Murray,et al.  Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion , 2003, Journal of the Neurological Sciences.

[31]  Geoffrey S Young,et al.  Advanced MRI of adult brain tumors. , 2007, Neurologic clinics.

[32]  Christos Davatzikos,et al.  GLISTR: Glioma Image Segmentation and Registration , 2012, IEEE Transactions on Medical Imaging.

[33]  I. Whittle,et al.  The dilemma of low grade glioma , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[34]  H. Frieboes,et al.  Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth , 2007, NeuroImage.

[35]  M. Powell The BOBYQA algorithm for bound constrained optimization without derivatives , 2009 .

[36]  Mauro Ferrari,et al.  Multiparameter computational modeling of tumor invasion. , 2009, Cancer research.