Literature DB >> 20042359

Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins.

Ender Konukoglu1, Olivier Clatz, Pierre-Yves Bondiau, Hervé Delingette, Nicholas Ayache.   

Abstract

Radiotherapy for brain glioma treatment relies on magnetic resonance (MR) and computed tomography (CT) images. These images provide information on the spatial extent of the tumor, but can only visualize parts of the tumor where cancerous cells are dense enough, masking the low density infiltration. In radiotherapy, a 2 m constant margin around the tumor is taken to account for this uncertainty. This approach however, does not consider the growth dynamics of gliomas, particularly the differential motility of tumor cells in the white and in the gray matter. In this article, we propose a novel method for estimating the full extent of the tumor infiltration starting from its visible mass in the patients' MR images. This estimation problem is a time independent problem where we do not have information about the temporal evolution of the pathology nor its initial conditions. Based on the reaction-diffusion models widely used in the literature, we derive a method to solve this extrapolation problem. Later, we use this formulation to tailor new tumor specific variable irradiation margins. We perform geometrical comparisons between the conventional constant and the proposed variable margins through determining the amount of targeted tumor cells and healthy tissue in the case of synthetic tumors. Results of these experiments suggest that the variable margin could be more effective at targeting cancerous cells and preserving healthy tissue. Copyright 2009 Elsevier B.V. All rights reserved.

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Year:  2009        PMID: 20042359     DOI: 10.1016/j.media.2009.11.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  29 in total

1.  Deformable registration of glioma images using EM algorithm and diffusion reaction modeling.

Authors:  Ali Gooya; George Biros; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2010-09-27       Impact factor: 10.048

2.  Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity.

Authors:  Hamed Akbari; Luke Macyszyn; Xiao Da; Ronald L Wolf; Michel Bilello; Ragini Verma; Donald M O'Rourke; Christos Davatzikos
Journal:  Radiology       Date:  2014-06-19       Impact factor: 11.105

3.  Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

Authors:  Saima Rathore; Hamed Akbari; Jimit Doshi; Gaurav Shukla; Martin Rozycki; Michel Bilello; Robert Lustig; Christos Davatzikos
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-01

4.  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

5.  Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Authors:  Andreas Mang; Spyridon Bakas; Shashank Subramanian; Christos Davatzikos; George Biros
Journal:  Annu Rev Biomed Eng       Date:  2020-06-04       Impact factor: 9.590

6.  Coupling brain-tumor biophysical models and diffeomorphic image registration.

Authors:  Klaudius Scheufele; Andreas Mang; Amir Gholami; Christos Davatzikos; George Biros; Miriam Mehl
Journal:  Comput Methods Appl Mech Eng       Date:  2019-01-07       Impact factor: 6.756

7.  WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.

Authors:  Shashank Subramanian; Klaudius Scheufele; Miriam Mehl; George Biros
Journal:  Inverse Probl       Date:  2020-02-26       Impact factor: 2.407

8.  GLISTR: glioma image segmentation and registration.

Authors:  Ali Gooya; Kilian M Pohl; Michel Bilello; Luigi Cirillo; George Biros; Elias R Melhem; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2012-08-13       Impact factor: 10.048

9.  Mathematical Oncology: How Are the Mathematical and Physical Sciences Contributing to the War on Breast Cancer?

Authors:  Arnaud H Chauviere; Haralampos Hatzikirou; John S Lowengrub; Hermann B Frieboes; Alastair M Thompson; Vittorio Cristini
Journal:  Curr Breast Cancer Rep       Date:  2010-07-22

10.  Hypoxic cell waves around necrotic cores in glioblastoma: a biomathematical model and its therapeutic implications.

Authors:  Alicia Martínez-González; Gabriel F Calvo; Luis A Pérez Romasanta; Víctor M Pérez-García
Journal:  Bull Math Biol       Date:  2012-11-14       Impact factor: 1.758

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