Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment

Abstract Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments’ effects, takin...

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Autores principales:	Ahmed Elazab, Hongmin Bai, Yousry M. Abdulazeem, Talaat Abdelhamid, Sijie Zhou, Kelvin K. L. Wong, Qingmao Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a5d08776ed8437195f67b87192e0334
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spelling	oai:doaj.org-article:3a5d08776ed8437195f67b87192e03342021-12-02T16:08:22ZPost-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment10.1038/s41598-017-01189-22045-2322https://doaj.org/article/3a5d08776ed8437195f67b87192e03342017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01189-2https://doaj.org/toc/2045-2322Abstract Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments’ effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies.Ahmed ElazabHongmin BaiYousry M. AbdulazeemTalaat AbdelhamidSijie ZhouKelvin K. L. WongQingmao HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Elazab Hongmin Bai Yousry M. Abdulazeem Talaat Abdelhamid Sijie Zhou Kelvin K. L. Wong Qingmao Hu Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
description	Abstract Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments’ effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies.
format	article
author	Ahmed Elazab Hongmin Bai Yousry M. Abdulazeem Talaat Abdelhamid Sijie Zhou Kelvin K. L. Wong Qingmao Hu
author_facet	Ahmed Elazab Hongmin Bai Yousry M. Abdulazeem Talaat Abdelhamid Sijie Zhou Kelvin K. L. Wong Qingmao Hu
author_sort	Ahmed Elazab
title	Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
title_short	Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
title_full	Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
title_fullStr	Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
title_full_unstemmed	Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment
title_sort	post-surgery glioma growth modeling from magnetic resonance images for patients with treatment
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/3a5d08776ed8437195f67b87192e0334
work_keys_str_mv	AT ahmedelazab postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT hongminbai postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT yousrymabdulazeem postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT talaatabdelhamid postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT sijiezhou postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT kelvinklwong postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment AT qingmaohu postsurgerygliomagrowthmodelingfrommagneticresonanceimagesforpatientswithtreatment
_version_	1718384522550050816

Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment

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