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Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma
Tumor Treating Fields (TTFields) therapy is an approved treatment that has known clinical efficacy against recurrent and newly diagnosed glioblastoma. However, the distribution of the electric fields and the corresponding pattern of energy deposition in the brain are poorly understood. To evaluate t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5463092/ https://www.ncbi.nlm.nih.gov/pubmed/28544575 http://dx.doi.org/10.1002/cam4.1095 |
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author | Lok, Edwin San, Pyay Hua, Van Phung, Melissa Wong, Eric T. |
author_facet | Lok, Edwin San, Pyay Hua, Van Phung, Melissa Wong, Eric T. |
author_sort | Lok, Edwin |
collection | PubMed |
description | Tumor Treating Fields (TTFields) therapy is an approved treatment that has known clinical efficacy against recurrent and newly diagnosed glioblastoma. However, the distribution of the electric fields and the corresponding pattern of energy deposition in the brain are poorly understood. To evaluate the physical parameters that may influence TTFields, postacquisition MP‐RAGE, T1 and T2 MRI sequences from a responder with a right parietal glioblastoma were anatomically segmented and then solved using finite‐element method to determine the distribution of the electric fields and rate of energy deposition at the gross tumor volume (GTV) and other intracranial structures. Electric field–volume histograms (EVH) and specific absorption rate–volume histograms (SARVH) were constructed to numerically evaluate the relative and/or absolute magnitude volumetric differences between models. The electric field parameters E(AUC), V(E) (150), E(95%), E(50%), and E(20%), as well as the SAR parameters SAR(AUC), V(SAR) (7.5), SAR (95%), SAR (50%), and SAR (20%), facilitated comparisons between models derived from various conditions. Specifically, TTFields at the GTV were influenced by the dielectric characteristics of the adjacent tissues as well as the GTV itself, particularly the presence or absence of a necrotic core. The thickness of the cerebrospinal fluid on the convexity of the brain and the geometry of the tumor were also relevant factors. Finally, the position of the arrays also influenced the electric field distribution and rate of energy deposition in the GTV. Using EVH and SARVH, a personalized approach for TTFields treatment can be developed when various patient‐related and tumor‐related factors are incorporated into the planning procedure. |
format | Online Article Text |
id | pubmed-5463092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54630922017-06-09 Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma Lok, Edwin San, Pyay Hua, Van Phung, Melissa Wong, Eric T. Cancer Med Clinical Cancer Research Tumor Treating Fields (TTFields) therapy is an approved treatment that has known clinical efficacy against recurrent and newly diagnosed glioblastoma. However, the distribution of the electric fields and the corresponding pattern of energy deposition in the brain are poorly understood. To evaluate the physical parameters that may influence TTFields, postacquisition MP‐RAGE, T1 and T2 MRI sequences from a responder with a right parietal glioblastoma were anatomically segmented and then solved using finite‐element method to determine the distribution of the electric fields and rate of energy deposition at the gross tumor volume (GTV) and other intracranial structures. Electric field–volume histograms (EVH) and specific absorption rate–volume histograms (SARVH) were constructed to numerically evaluate the relative and/or absolute magnitude volumetric differences between models. The electric field parameters E(AUC), V(E) (150), E(95%), E(50%), and E(20%), as well as the SAR parameters SAR(AUC), V(SAR) (7.5), SAR (95%), SAR (50%), and SAR (20%), facilitated comparisons between models derived from various conditions. Specifically, TTFields at the GTV were influenced by the dielectric characteristics of the adjacent tissues as well as the GTV itself, particularly the presence or absence of a necrotic core. The thickness of the cerebrospinal fluid on the convexity of the brain and the geometry of the tumor were also relevant factors. Finally, the position of the arrays also influenced the electric field distribution and rate of energy deposition in the GTV. Using EVH and SARVH, a personalized approach for TTFields treatment can be developed when various patient‐related and tumor‐related factors are incorporated into the planning procedure. John Wiley and Sons Inc. 2017-05-23 /pmc/articles/PMC5463092/ /pubmed/28544575 http://dx.doi.org/10.1002/cam4.1095 Text en © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Clinical Cancer Research Lok, Edwin San, Pyay Hua, Van Phung, Melissa Wong, Eric T. Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title | Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title_full | Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title_fullStr | Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title_full_unstemmed | Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title_short | Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma |
title_sort | analysis of physical characteristics of tumor treating fields for human glioblastoma |
topic | Clinical Cancer Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5463092/ https://www.ncbi.nlm.nih.gov/pubmed/28544575 http://dx.doi.org/10.1002/cam4.1095 |
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