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Visualising spatial heterogeneity in glioblastoma using imaging habitats
Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glio...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731157/ https://www.ncbi.nlm.nih.gov/pubmed/36505856 http://dx.doi.org/10.3389/fonc.2022.1037896 |
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author | Waqar, Mueez Van Houdt, Petra J. Hessen, Eline Li, Ka-Loh Zhu, Xiaoping Jackson, Alan Iqbal, Mudassar O’Connor, James Djoukhadar, Ibrahim van der Heide, Uulke A. Coope, David J. Borst, Gerben R. |
author_facet | Waqar, Mueez Van Houdt, Petra J. Hessen, Eline Li, Ka-Loh Zhu, Xiaoping Jackson, Alan Iqbal, Mudassar O’Connor, James Djoukhadar, Ibrahim van der Heide, Uulke A. Coope, David J. Borst, Gerben R. |
author_sort | Waqar, Mueez |
collection | PubMed |
description | Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma’s imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or ‘habitats’ based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications. |
format | Online Article Text |
id | pubmed-9731157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97311572022-12-09 Visualising spatial heterogeneity in glioblastoma using imaging habitats Waqar, Mueez Van Houdt, Petra J. Hessen, Eline Li, Ka-Loh Zhu, Xiaoping Jackson, Alan Iqbal, Mudassar O’Connor, James Djoukhadar, Ibrahim van der Heide, Uulke A. Coope, David J. Borst, Gerben R. Front Oncol Oncology Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma’s imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or ‘habitats’ based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications. Frontiers Media S.A. 2022-11-24 /pmc/articles/PMC9731157/ /pubmed/36505856 http://dx.doi.org/10.3389/fonc.2022.1037896 Text en Copyright © 2022 Waqar, Van Houdt, Hessen, Li, Zhu, Jackson, Iqbal, O’Connor, Djoukhadar, van der Heide, Coope and Borst https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Waqar, Mueez Van Houdt, Petra J. Hessen, Eline Li, Ka-Loh Zhu, Xiaoping Jackson, Alan Iqbal, Mudassar O’Connor, James Djoukhadar, Ibrahim van der Heide, Uulke A. Coope, David J. Borst, Gerben R. Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title | Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title_full | Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title_fullStr | Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title_full_unstemmed | Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title_short | Visualising spatial heterogeneity in glioblastoma using imaging habitats |
title_sort | visualising spatial heterogeneity in glioblastoma using imaging habitats |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731157/ https://www.ncbi.nlm.nih.gov/pubmed/36505856 http://dx.doi.org/10.3389/fonc.2022.1037896 |
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