Visualising spatial heterogeneity in glioblastoma using imaging habitats
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Authors
Waqar, M.Van Houdt, P. J.
Hessen, E.
Li, K. L.
Zhu, X.
Jackson, A.
Iqbal, M.
O'Connor, James P B
Djoukhadar, I.
van der Heide, U. A.
Coope, D. J.
Borst, Gerben R
Affiliation
Department of Neurosurgery, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, ManchesterIssue Date
2022
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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.Citation
Waqar M, Van Houdt PJ, Hessen E, Li KL, Zhu X, Jackson A, et al. Visualising spatial heterogeneity in glioblastoma using imaging habitats. Frontiers in oncology. 2022;12:1037896. PubMed PMID: 36505856. Pubmed Central PMCID: PMC9731157. Epub 2022/12/13. eng.Journal
Frontiers in OncologyDOI
10.3389/fonc.2022.1037896PubMed ID
36505856Additional Links
https://dx.doi.org/10.3389/fonc.2022.1037896Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.3389/fonc.2022.1037896