Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review
Year: 2024
Type: review
Abstract: Background and purpose: Despite multimodal treatment of glioblastoma, recurrence beyond the initial tumor volume is inevitable. Moreover, conventional MRI has shortcomings that hinder the early detection of occult white matter tract infiltration by tumor, but diffusion tensor imaging (DTI) is a sensitive probe for assessing microstructural changes, facilitating the identification of progression before standard imaging. This sensitivity makes DTI a valuable tool for predicting recurrence. A systematic review was therefore conducted to investigate how DTI, in comparison to conventional MRI, can be used for predicting glioblastoma progression. Methods: We queried three databases (PubMed, Web of Science, and Scopus) using the search terms: (diffusion tensor imaging OR DTI) AND (glioblastoma OR GBM) AND (recurrence OR progression). For included studies, data pertaining to the study type, number of glioblastoma recurrence patients, treatment type(s), and DTI-related metrics of recurrence were extracted. Results: In all, 16 studies were included, from which there were 394 patients in total. Six studies reported decreased fractional anisotropy in recurrence regions, and 2 studies described the utility of connectomics/tractography for predicting tumor migratory pathways to a site of recurrence. Three studies reported evidence of tumor progression using DTI before recurrence was visible on conventional imaging. Conclusions: These findings suggest that DTI metrics may be useful for guiding surgical and radiotherapy planning for glioblastoma patients, and for informing long-term surveillance. Understanding the current state of the literature pertaining to these metrics’ trends is crucial, particularly as DTI is increasingly used as a treatment-guiding imaging modality.
Source: Apollo (University of Cambridge)
DOI: 10.17863/cam.113420
Authors: Cozzi, Francesca, Mayrand, Roxanne, Wan, Yizhou, Price, Stephen
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6925283709
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