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Updated: Jun 25, 2026
Use of Cerebral Open-Flow Microperfusion for the Longitudinal Collection of Interstitial Fluid in an Animal Model of Glioblastoma
Published on: December 23, 2025
Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution
Gisele B Caseiras1, Sophie Chheang, James Babb
1Institute of Neurology, UCL, London SW1X 0HZ, UK. g.brasil@ion.ucl.ac.uk
Background/Purpose:
The prognostic value of defining subcategories of gliomas is still controversial. This study aims to determine the utility of relative cerebral blood volume (rCBV) in predicting clinical response in patients with low-grade glioma at multiple institutions.
Materials And Methods:
Sixty-nine patients were studied with dynamic susceptibility contrast-enhanced perfusion MRI at two institutions. The pathologic diagnoses of the low-grade gliomas were 34 astrocytomas, 20 oligodendroglioma, 9 oligoastrocytomas, 1 ganglioglioma and 5 with indeterminate histology. Wilcoxon tests were used to compare patients in different response categories with respect to baseline rCBV. Kaplan-Meier curve and log-rank tests were used to predict the association of rCBV with time to progression.
Results:
At both institutions, patients with an adverse event (progressive disease or death) had a significantly higher baseline rCBV than those without (complete response or stable disease) (p value=0.0138). The odds ratio for detecting an adverse event when using rCBV was 1.87 (95% confidence interval: 1.14-3.08). rCBV was significantly negatively associated with time to progression (p=0.005). The median time to progression among subjects with rCBV>1.75 was 365 days, while there was 95% confidence that the median time to progression was at least 889 days among subjects with rCBV<1.75.
Conclusion:
Our study suggests not only that rCBV measurements correlate well with time to progression or death, but also that the findings can be replicated across institutions, which supports the application of rCBV as an adjunct to pathology in predicting glioma biology.
