What type of study is this?

October 1, 2025Open Access

A Novel Vascular Model Yields Increased MR Perfusion Metrics Compared to Conventional Dynamic Susceptibility Contrast Algorithms In Untreated Glioblastoma

Key Points

The novel vascular model shows significantly higher perfusion metrics than conventional algorithms in untreated glioblastoma, enhancing diagnostic accuracy.
Measurements like normalized cerebral blood flow, cerebral blood volume, and mean transit time were all significantly elevated using the new model.
Assessment was performed by two neuroradiologists, ensuring reliability of results with inter-rater agreement coefficients above 0.86.
Prospective multicenter studies are needed to further validate findings, especially regarding clinical decision-making and treatment monitoring.

Abstract

Abstract Background Malignant gliomas are heterogeneous brain tumors with extensive neovascularization. Conventional gradient-echo dynamic susceptibility contrast (GRE-DSC) perfusion MRI may underestimate microvascular alterations. We hypothesized that a novel vascular model (NVM), based on Bayesian voxel-wise transit time distribution analysis, could yield higher perfusion metrics in untreated isocitrate dehydrogenase (IDH)-wild-type glioblastoma compared to standard vendor GRE-DSC algorithms. Methods In this retrospective, single-center study, 89 patients with neuropathologically confirmed glioblastoma underwent pretherapeutic GRE-DSC perfusion MRI at 1.5 or 3.0 T. Perfusion maps were generated using both the NVM and default vendor algorithms. Using co-registered T1-post-contrast and T2/FLAIR images, two neuroradiologists independently assessed perfusion conspicuity of color-coded maps for each algorithm and manually performed region-of-interest analyses within visually identified tumor hotspots for quantification. Relative values of cerebral blood flow (rCBF), cerebral blood volume (rCBV), and mean transit time (rMTT) were normalized to contralateral normal-appearing white matter. Nonparametric tests evaluated group differences. Results The NVM yielded enhanced hotspot delineation and significantly higher median normalized perfusion values than vendor algorithms (all p 0.001), with excellent inter-rater reliability (Cohen’s κ and intraclass correlation coefficients ≥0.86). At 3.0 T, NVM-derived rCBV was significantly higher than at 1.5 T (p = 0.008). Conclusions NVM post-processing yielded higher normalized CBF, CBV and MTT values within tumor hotspots than vendor pipelines, suggesting that Bayesian model-based perfusion analysis may enhance the detection of microvascular changes in glioblastoma. As validation against a gold standard is missing, prospective multicenter studies are warranted to confirm our findings, particularly with regard to treatment monitoring and clinical decision-making.

Read Full Paperexternally

问 AI

Bookmark

View Full Paper