Repeatability of an MRI protocol for generating habitats based on cellularity, perfusion, and hypoxia in a murine model of glioma

Background Hypoxia, cellularity, and vascularity are well-established as important drivers of tumor growth and treatment response. Diffusion weighted (DW-), dynamic contrast enhanced magnetic (DCE-), and oxygen enhanced (OE-) MRI techniques have been developed to quantitatively map these three characteristics. We have developed and implemented a multi-parametric MRI protocol to identify tumor subregions that share similar biological features or “habitats”. Methods We performed test-retest DW-, DCE-, and OE-MRI measurements on Wistar rats bearing a C6 glioma ( n = 24). MRI data was summarized as apparent diffusion coefficient ( ADC) maps from DWI-MRI, area under the curve ( AUC ) from DCE-MRI, and normalized change in T ₁ (Δ T ₁ ) from OE-MRI. We then applied a data driven and k-means clustering to the aggregated dataset to identify distinct habitats within each animal. We employed four different approaches to partition the data, with each defined by a different number of habitats. The imaging data were partitioned into one of five habitats: three normoxic habitats (low cellularity–low perfusion, low cellularity–high perfusion, and high cellularity–high perfusion), one hypoxic habitat, and one necrotic habitat. Results Across all habitat construction approaches, Pearson and concordance correlation coefficients were both above 0.90 when comparing the test and retest habitat compositions. Conclusion The combination of the MRI protocol and our data-driven approach can reliably generate habitats that capture distinct cellularity, perfusion-related information, and hypoxia patterns with a high degree of repeatability.