The Association of Fractal Dimension with Vascularity and Clinical Outcomes in Glioblastoma.

Background: Growing evidence indicates fractal analysis (FA) has potential as a computational tool to assess tumor microvasculature in glioblastoma (GBM). As fractal parameters of microvasculature have shown to be reliable quantitative biomarkers in brain tumors, there has been similar success in measuring the architecture of tumor tissue using FA in other tumor types. However, evaluating fractal parameters of tissue structure in relation to the microvasculature has not yet been implemented in GBM. We aimed to assess the utility of this methodology in quantifying structural characteristics of GBM cytoarchitecture and vascularity by correlating fractal parameters with gene expression.

Methods: Formalin-fixed paraffin-embedded specimens were retrospectively collected from 43 patients following resection of a newly diagnosed GBM; 4 normal brain specimens were obtained from epilepsy surgeries as controls. Tumor samples were processed using FA employing a software-based box-counting method algorithm and custom messenger RNA expression assays. Fractal parameters were then correlated with clinical features, outcomes, and a panel of 92 genes associated with vascularity and angiogenesis.

Results: Statistical analysis demonstrated that fractal-based indices were not adequate parameters for distinction of GBM cytoarchitecture compared with normal brain specimens. Correlation analysis of our gene expression findings suggested that hematoxylin and eosin-based FA may have adequate sensitivity to detect associations with vascular gene expression.

Conclusions: The combination of neuropathological assessment and histology does not provide optimized data for FA in GBM. However, an association between FA and gene expression in GBM of genes pertaining to cytoarchitecture and angiogenesis warrants further investigation.