Detecting glioblastoma infiltration beyond conventional imaging tumour margins using MTE-NODDI

Glioblastoma (GBM) is the most common and aggressive brain tumour with starkresistance to available therapies, leading to relapse and a median survival of<15 months. A key cause of therapy resistance is diffuse infiltration oftumour cells into brain regions surrounding the tumour, which presents a majorclinical challenge as existing imaging techniques offer limited detection of theresectable margin. Here, we use diffusion weighted imaging (DWI) and apply themultiple echo time neurite orientation dispersion and density imaging(MTE-NODDI) model as a tool to detect tumour cells in the hard-to-distinguishmargin. We used the G144 patient-derived xenograft model, with characteristicinvasion along white matter tracts, in combination with MTE-NODDI. Tumourdevelopment was monitored, and magnetic resonance imaging (MRI) data wereacquired over a 4-week period, starting at 4 weeks after stereotactic injectionof tumour cells. MTE-NODDI demonstrated sensitivity to the developing tumour inthe invading margin, and changes in measured parameters were apparent from 6weeks after injection. In comparison to standard DWI, MTE-NODDI showed increasedsensitivity to the tumour-associated changes in the margin. Furthermore,extraneurite volume fraction (f_en ) and neuritedensity index (NDI) measured from MTE-NODDI correlated with immunohistologicalmeasurement of tumour cells. These findings suggest that MTE-NODDI maynon-invasively detect infiltrating cells and tumour-induced pathology in marginregions without T2 or DWI changes in a patient-derived mouse model of GBM.MTE-NODDI is clinically translatable and could be a powerful tool forneurosurgeons to maximise surgical resection, resulting in better survivaloutcomes for patients with GBM.