  In malignant primary brain tumors, cancer cells infiltrate into the
peritumoral brain structures which results in inevitable recurrence.
Quantitative assessment of infiltrative heterogeneity in the peritumoral
region, the area where biopsy or resection can be hazardous, is important for
clinical decision making. Previous work on characterizing the infiltrative
heterogeneity in the peritumoral region used various imaging modalities, but
information of extracellular free water movement restriction has been limitedly
explored. Here, we derive a unique set of Artificial Intelligence (AI)-based
markers capturing the heterogeneity of tumor infiltration, by characterizing
free water movement restriction in the peritumoral region using Diffusion
Tensor Imaging (DTI)-based free water volume fraction maps. A novel voxel-wise
deep learning-based peritumoral microenvironment index (PMI) is first extracted
by leveraging the widely different water diffusivity properties of
glioblastomas and brain metastases as regions with and without infiltrations in
the peritumoral tissue. Descriptive characteristics of locoregional hubs of
uniformly high PMI values are extracted as AI-based markers to capture distinct
aspects of infiltrative heterogeneity. The proposed markers are applied to two
clinical use cases on an independent population of 275 adult-type diffuse
gliomas (CNS WHO grade 4), analyzing the duration of survival among
Isocitrate-Dehydrogenase 1 (IDH1)-wildtypes and the differences with
IDH1-mutants. Our findings provide a panel of markers as surrogates of
infiltration that captures unique insight about underlying biology of
peritumoral microstructural heterogeneity, establishing them as biomarkers of
prognosis pertaining to survival and molecular stratification, with potential
applicability in clinical decision making.
