A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma.

backgroundGlioblastoma (GBM) is a lethal tumor, actively growing and invading neighboring neural tissue. GBMs appear functionally connected to distributed and spatially distant regions rather than representing an isolated and passive lesion disrupting the brain circuitry. Moreover, increasing evidence suggests that white matter serves as the morphological substrate for GBM to progress and migrate to distant areas in the human brain.

methodsWe hypothesized that the subset of white matter tracts intersecting the tumors depicts the physical substrate for large-scale neuron-glioma interactions and would therefore inform prognosis. Using normative models, we design, analyze, interpret, and test a Lesion-Tract Density Index (L -TDI) marker that considers the distributed white matter pathways interacting with the tumor in 2 independent cohorts of N=367 and N=496 patients, respectively.

resultsFirst, we show that the tract density within this white matter map robustly stratifies survival rates due to widespread white matter involvement. Second, we demonstrate why tract density-based markers offer critical and necessary insights into the morphology, location, and evolution of human GBM by proving how the proposed L -TDI implicitly considers tumor volume, white matter density, and location. We provide further evidence that the non-uniform distribution of GBMs and their differential prognosis emerge from white matter morphology. Third, we validate the L -TDI marker with multiple Cox survival models and analyze its contribution in relation to other covariates of interest (eg, MGMT promoter methylation). Lastly, by using a simple logistic model, we predict patient death at 12 months with balanced accuracies of 68% and 65%, and areas under the curve of 0.74 and 0.73 when training and testing in separate and independent cohorts.

conclusionsOverall, we offer a concrete implementation of the emerging paradigm that views GBM not as a focal lesion, but as a network disease sh aped by its complex interactions with distant brain regions.