Graph-based stochastic modelling of glioblastoma invasion using patient-specific structural brain connectomes

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor in adults with extremely poor prognosis. Complete surgical treatment is practically impossible, as the true extent of GBM infiltration cannot be fully delineated using currently available in vivo neuroimaging methods, leading to frequent recurrences and low overall survival. Consequently, mathematical models are being developed to estimate the GBM expanse beyond the visible tumor mass, providing additional information for treatment planning and patient prognosis. Here, a novel graph-based stochastic mathematical model of GBM invasion using patient-specific structural brain connectome data is proposed. The model is assessed using publicly available UCSF-PDGM dataset to demonstrate GBM invasion dynamics across multiple patients and anatomical locations. Additional scaling using fractional anisotropy (FA) is tested and evaluated. Parameter sensitivity analysis is provided to explore model's behavior under different settings. Ablation testing is performed to suppress model mechanisms utilizing the structural connectome, showing that the tentacle-like extrusions from the tumor core emerge only if the patient-specific connectome is utilized. The model seems to capture GBM micro-infiltration along white matter tracts to a very high degree, making it a potential tool for studying distant recurrences farther from the resection cavity and GBM invasion dynamics in relation to the structural connectome. Full source code is publicly available, ensuring complete transparency of the study.