Speaker
Description
The pathways of glioblastoma progression involve a complicated association among tumor cells, immune factors, vascular systems, and metabolite reorganization. Despite the use of mathematical models, which have been used to capture individual aspects, the gap is the approach of having a combined framework. We present a Multi-scale- Multi-processing Combined Glioblastoma Mathematical Model. This multi-scale model connects the tumor proliferation-invasion, immune response, integrity of the blood-brain barrier, angiogenesis triggered by hypoxia, and competition within the metabolism. The system of PDEs is solved using various mathematical methods. This model can be employed to simulate the best sequence of drugs to be used in combination, and one of the possible treatment methods is lactate modulation. The given Model is a confirmed platform for individualizing treatment optimization in glioblastoma, relying on clinical data.
The findings enable the understanding of the processes that control the development of glioma and the identification of major parameters that impact the growth of tumors and their interactions with the microenvironment. This framework can help to create predictive computational models of glioma evolution and be further expanded to incorporate the use of medical imaging data and include physics-informed neural networks to predict time-dependent tumor growth in humans.
Bibliography
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