High-grade gliomas (HGGs) are a type of malignant brain cancer, which include glioblastomas (GBMs). The
median survival for patients diagnosed with GBMs is less than 15 months. The inability to accurately determine
the full extent of the tumor-invaded regions in the brain is assumed to be the reason for the incurability of
GBMs. In radiotherapy, the microscopic infiltration of normal tissue by tumor cells near the GTV is accounted
for by extending the target into a clinical target volume (CTV). Despite a generous margin of 15 mm, the
persistent recurrence of GBMs following treatment indicates that conventional CTV delineations fail to
encompass the entirety of the tumor cell distribution. To improve the CTV delineation and possibly treatment of
GBMs, novel approaches in determining the tumor infiltrated regions have been suggested in the form of
mathematical modeling.
The aim of this project is to develop a mathematical model for the infiltration of glioma cells into normal brain
tissue and implement it into a framework for predicting the full extent of tumor-invaded tissue for HGGs. By
applying the model to a large dataset, the behavior of the model could be investigated statistically, and optimal
input parameters determined. The results of the tumor invasion simulations were compared in terms of volumes
to the conventionally delineated CTVs, which were found not to adhere to the pathways of the simulated spread.
Additionally, the resulting simulated invasions were to predict the overall survival (OS) of the same cohort of
cases. OS prediction was better predicted by the simulated volumes of the tumor spread than the size of the
GTV. The results showed the potential of improving OS prediction and furthermore demonstrated a new
methodology for indirect model verification that does not rely on histopathological data.