A Novel Approach for Radiotherapy and Radiosurgery Treatment Planning Accounting for High-Grade Glioma Invasiveness into Normal Tissue

Abstract: High-grade gliomas (HGGs) are a type of malignant brain cancer, which include glioblastomas (GBMs). In adults, GBM is the most common malignant primary brain cancer. Attempts to treat patients with GBMs have been conducted for over a century, but the prognosis has only marginally improved. Current standard treatment involves surgical resection of the gross tumor volume (GTV), followed by radiotherapy and chemotherapy. Despite the efforts, 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 in the vicinity of the GTV is accounted for by extending the target into a clinical target volume (CTV). Current recommended margin widths for GBMs range from 15 to 30 mm. Despite a generous margin, the persistent recurrence of GBMs following treatment indicates that the CTV delineations currently used might fail to encompass the entirety of the tumor cell distribution, leaving clonogenic tumor cells untreated. 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.  This thesis is comprised of papers I–II, an overview of the methodology, results, and discussion of the work. The work herein is presented in order of: 1) model development; 2) model verification. Paper I explores the robustness and results of a mathematical model for tumor spread in terms of its input parameters. 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. Paper II used the resulting simulated invasions from paper I 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. Planned future work will revolve around dose prescription and plan optimization based on the simulated tumor spread, model investigation using artificial intelligence methods, and finally, practical implementation of the model into research versions of treatment planning systems.