A novel approach for radiotherapy treatment planning accounting for high-grade glioma invasiveness into normal tissue

by Wille Häger (Stockholm University)

Tuesday 17 Dec 2024, 13:00 → 16:00 Europe/Stockholm

Lecture hall CCK Visionsgatan 56b, Solna (Cancer Centrum Karolinska)

Abstract
High-grade gliomas (HGGs) are a form of malignant brain cancer that includes 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, followed by radiotherapy and chemotherapy. Despite the efforts, the median survival for patients diagnosed
with GBMs is less than 15 months. Studies have shown that tumor recurrence has an increased probability of occurring
near the edge of the resected volume. This suggests that the inability to accurately determine the full extent of the tumorinvaded
regions in the brain may be the reason for the incurability of GBMs.
In radiotherapy, microscopic infiltration of tumor cells into adjacent normal tissue beyond the boundaries of the gross
tumor volume (GTV) is addressed by expanding the target to define a clinical target volume (CTV). This additional margin
aims to encompass potential microscopic disease spread and has been associated with improved treatment outcomes by
reducing the likelihood of local recurrence. Current recommended CTV 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 in use 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 comprises Papers I–IV, complemented by an overview of the methodology, results, and discussion of the
work. The work herein is presented in the following order: 1) model development; 2) model verification; 3) treatment
planning accounting for the modeled tumor cell infiltration. Paper I explores the robustness and results of a mathematical
model for tumor spread in terms of its input parameters. Applying the model to a large dataset enables a statistical analysis
of its behavior, allowing for the identification of optimal input parameters. 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. Paper III applied clinical dose
plans to simulated tumor spread and demonstrated that the distribution of surviving tumor cells correlated with tumor
recurrence post-treatment at an early time point. This suggests that treatment outcomes could potentially be improved
by incorporating modeled tumor spread. Lastly, Paper IV explored two methodologies for treatment planning on GBMs,
which take modeled tumor spread into account.