A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using ¹⁸ F-FMISO-PET

Abstract

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.

A Multiscale Modeling Approach to Glioma Invasion with Therapy

“…On the other hand, however, the receptor binding inhibition can also impair cell proliferation, as the latter is known to be influenced by cell-matrix (and cell-cell) adhesion [14,27,32,40,45,63]; hence, the balance between increasing proliferation through stopping migration and reducing mitotic activity through inhibiting adhesion will be the driving factor for enhancing radiosensitivity. Mathematical models for the therapy of glioma have also been considered in [2,51,52], however in a much simplified monoscale case not able to account for the highly infiltrative behavior of this type of cancer. Here we start from the multiscale setting introduced in [19] to describe the evolution of a heterogeneous tumor consisting of migrating and proliferating glioma cells moving along white matter tracts of white matter.…”

A 3-dimensional DTI MRI-based model of GBM growth and response to radiation therapy

“…R is defined as a function of S, the fraction of cells surviving a radiation dose, using the wellknown linear-quadratic dose-response model: S = e -(αd+βd2) , where α (in units of Gy -1 ) and β (in units of Gy -2 ) reflect type A (single ionizing event) and type B (pairwise interaction of ionizing events) tissue damage. Since tissues can be somewhat characterized by an α/β ratio, and to simplify the model to a single radiation parameter, the α/β is held fixed, as in prior work (6,9,10). In the present study, similar to prior studies, this ratio is held at 10 Gy (6,9,10).…”

“…Similar to the work of Rockne et al (9,10), the R term quantifies the loss of tumor cells due to radiation therapy, which is delivered as discrete doses, and is hence amenable to modeling different dosing schedules. R is defined as a function of S, the fraction of cells surviving a radiation dose, using the wellknown linear-quadratic dose-response model: S = e -(αd+βd2) , where α (in units of Gy -1 ) and β (in units of Gy -2 ) reflect type A (single ionizing event) and type B (pairwise interaction of ionizing events) tissue damage.…”

“…As per Rockne et al (9,10), the effect of radiation therapy is also cast as a function of cell density, using the same logistic formulation as the tumor growth model as follows:…”

“…Such models would not only have the potential to optimize therapy, but also to potentially predict individual patient response patterns to a proposed treatment regimen (6,9,10).…”