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

Author:

Rockne Russell C.12ORCID,Trister Andrew D.3ORCID,Jacobs Joshua12,Hawkins-Daarud Andrea J.12,Neal Maxwell L.4ORCID,Hendrickson Kristi3,Mrugala Maciej M.5,Rockhill Jason K.3,Kinahan Paul6ORCID,Krohn Kenneth A.36,Swanson Kristin R.12ORCID

Affiliation:

1. Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N Saint Clair Street, Suite 1300, Chicago, IL 60611, USA

2. Northwestern Brain Tumor Institute, Northwestern University, 675 N Saint Clair Street, Suite 2100, Chicago, IL 60611, USA,

3. Department of Radiation Oncology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA

4. Department of Pathology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA

5. Department of Neurology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA

6. Department of Radiology, University of Washington, School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA

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 [ 18 F]-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.

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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