Congratulations to Dr. Jacobs and Dr. Hawkins-Daarud on their new paper in Journal of the Royal Society Interface

Written on May 25th, 2016

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


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Russell Rockne, Andrew D. Trister, Joshua Jacobs, Andrea J. Hawkins-Daarud, Maxwell L. Neal, Kristi Hendrickson, Maciej M. Mrugala, Jason K. Rockhill, Paul Kinahan, Kenneth A. Krohn, and Kristin R. Swanson

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.

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