Citation: Chase Christenson, Chengyue Wu, David A. Hormuth, Shiliang Huang, Ande Bao, Andrew Brenner, Thomas E. Yankeelov, Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes, Brain Multiphysics, 2023, 100084, ISSN 2666-5220, https://doi.org/10.1016/j.brain.2023.100084.
Code authors: Chase Christenson, Chengyuye Wu, David A. Hormuth II
The provided MATLAB functions can be used to predict glioblsatoma growth with either the patient-specific, or cohort based methods described in the manuscript. The clinical data is not publically available so replacement examples with similar formatting have been provided.
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Patient-specific calibration
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Inputs MRI and segmentations with similar format to processed clinical data
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Calibrates the provided model (M0 or M1 from manuscript)
$$\frac{\partial N}{\partial t}=\nabla \cdot \left(d\nabla N\right)+k_pN\left(1-\frac{N}{\theta}\right) \tag{M0}$$ $$\frac{\partial N}{\partial t}=\nabla \cdot \left(d\nabla N\right)+k_p(\textbf{x})N\left (1-\frac{N}{\theta}\right) \tag{M1}$$ -
Note: details for radiation coupled models (M2-M9) are available upon request
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Cohort calibration
- Samples parameters from in silico generated distribution to predict response using M0
(1) Hormuth II DA, Eldridge SL, Weis JA, et al. Mechanically coupled reaction-diffusion model to predict Glioma growth: methodological details. Methods Mol Biol. 2018;1711:225–241.
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