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Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Title	Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes
Authors	Chase Christenson, Chengyue Wu, David A. Hormuth II, Shiliang Huang, Ande Bao, Andrew Brenner, Thomas E. Yankeelov
Magazine	Brain multiphysics
Date	10/29/2023
DOI	10.1016/j.brain.2023.100084
Introduction	The application of Rhenium-186 labelled nanoliposome (RNL) therapy offers a promising advancement in treating recurrent glioblastoma by facilitating targeted radiation delivery. To enhance RNL administration efficacy, we developed a predictive framework using image-guided mathematical models tailored to individual patients. This involved calibrating reaction-diffusion models with imaging data from ten patients to forecast tumour dynamics. By utilising MRI and SPECT data, we assessed tumour burden and local RNL activity. Our selected model achieved high correlation with measured data, demonstrating its predictive power using both patient-specific and leave-one-out analyses. These findings underscore the potential of computational models in predicting glioblastoma response to radionuclide therapy and highlight the importance of imaging studies focusing on local immune and vascular responses to high-dose radiation.
Quote	Chase Christenson, Chengyue Wu and David A. Hormuth et al. Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes. Brain Multiphysics. 2023. Vol. 5. DOI: 10.1016/j.brain.2023.100084
Materials	Nanocomposites
Industry	Medical Devices

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