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

ABSTRACT: Rhenium-186 (¹⁸⁶Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future

SUBMITTER: Christenson C 

PROVIDER: S-EPMC10768931 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature