Development of an Image-Guided Dosimetric Planning System for Injectable Brachytherapy using ELP Nanoparticles
Elastin-Like Polypeptide (ELP) nanoparticles present a promising mechanism for delivering brachytherapy for cancer treatment. These organic, polymer-based nanoparticles are injectable, biodegradable, and genetically tunable. Presented as the motivation of this thesis is a genetically encoded polymer-solution, composed of novel radiolabeled-ELP nanoparticles that are custom-designed to self-assemble into a local source upon intratumoral injection1. While preliminary results from a small animal study demonstrate 100% tumor response, effective radionuclide retention-rates, strong in vivo stability, and no polymer-induced toxicities, the current workflow lacks a dosimetry framework. The purpose of this thesis research was to provide such an infrastructure. We have developed a robust software framework that provides image-guided dosimetric-planning capabilities for ELP brachytherapy. This has resulted in several novel applications. First, the development of a point-dose-kernel-convolution-based dose calculation algorithm has invited the possibility of more quantitative ELP brachytherapy outcomes. Likewise, the ability to graphically pre-determine ELP injection sites under μCT image-guidance has introduced a new technical advantage into the current workflow. The planning system has also been integrated into a Monte Carlo environment, where SPECT imaging information can be exported and converted into a simulated source, allowing realistic, injection specific simulations to be performed. In addition to these technical developments, ELP steady state distributions have been experimentally measured via μSPECT acquisition, and the dose calculation algorithm has been validated against Monte Carlo simulation. The planning system was ultimately used to perform an internal dosimetry calculation of an in vivo ELP solution. Prior to this thesis work, this type of calculation had yet to be performed.
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Rights for Collection: Masters Theses