(Sunday, 7/29/2018) 3:00 PM - 6:00 PM
Room: Exhibit Hall
Purpose: Gold nanoparticles (AuNPs), and other radiosensitizers, have shown potential in high-conformality “dose-painting� by using photoelectrons and Auger electrons to increase dose to the target volume. This enables delivering less incident radiation to the planning-target-volume (PTV), sparing healthy surrounding tissue. This study is a theoretical investigation of dose-painting using AuNPs released from smart-radiotherapy-biomaterials (SRBs) in tissue and the development of working clinical models to calculate dose-enhancement to patients receiving AuNP-aided radiotherapy.
Methods: Using data from previous studies, a particle diffusion model was made to estimate the dose-enhancement-factor (DEF) due to AuNP photoelectric contribution. The model calculates diffusion rates of different size AuNPs and dose-enhancement from various incident radiation sources. It was constructed with a user-interface to easily change parameter values, enabling quick comparison between models and optimization analysis of dose-painting. Two SRB systems were considered for breast cancer using a SRB balloon applicator coated with nanoparticles during 50kVp x-ray Xoft electronic brachytherapy, and SRB spacers loaded with nanoparticles for prostate cancer using a I-125 brachytherapy source.
Results: The simulations show significant dose-enhancement to various ranges within reasonably achievable diffusion periods. For a 50 kVp source and 10nm AuNPs, a clinically significant DEF of 1.2 could be achieved at 5mm after 10 days. For I-125 brachytherapy it could be achieved after 25 days. The results allow for prediction of information to determine optimal source choice, diffusion period, and NP size to dose-paint any size target volume with sufficient dose-enhancement.
Conclusion: We have developed a nanoparticle-aided radiotherapy dose-painting app for predicting DEF which would be very useful in guiding experimental studies and treatment planning during clinical translation. Ongoing in-vivo experimental studies will be used to optimize these models for clinically practical treatment planning.
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