Fifty-eighth annual meeting of the american association of physicists in medicine
SU-G-TeP3-03: Dose Enhancement of Gold Nanoparticle in Proton Therapy: A Monte Carlo Study Based On the Transmission Electron Microscopy Imaging
Gold nanoparticle (GNP) is a promising radiosensitizer that selectively boosts tumor dose in radiotherapy. Transmission electron microscopy (TEM) imaging observations recently revealed for the first time that GNP exists in vivo in the form of highly localized vesicles, instead of hypothetical uniform distribution. This work investigates the corresponding difference of energy deposition in proton therapy.
First, single vesicles of various radii were constructed by packing GNPs (as Φ50 nm gold spheres) in spheres and were simulated, as well as a single GNP. The radial energy depositions (REDs) were scored using 100 concentric spherical shells from 0.1µm to 10µm, 0.1µm thickness each, for both vesicles and GNP, and compared. TEM images, 8 days after injection in a PC3 prostate cancer murine model, were used to extract position/dimension of vesicles, as well as contours of cytoplasmic and nucleus membranes. Vesicles were then constructed based on the TEM images. A 100 MeV proton beam was studied by using the Geant4-DNA code, which simulates all energy deposition events.
The vesicle REDs, normalized to the same proton energy loss as in a single GNP, are larger (smaller) than that of a single GNP when radius >2µm (<2µm). The peak increase (at about 3µm radius) is about 10% and 18% for Φ1µm and Φ1.6µm vesicles respectively, relative to a single GNP. The TEM-based simulation resulted in a larger energy deposition (by about one order of magnitude) that follows completely different pattern from that of hypothetical GNP distributions (regular dotted pattern in extracellular and/or extranucleus regions).
The in vivo energy deposition, both in pattern and magnitude, of proton therapy is greatly affected by the true distribution of the GNP, as illustrated by the presence of GNP vesicles compared to hypothetical scenarios.
Work supported by NSERC Discovery Grant #435510, Canada