B. George

Unwrapping 3D complex hollow organs for spatial dose surface analysis

Purpose Toxicity dose–response models describe the correlation between dose delivered to an organ and a given toxic endpoint. Duodenal toxicity is a dose limiting factor in the treatment of pancreatic cancer with radiation but the relationship between dose and toxicity in the duodenum is not well understood. While there have been limited studies into duodenal toxicity through investigations of the volume of the organ receiving dose over a specific threshold, both dose-volume and dose-surface histograms lack spatial information about the dose distribution, which may be important in determining normal tissue response. Due to the complex geometry of the duodenum, previous methods for unwrapping tubular organs for spatial modeling of toxicity are insufficient. A geometrically robust method for producing 2D dose surface maps (DSMs), specifically for the duodenum, has been developed and tested in order to characterize the spatial dose distribution. Methods The organ contour is defined using Delaunay triangulation. The user selects a start and end coordinate in the structure and a path is found by regulating both length and curvature. This path is discretized and rays are cast from each point on the plane normal to the vector between the previous and the next point on the path and the dose at the closest perimeter point recorded. These angular perimeter slices are “unwrapped” from the edge distal to the pancreas to ensure the high dose region (proximal to the tumor) falls in the centre of the dose map. Gamma analysis is used to quantify the robustness of this method and the effect of overlapping planes. Results This method was used to extract DSMs for 15 duodena, with one esophagus case to illustrate the application to simpler geometries. Visual comparison indicates that a 30 × 30 map provides sufficient resolution to view gross spatial features of interest. A lookup table is created to store the area (cm2) represented by each pixel in the DSMs in order to allow spatial descriptors in absolute size. The method described in this paper is robust, requires minimal human interaction, has been shown to be generalizable to simpler geometries, and uses readily available commercial software. The difference seen in DSMs due to overlapping planes is large and justifies the need for a solution that removes such planes. Conclusions This is the first time 2D dose surface maps have been produced for the duodenum and provide spatial dose distribution information which can be explored to create models that may improve toxicity prediction in treatments for locally advanced pancreatic cancer.

Using stable distributions to characterize proton pencil beams

Purpose To introduce and evaluate the use of stable distributions as a methodology to quantify the behavior of proton pencil beams in a medium. Methods The proton pencil beams of a clinically commissioned proton treatment facility are replicated in a Monte Carlo simulation system (FLUKA). For each available energy, the beam deposition in water medium is characterized by the dose deposition. Using a stable distribution methodology, each beam with a nominal energy E is characterized by the lateral spread at depth z: S(z; α, γ, E) and a total energy deposition I D(z, E). The parameter α describes the tailedness of the distributions, while γ is used to scale the size of the function. The beams can then be described completely by a function of the variation of the parameters with depth. Results Quantitatively, the fit of the stable distributions, compared to those implemented in some standard treatment planning systems, are equivalent for all but the highest energies (i.e., 230 MeV/u). The decrease in goodness of fit makes this methodology comparable to a double Gaussian approach. The introduction of restricted linear combinations of stable distributions also resolves that particular case. More importantly, the meta‐parameterization (i.e., the description of the dose deposition by only providing the fitted parameters) allows for interpolation of nonmeasured data. In the case of the clinical commissioning data used in this paper, it was possible to only commission one out of five nominal energies to obtain a viable dataset, valid for all energies. An additional parameter β allows to describe asymmetric beam profiles as well. Conclusions Stable distributions are intrinsically suited to describe proton pencil beams in a medium and provide a tool to quantify the propagation of proton beams in a medium.

Robustness assessment using probabilistic scenarios of intensity modulated proton therapy and volumetric arc therapy in non-small-cell lung cancer: an in-silico radiotherapy planning study

Abstract Background Radiotherapy is an essential treatment component for patients with stage III non-small-cell lung cancer. Despite advances, survival remains poor. Proton beam therapy holds the promise of improving cure rates without increasing treatment-related toxicity. However, precision in dose delivery is sensitive to setup uncertainties. The conventional method of adding a margin to account for this problem can be inadequate. We aimed to use the probabilistic scenarios methodology to assess the robustness of intensity modulated proton therapy (IMPT) and volumetric arc therapy (VMAT). Methods Plans were optimised by minimax robust optimisation (MM) and margin-based (planning target volume [PTV]) methods (MM–IMPT, PTV–IMPT, and VMAT). Robustness was assessed with probabilistically simulated setup errors. 35 perturbed doses were summed to model a treatment course. The CTV-D98 (dose to 98% of the clinical target volume) of each summed dose distribution was compared with the nominal plan and considered robust if within 5%. The variance of the CTV-D98 in IMPT and VMAT plans were compared using Levenes Test. Findings 700 simulations from 20 plans were analysed. Despite dose variation over a simulated course of treatment, the robustness of each summed plan was within clinical limits. There was significantly less variance in the perturbed CTV-D98 in the MM–IMPT than in PTV–IMPT plans (4·43 cGy 2 vs 16·17, F =50·993, p 2 vs 4·04, F =0·312, p=0·577). Target conformality improved with increasing number of beams and robust optimisation. All summed plans met normal tissue dose constraints. Interpretation Initial results showed that fractionation reduced uncertainties in dose distribution due to setup errors. Robustness of MM–IMPT and VMAT plans were similar. Although the present analysis has not considered range uncertainties and organ motion, the simulations highlight differences in plan qualities with different optimisation strategies. The probabilistic scenarios methodology might be used to estimate robustness of IMPT plans in stage III non-small-cell lung cancer. Funding Cancer Research UK.

SU‐F‐T‐147: An Alternative Parameterization of Scatter Behavior Allows Significant Reduction of Beam Characterization for Pencil Beam Proton Therapy

PURPOSE 1) To describe the characteristics of pencil beam proton dose deposition kernels in a homogenous medium using a novel parameterization. 2) To propose a method utilizing this novel parametrization to reduce the measurements and pre-computation required in commissioning a pencil beam proton therapy system. METHODS Using beam data from a clinical, pencil beam proton therapy center, Monte Carlo simulations were performed to characterize the dose depositions at a range of energies from 100.32 to 226.08 MeV in 3.6MeV steps. At each energy, the beam is defined at the surface of the phantom by a two-dimensional Normal distribution. Using FLUKA, the in-medium dose distribution is calculated in 200×200×350 mm cube with 1 mm3 tally volumes. The calculated dose distribution in each 200×200 slice perpendicular to the beam axis is then characterized using a symmetric alpha-stable distribution centered on the beam axis. This results in two parameters, α and γ, that completely describe shape of the distribution. In addition, the total dose deposited on each slice is calculated. The alpha-stable parameters are plotted as function of the depth in-medium, providing a representation of dose deposition along the pencil beam. We observed that these graphs are isometric through a scaling of both abscissa and ordinate map the curves. RESULTS Using interpolation of the scaling factors of two source curves representative of different beam energies, we predicted the parameters of a third curve at an intermediate energy. The errors are quantified by the maximal difference and provide a fit better than previous methods. The maximal energy difference between the source curves generating identical curves was 21.14MeV. CONCLUSION We have introduced a novel method to parameterize the in-phantom properties of pencil beam proton dose depositions. For the case of the Knoxville IBA system, no more than nine pencil beams have to be fully characterized.