Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration

Abstract

The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0–3.4%, primarily because soft tissue is the dominant tissue type in human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield Numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction.

Determination of mean ionization potential using magnetic resonance imaging for the reduction of proton beam range uncertainties: theory and application

“…The accuracy of DECT parameterizations for SPR determination have varied RMSE from 0.12% -0.28% with maximum errors ranging between 0.39% to 0.98% (Taasti et al, 2016) depending on the type of parameterization used when tested in "reference" tissues. Nearly all of these SECT (stoichiometric) and DECT parameterization methods require parameterization to "reference" human tissues (as those of ICRU Report #44) and suffer from increased errors as elemental compositions for particular tissue types deviate away from them (Taasti et al, 2016;Yang et al, 2010;Yang et al, 2012). In practice, errors from SECT and DECT calculation of Im and SPR can be much larger.…”

“…The UC model is therefore able to account for variations in an individual's tissue composition at a unique level rather than rely on parameterization to "reference" standard human tissue elemental compositions that may not be representative of patientspecific compositions. Compositions of water, fat and protein vary significantly from individual to individual with estimates of 1.2% (1s) error in SPR for soft tissue being due to deviations in human body tissue composition differences from "reference" standard human tissue compositions (Yang et al, 2012). Voxel to voxel differences in composition may also exist even within a single organ.…”

“…Five-component models of molecules in the human body have been used previously in the study of the human body (Heymsfield, 2005;Wang et al, 1992) and are assumed, in this study, to be sufficient to accurately calculate the mean ionization potential for naturally occurring biological tissues. From the work of Yang et al (Yang et al, 2010;Yang et al, 2012), they found that the primary contributor to uncertainties in an individual's tissues was hydrogen content in soft tissues and calcium content in mineralized/bony tissues. We investigated the hydrogen dependence of Im for water, lipids, carbohydrates and proteins by determining Im in water, lipids such as triglycerides, glucose and glycogen, and the 20 common (+2 highly uncommon) amino acids that compose proteins.…”

Time‐resolved diode dosimetry calibration through Monte Carlo modeling for in vivo passive scattered proton therapy range verification

“…Additionally, an HU variability of up to 20 HU was observed in the water‐equivalent plastic CT image. This results in a relative stopping power ratio variation of 0.02 according to the methodology in Yang et al 16. Both simulation cases suffer from uncertainties, and a ground truth cannot be determined with sufficient accuracy based on the known information.…”

Using gEUD based plan analysis method to evaluate proton vs. photon plans for lung cancer radiation therapy

“…The distal and proximal margin from the ITV was calculated as the square root value of the range uncertainty and the setup uncertainty assuming those two factors were independent from each other. Based on multiple institutions’ experience,11, 12 3 mm plus 3.5% range uncertainty value for proton beam was used in our institution. The compensator smearing was 10 mm.…”