Jan Schuemann

A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data.

Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies along the treatment field, i.e. with linear energy transfer (LET) and with tissue type. A recent review study collected over 70 experimental reports on proton RBE, providing a comprehensive dataset for predicting RBE for cell survival. Using this dataset we developed a model to predict proton RBE based on dose, dose average LET (LETd) and the ratio of the linear-quadratic model parameters for the reference radiation (α/β)x, as the tissue specific parameter. The proposed RBE model is based on the linear quadratic model and was derived from a nonlinear regression fit to 287 experimental data points. The proposed model predicts that the RBE increases with increasing LETd and decreases with increasing (α/β)x. This agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/β)x. The model additionally predicts a decrease in RBE with increasing dose and shows a relationship between both α and β with LETd. Our proposed phenomenological RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. Previously published phenomenological models, based on a limited data set, may have to be revised.

Comparing gold nano-particle enhanced radiotherapy with protons, megavoltage photons and kilovoltage photons: a Monte Carlo simulation

Gold nanoparticles (GNPs) have shown potential to be used as a radiosensitizer for radiation therapy. Despite extensive research activity to study GNP radiosensitization using photon beams, only a few studies have been carried out using proton beams. In this work Monte Carlo simulations were used to assess the dose enhancement of GNPs for proton therapy. The enhancement effect was compared between a clinical proton spectrum, a clinical 6 MV photon spectrum, and a kilovoltage photon source similar to those used in many radiobiology lab settings. We showed that the mechanism by which GNPs can lead to dose enhancements in radiation therapy differs when comparing photon and proton radiation. The GNP dose enhancement using protons can be up to 14 and is independent of proton energy, while the dose enhancement is highly dependent on the photon energy used. For the same amount of energy absorbed in the GNP, interactions with protons, kVp photons and MV photons produce similar doses within several nanometers of the GNP surface, and differences are below 15% for the first 10 nm. However, secondary electrons produced by kilovoltage photons have the longest range in water as compared to protons and MV photons, e.g. they cause a dose enhancement 20 times higher than the one caused by protons 10 μm away from the GNP surface. We conclude that GNPs have the potential to enhance radiation therapy depending on the type of radiation source. Proton therapy can be enhanced significantly only if the GNPs are in close proximity to the biological target.

Biological modeling of gold nanoparticle enhanced radiotherapy for proton therapy

Gold nanoparticles (GNPs) have shown potential as a radiosensitizer for radiation therapy using photon beams. Recently, experimental studies have been carried out using proton beams showing the GNP enhanced responses in proton therapy. In this work, we established a biological model to investigate the change in survival of irradiated cells due to the radiosensitizing effect of gold nanoparticles. Results for proton, megavoltage (MV) photon and kilovoltage (kV) photon beams are compared. For each particle source, we assessed various treatment depths, GNP cellular uptakes and sizes. We showed that kilovoltage photons caused the highest enhancement due to the high interaction probability between GNPs and kV photons. The cell survival fraction can be significantly reduced for both proton and MV photon irradiations if GNPs accumulate in the cell. For instance, the sensitizer enhancement ratio (SER) is 1.33 for protons in the middle of a spread out Bragg peak for 1 µM of internalized 50 nm GNPs. If the GNPs can all be internalized into the cell nucleus, the SER for proton therapy increases from 1.33 to 1.81. The results also show that for the same mass of GNPs in the cells, one can expect the greatest sensitization by smaller GNPs, i.e. a SER of 1.33 for 1 µM of internalized 50 nm GNPs and a SER of 3.98 for the same mass of 2 nm GNPs. We concluded that if the GNPs cannot be internalized into the cytoplasm, no GNP enhancement will be observed for proton treatment. Meanwhile, proton radiotherapy can potentially be enhanced with GNPs if they can be internalized into cells, and especially the cell nucleus.

Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy

PURPOSE To assess the impact of approximations in current analytical dose calculation methods (ADCs) on tumor control probability (TCP) in proton therapy. METHODS Dose distributions planned with ADC were compared with delivered dose distributions as determined by Monte Carlo simulations. A total of 50 patients were investigated in this analysis with 10 patients per site for 5 treatment sites (head and neck, lung, breast, prostate, liver). Differences were evaluated using dosimetric indices based on a dose-volume histogram analysis, a γ-index analysis, and estimations of TCP. RESULTS We found that ADC overestimated the target doses on average by 1% to 2% for all patients considered. The mean dose, D95, D50, and D02 (the dose value covering 95%, 50% and 2% of the target volume, respectively) were predicted within 5% of the delivered dose. The γ-index passing rate for target volumes was above 96% for a 3%/3 mm criterion. Differences in TCP were up to 2%, 2.5%, 6%, 6.5%, and 11% for liver and breast, prostate, head and neck, and lung patients, respectively. Differences in normal tissue complication probabilities for bladder and anterior rectum of prostate patients were less than 3%. CONCLUSION Our results indicate that current dose calculation algorithms lead to underdosage of the target by as much as 5%, resulting in differences in TCP of up to 11%. To ensure full target coverage, advanced dose calculation methods like Monte Carlo simulations may be necessary in proton therapy. Monte Carlo simulations may also be required to avoid biases resulting from systematic discrepancies in calculated dose distributions for clinical trials comparing proton therapy with conventional radiation therapy.

Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1-2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8-2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.

An algorithm to assess the need for clinical Monte Carlo dose calculation for small proton therapy fields based on quantification of tissue heterogeneity

PURPOSE In proton therapy, complex density heterogeneities within the beam path constitute a challenge to dose calculation algorithms. This might question the reliability of dose distributions predicted by treatment planning systems based on analytical dose calculation. For cases in which substantial dose errors are expected, resorting to Monte Carlo dose calculations might be essential to ensure a successful treatment outcome and therefore the benefit is worth a presumably long computation time. The aim of this study was to define an indicator for the accuracy of dose delivery based on analytical dose calculations in treatment planning systems for small proton therapy fields to identify those patients for which Monte Carlo dose calculation is warranted. METHODS Fourteen patients treated at our facility with small passively scattered proton beams (apertures diameters below 7 cm) were selected. Plans were generated in the XiO treatment planning system in combination with a pencil beam algorithm developed at the Massachusetts General Hospital and compared to Monte Carlo dose calculations. Differences in the dose to the 50% of the gross tumor volume (D50, GTV) were assessed in a field-by-field basis. A simple and fast methodology was developed to quantify the inhomogeneity of the tissue traversed by a single small proton beam using a heterogeneity index (HI)-a concept presented by Plugfelder et al. [Med. Phys. 34, 1506-1513 (2007)] for scanned proton beams. Finally, the potential correlation between the error made by the pencil beam based treatment planning algorithm for each field and the level of tissue heterogeneity traversed by the proton beam given by the HI was evaluated. RESULTS Discrepancies up to 5.4% were found in D50 for single fields, although dose differences were within clinical tolerance levels (<3%) when combining all of the fields involved in the treatment. The discrepancies found for each field exhibited a strong correlation to their associated HI-values (Spearmans ρ=0.8, p<0.0001); the higher the level of tissue inhomogeneities for a particular field, the larger the error by the analytical algorithm. With the established correlation a threshold for HI can be set by choosing a tolerance level of 2-3%-commonly accepted in radiotherapy. CONCLUSIONS The HI is a good indicator for the accuracy of proton field delivery in terms of GTV prescription dose coverage when small fields are delivered. Each HI-value was obtained from the CT image in less than 3 min on a computer with 2 GHz CPU allowing implementation of this methodology in clinical routine. For HI-values exceeding the threshold, either a change in beam direction (if feasible) or a recalculation of the dose with Monte Carlo would be highly recommended.

Validation of the radiobiology toolkit TOPAS-nBio in simple DNA geometries

Computational simulations offer a powerful tool for quantitatively investigating radiation interactions with biological tissue and can help bridge the gap between physics, chemistry and biology. The TOPAS collaboration is tackling this challenge by extending the current Monte Carlo tool to allow for sub-cellular in silico simulations in a new extension, TOPAS-nBio. TOPAS wraps and extends the Geant4 Monte Carlo simulation toolkit and the new extension allows the modeling of particles down to vibrational energies (∼2eV) within realistic biological geometries. Here we present a validation of biological geometries available in TOPAS-nBio, by comparing our results to two previously published studies. We compare the prediction of strand breaks in a simple linear DNA strand from TOPAS-nBio to a published Monte Carlo track structure simulation study. While TOPAS-nBio confirms the trend in strand break generation, it predicts a higher frequency of events below an energy of 17.5eV compared to the alternative Monte Carlo track structure study. This is due to differences in the physics models used by each code. We also compare the experimental measurement of strand breaks from incident protons in DNA plasmids to TOPAS-nBio simulations. Our results show good agreement of single and double strand breaks predicting a similar increase in the strand break yield with increasing LET.

Dose enhancement effects to the nucleus and mitochondria from gold nanoparticles in the cytosol

Gold nanoparticles (GNPs) have shown potential as dose enhancers for radiation therapy. Since damage to the genome affects the viability of a cell, it is generally assumed that GNPs have to localise within the cell nucleus. In practice, however, GNPs tend to localise in the cytoplasm yet still appear to have a dose enhancing effect on the cell. Whether this effect can be attributed to stress-induced biological mechanisms or to physical damage to extra-nuclear cellular targets is still unclear. There is however growing evidence to suggest that the cellular response to radiation can also be influenced by indirect processes induced when the nucleus is not directly targeted by radiation. The mitochondrion in particular may be an effective extra-nuclear radiation target given its many important functional roles in the cell. To more accurately predict the physical effect of radiation within different cell organelles, we measured the full chemical composition of a whole human lymphocytic JURKAT cell as well as two separate organelles; the cell nucleus and the mitochondrion. The experimental measurements found that all three biological materials had similar ionisation energies  ∼70 eV, substantially lower than that of liquid water  ∼78 eV. Monte Carlo simulations for 10-50 keV incident photons showed higher energy deposition and ionisation numbers in the cell and organelle materials compared to liquid water. Adding a 1% mass fraction of gold to each material increased the energy deposition by a factor of  ∼1.8 when averaged over all incident photon energies. Simulations of a realistic compartmentalised cell show that the presence of gold in the cytosol increases the energy deposition in the mitochondrial volume more than within the nuclear volume. We find this is due to sub-micron delocalisation of energy by photoelectrons, making the mitochondria a potentially viable indirect radiation target for GNPs that localise to the cytosol.

Gold nanoparticle induced vasculature damage in radiotherapy: Comparing protons, megavoltage photons, and kilovoltage photons

PURPOSE The purpose of this work is to investigate the radiosensitizing effect of gold nanoparticle (GNP) induced vasculature damage for proton, megavoltage (MV) photon, and kilovoltage (kV) photon irradiation. METHODS Monte Carlo simulations were carried out using tool for particle simulation (TOPAS) to obtain the spatial dose distribution in close proximity up to 20 μm from the GNPs. The spatial dose distribution from GNPs was used as an input to calculate the dose deposited to the blood vessels. GNP induced vasculature damage was evaluated for three particle sources (a clinical spread out Bragg peak proton beam, a 6 MV photon beam, and two kV photon beams). For each particle source, various depths in tissue, GNP sizes (2, 10, and 20 nm diameter), and vessel diameters (8, 14, and 20 μm) were investigated. Two GNP distributions in lumen were considered, either homogeneously distributed in the vessel or attached to the inner wall of the vessel. Doses of 30 Gy and 2 Gy were considered, representing typical in vivo enhancement studies and conventional clinical fractionation, respectively. RESULTS These simulations showed that for 20 Au-mg/g GNP blood concentration homogeneously distributed in the vessel, the additional dose at the inner vascular wall encircling the lumen was 43% of the prescribed dose at the depth of treatment for the 250 kVp photon source, 1% for the 6 MV photon source, and 0.1% for the proton beam. For kV photons, GNPs caused 15% more dose in the vascular wall for 150 kVp source than for 250 kVp. For 6 MV photons, GNPs caused 0.2% more dose in the vascular wall at 20 cm depth in water as compared to at depth of maximum dose (Dmax). For proton therapy, GNPs caused the same dose in the vascular wall for all depths across the spread out Bragg peak with 12.7 cm range and 7 cm modulation. For the same weight of GNPs in the vessel, 2 nm diameter GNPs caused three times more damage to the vessel than 20 nm diameter GNPs. When the GNPs were attached to the inner vascular wall, the damage to the inner vascular wall can be up to 207% of the prescribed dose for the 250 kVp photon source, 4% for the 6 MV photon source, and 2% for the proton beam. Even though the average dose increase from the proton beam and MV photon beam was not large, there were high dose spikes that elevate the local dose of the parts of the blood vessel to be higher than 15 Gy even for 2 Gy prescribed dose, especially when the GNPs can be actively targeted to the endothelial cells. CONCLUSIONS GNPs can potentially be used to enhance radiation therapy by causing vasculature damage through high dose spikes caused by the addition of GNPs especially for hypofractionated treatment. If GNPs are designed to actively accumulate at the tumor vasculature walls, vasculature damage can be increased significantly. The largest enhancement is seen using kilovoltage photons due to the photoelectric effect. Although no significant average dose enhancement was observed for the whole vasculature structure for both MV photons and protons, they can cause high local dose escalation (>15 Gy) to areas of the blood vessel that can potentially contribute to the disruption of the functionality of the blood vessels in the tumor.