Complete patient exposure during paediatric brain cancer treatment for photon and proton therapy techniques including imaging procedures

Background

In radiotherapy, especially when treating children, minimising exposure of healthy tissue can prevent the development of adverse outcomes, including second cancers. In this study we propose a validated Monte Carlo framework to evaluate the complete patient exposure during paediatric brain cancer treatment.

Materials and methods

Organ doses were calculated for treatment of a diffuse midline glioma (50.4 Gy with 1.8 Gy per fraction) on a 5-year-old anthropomorphic phantom with 3D-conformal radiotherapy, intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT) and intensity modulated pencil beam scanning (PBS) proton therapy. Doses from computed tomography (CT) for planning and on-board imaging for positioning (kV-cone beam CT and X-ray imaging) accounted for the estimate of the exposure of the patient including imaging therapeutic dose. For dose calculations we used validated Monte Carlo-based tools (PRIMO, TOPAS, PENELOPE), while lifetime attributable risk (LAR) was estimated from dose-response relationships for cancer induction, proposed by Schneider et al.

Results

Out-of-field organ dose equivalent data of proton therapy are lower, with doses between 0.6 mSv (testes) and 120 mSv (thyroid), when compared to photon therapy revealing the highest out-of-field doses for IMRT ranging between 43 mSv (testes) and 575 mSv (thyroid). Dose delivered by CT ranged between 0.01 mSv (testes) and 72 mSv (scapula) while a single imaging positioning ranged between 2 μSv (testes) and 1.3 mSv (thyroid) for CBCT and 0.03 μSv (testes) and 48 μSv (scapula) for X-ray. Adding imaging dose from CT and daily CBCT to the therapeutic demonstrated an important contribution of imaging to the overall radiation burden in the course of treatment, which is subsequently used to predict the LAR, for selected organs.

Conclusion

The complete patient exposure during paediatric brain cancer treatment was estimated by combining the results from different Monte Carlo-based dosimetry tools, showing that proton therapy allows significant reduction of the out-of-field doses and secondary cancer risk in selected organs.

Experimental Validation of an Analytical Program and a Monte Carlo Simulation for the Computation of the Far Out-of-Field Dose in External Beam Photon Therapy Applied to Pediatric Patients

Background

The out-of-the-field absorbed dose affects the probability of primary second radiation-induced cancers. This is particularly relevant in the case of pediatric treatments. There are currently no methods employed in the clinical routine for the computation of dose distributions from stray radiation in radiotherapy. To overcome this limitation in the framework of conventional teletherapy with photon beams, two computational tools have been developed—one based on an analytical approach and another depending on a fast Monte Carlo algorithm. The purpose of this work is to evaluate the accuracy of these approaches by comparison with experimental data obtained from anthropomorphic phantom irradiations.

Materials and Methods

An anthropomorphic phantom representing a 5-year-old child (ATOM, CIRS) was irradiated considering a brain tumor using a Varian TrueBeam linac. Two treatments for the same planned target volume (PTV) were considered, namely, intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). In all cases, the irradiation was conducted with a 6-MV energy beam using the flattening filter for a prescribed dose of 3.6 Gy to the PTV. The phantom had natLiF : Mg, Cu, P (MCP-N) thermoluminescent dosimeters (TLDs) in its 180 holes. The uncertainty of the experimental data was around 20%, which was mostly attributed to the MCP-N energy dependence. To calculate the out-of-field dose, an analytical algorithm was implemented to be run from a Varian Eclipse TPS. This algorithm considers that all anatomical structures are filled with water, with the exception of the lungs which are made of air. The fast Monte Carlo code dose planning method was also used for computing the out-of-field dose. It was executed from the dose verification system PRIMO using a phase-space file containing 3x109 histories, reaching an average standard statistical uncertainty of less than 0.2% (coverage factor k = 1 ) on all voxels scoring more than 50% of the maximum dose. The standard statistical uncertainty of out-of-field voxels in the Monte Carlo simulation did not exceed 5%. For the Monte Carlo simulation the actual chemical composition of the materials used in ATOM, as provided by the manufacturer, was employed.

Results

In the out-of-the-field region, the absorbed dose was on average four orders of magnitude lower than the dose at the PTV. For the two modalities employed, the discrepancy between the central values of the TLDs located in the out-of-the-field region and the corresponding positions in the analytic model were in general less than 40%. The discrepancy in the lung doses was more pronounced for IMRT. The same comparison between the experimental and the Monte Carlo data yielded differences which are, in general, smaller than 20%. It was observed that the VMAT irradiation produces the smallest out-of-the-field dose when compared to IMRT.

Conclusions

The proposed computational methods for the routine calculation of the out-of-the-field dose produce results that are similar, in most cases, with the experimental data. It has been experimentally found that the VMAT irradiation produces the smallest out-of-the-field dose when compared to IMRT for a given PTV.

Validation of a Monte Carlo Framework for Out-of-Field Dose Calculations in Proton Therapy

Proton therapy enables to deliver highly conformed dose distributions owing to the characteristic Bragg peak and the finite range of protons. However, during proton therapy, secondary neutrons are created, which can travel long distances and deposit dose in out-of-field volumes. This out-of-field absorbed dose needs to be considered for radiation-induced secondary cancers, which are particularly relevant in the case of pediatric treatments. Unfortunately, no method exists in clinics for the computation of the out-of-field dose distributions in proton therapy. To help overcome this limitation, a computational tool has been developed based on the Monte Carlo code TOPAS. The purpose of this work is to evaluate the accuracy of this tool in comparison to experimental data obtained from an anthropomorphic phantom irradiation. An anthropomorphic phantom of a 5-year-old child (ATOM, CIRS) was irradiated for a brain tumor treatment in an IBA Proteus Plus facility using a pencil beam dedicated nozzle. The treatment consisted of three pencil beam scanning fields employing a lucite range shifter. Proton energies ranged from 100 to 165 MeV. A median dose of 50.4 Gy(RBE) with 1.8 Gy(RBE) per fraction was prescribed to the initial planning target volume (PTV), which was located in the cerebellum. Thermoluminescent detectors (TLDs), namely, Li-7-enriched LiF : Mg, Ti (MTS-7) type, were used to detect gamma radiation, which is produced by nuclear reactions, and secondary as well as recoil protons created out-of-field by secondary neutrons. Li-6-enriched LiF : Mg,Cu,P (MCP-6) was combined with Li-7-enriched MCP-7 to measure thermal neutrons. TLDs were calibrated in Co-60 and reported on absorbed dose in water per target dose (μGy/Gy) as well as thermal neutron dose equivalent per target dose (μSv/Gy). Additionally, bubble detectors for personal neutron dosimetry (BD-PND) were used for measuring neutrons (>50 keV), which were calibrated in a Cf-252 neutron beam to report on neutron dose equivalent dose data. The Monte Carlo code TOPAS (version 3.6) was run using a phase-space file containing 10¹⁰ histories reaching an average standard statistical uncertainty of less than 0.2% (coverage factor k = 1) on all voxels scoring more than 50% of the maximum dose. The primary beam was modeled following a Fermi–Eyges description of the spot envelope fitted to measurements. For the Monte Carlo simulation, the chemical composition of the tissues represented in ATOM was employed. The dose was tallied as dose-to-water, and data were normalized to the target dose (physical dose) to report on absorbed doses per target dose (mSv/Gy) or neutron dose equivalent per target dose (μSv/Gy), while also an estimate of the total organ dose was provided for a target dose of 50.4 Gy(RBE). Out-of-field doses showed absorbed doses that were 5 to 6 orders of magnitude lower than the target dose. The discrepancy between TLD data and the corresponding scored values in the Monte Carlo calculations involving proton and gamma contributions was on average 18%. The comparison between the neutron equivalent doses between the Monte Carlo simulation and the measured neutron doses was on average 8%. Organ dose calculations revealed the highest dose for the thyroid, which was 120 mSv, while other organ doses ranged from 18 mSv in the lungs to 0.6 mSv in the testes. The proposed computational method for routine calculation of the out-of-the-field dose in proton therapy produces results that are compatible with the experimental data and allow to calculate out-of-field organ doses during proton therapy.

Development of whole-body representation and dose calculation in a commercial treatment planning system

For the epidemiological evaluation of long-term side effects of radiotherapy patients, it is important to know the doses to organs and tissues everywhere in the patient. Computed tomography (CT) images of the patients which contain the anatomical information are sometimes available for each treated patient. However, the available CT scans usually cover only the treated volume of the patient including the target and surrounding anatomy. To overcome this limitation, in this work we describe the development of a software tool using the Varian Eclipse Scripting API for extending a partial-body CT to a whole-body representation in the treatment planning system for dose calculation. The whole-body representation is created by fusing the partial-body CT with a similarly sized whole-body computational phantom selected from a library containing 64 phantoms of different heights, weights, and genders. The out-of-field dose is calculated with analytical models from the literature and merged with the treatment planning system-calculated dose. To test the method, the out-of-field dose distributions on the computational phantoms were compared to dose calculations on whole-body patient CTs. The mean doses, D2% and D98% were compared in 26 organs and tissues for 14 different treatment plans in 5 patients using 3D-CRT, IMRT, VMAT, coplanar and non-coplanar techniques. From these comparisons we found that mean relative differences between organ doses ranged from -10% and +20% with standard deviations of up to 40%. The developed method will help epidemiologists and researchers estimate organ doses outside the treated volume when only limited treatment planning CT information is available.

First measurements of ionization cluster-size distributions with a compact nanodosimeter

PURPOSE

A nanodosimeter is a type of detector which measures single ionizations in a small gaseous volume in order to obtain ionization cluster size probability distributions for characterization of radiation types. Working nanodosimeter detectors are usually bulky machines which require a lot of space. In this work, the authors present a compact ceramic nanodosimeter detector and report on first measurements of cluster size distributions of 5 MeV alpha particles.

METHODS

Single ionization measurements are achieved by applying a weak electric field to collect positive ions in a hole in a ceramic plate. Inside the ceramic plate, due to a strong electric field, the ions are accelerated and produce impact-ionizations. The resulting electron avalanche is detected in a read-out electrode. A Bayesian unfolding algorithm is then applied to the experimentally obtained cluster size distributions to reconstruct the true cluster size distributions.

RESULTS

Experimentally obtained cluster size distributions by the compact nanodosimeter detector are presented. The reconstructed cluster size distributions agreed well with Monte Carlo simulated cluster size distributions for small volumes (diameter = 2.5 nm). For larger volumes, discrepancies between the reconstructed cluster size distributions and cluster size distributions from Monte Carlo simulations were observed.

CONCLUSIONS

For the first time, ionization cluster size probability distributions could be obtained by a small and compact nanodosimeter detector. This signifies the achievement of a critical step toward the wide application of nanodosimetric characterization of radiation types including in clinical environments.

A model of radiation action based on nanodosimetry and the application to ultra-soft X-rays

A radiation action model based on nanodosimetry is presented. It is motivated by the finding that the biological effects of various types of ionizing radiation lack a consistent relation with absorbed dose. It is postulated that the common fundamental cause of these effects is the production of elementary sublesions (DSB), which are created at a rate that is proportional to the probability to produce more than two ionisations within a volume of 10 base pairs of the DNA. The concepts of nanodosimetry allow for a quantitative characterization of this process in terms of the cumulative probability F₂. The induced sublesions can interact in two ways to produce lethal damage. First, if two or more sublesions accumulate in a locally limited spherical volume of 3-10 nm in diameter, clustered DNA damage is produced. Second, consequent interactions or rearrangements of some of the initial damage over larger distances (~ µm) can produce additional lethal damage. From the comparison of theoretical predictions deduced from this concept with experimental data on relative biological effectiveness, a cluster volume with a diameter of 7.5 nm could be determined. It is shown that, for electrons, the predictions agree well with experimental data over a wide energy range. The only free parameter needed to model cell survival is the intersection cross-section which includes all relevant cell-specific factors. Using ultra-soft X-rays it could be shown that the energy dependence of cell survival is directly governed by the nanodosimetric characteristics of the radiation track structure. The cell survival model derived in this work exhibits exponential cell survival at a high dose and a finite gradient of cell survival at vanishing dose, as well as the dependence on dose-rate.

TRACK EVENT THEORY: A CELL SURVIVAL and RBE MODEL CONSISTENT WITH NANODOSIMETRY

A simple model for cell survival which is valid also at high dose has been developed. The model parameters can be traced back to measurable quantities from nanodosimetry. It is assumed that a cell is killed by an event which is defined by two or more double strand breaks in differently sized lethal interaction volumes (LIVs). Two different mechanisms can produce events, one-track events by one-particle track and two-track events by two. One- and two-track events are statistically independent. From the stochastic nature of cell killing which is described by the Poisson distribution, the cell survival probability was derived. The ratio of the number of one- and two-track events can be directly expressed in terms of nanodosimetry by the probability F2 that at least two ionizations are produced in a basic interaction volume (5-10 base pairs). From the model, relative biological effectiveness (RBE) can be derived which depends only on F2 and the size of the LIV. The expression for RBE fits experimental data with satisfying quality.

The probabilities of one- and multi-track events for modeling radiation-induced cell kill

In view of the clinical importance of hypofractionated radiotherapy, track models which are based on multi-hit events are currently reinvestigated. These models are often criticized, because it is believed that the probability of multi-track hits is negligible. In this work, the probabilities for one- and multi-track events are determined for different biological targets. The obtained probabilities can be used with nano-dosimetric cluster size distributions to obtain the parameters of track models. We quantitatively determined the probabilities for one- and multi-track events for 100, 500 and 1000 keV electrons, respectively. It is assumed that the single tracks are statistically independent and follow a Poisson distribution. Three different biological targets were investigated: (1) a DNA strand (2 nm scale); (2) two adjacent chromatin fibers (60 nm); and (3) fiber loops (300 nm). It was shown that the probabilities for one- and multi-track events are increasing with energy, size of the sensitive target structure, and dose. For a 2 × 2 × 2 nm³ target, one-track events are around 10,000 times more frequent than multi-track events. If the size of the sensitive structure is increased to 100-300 nm, the probabilities for one- and multi-track events are of the same order of magnitude. It was shown that target theories can play a role for describing radiation-induced cell death if the targets are of the size of two adjacent chromatin fibers or fiber loops. The obtained probabilities can be used together with the nano-dosimetric cluster size distributions to determine model parameters for target theories.

Evolution of competitive fitness in experimental populations of E. coli: what makes one genotype a better competitor than another?

An important problem in microbial ecology is to identify those phenotypic attributes that are responsible for competitive fitness in a particular environment. Thousands of papers have been published on the physiology, biochemistry, and molecular genetics of Escherichia coli and other bacterial models. Nonetheless, little is known about what makes one genotype a better competitor than another even in such well studied systems. Here, we review experiments to identify the phenotypic bases of improved competitive fitness in twelve E. coli populations that evolved for thousands of generations in a defined environment, in which glucose was the limiting substrate. After 10,000 generations, the average fitness of the derived genotypes had increased by approximately 50% relative to the ancestor, based on competition experiments using marked strains in the same environment. The growth kinetics of the ancestral and derived genotypes showed that the latter have a shorter lag phase upon transfer into fresh medium and a higher maximum growth rate. Competition experiments were also performed in environments where other substrates were substituted for glucose. The derived genotypes are generally more fit in competition for those substrates that use the same mechanism of transport as glucose, which suggests that enhanced transport was an important target of natural selection in the evolutionary environment. All of the derived genotypes produce much larger cells than does the ancestor, even when both types are forced to grow at the same rate. Some but not all, of the derived genotypes also have greatly elevated mutation rates. Efforts are now underway to identify the genetic changes that underlie those phenotypic changes, especially substrate specificity and elevated mutation rate for which there are good candidate loci. Identification and subsequent manipulation of these genes may provide new insights into the reproducibility of adaptive evolution, the importance of co-adapted gene complexes, and the extent to which distinct phenotypes (e.g., substrate specificity and cell size) are affected by the same mutations.