One major rationale for the application of heavy ion beams in tumour therapy is their increased relative biological effectiveness (RBE). The complex dependencies of the RBE on dose, biological endpoint, position in the field etc require the use of biophysical models in treatment planning and clinical analysis. This study aims to introduce a new software, named 'Survival', to facilitate the radiobiological computations needed in ion therapy. The simulation toolkit was written in C++ and it was developed with a modular architecture in order to easily incorporate different radiobiological models. The following models were successfully implemented: the local effect model (LEM, version I, II and III) and variants of the microdosimetric-kinetic model (MKM). Different numerical evaluation approaches were also implemented: Monte Carlo (MC) numerical methods and a set of faster analytical approximations. Among the possible applications, the toolkit was used to reproduce the RBE versus LET for different ions (proton, He, C, O, Ne) and different cell lines (CHO, HSG). Intercomparison between different models (LEM and MKM) and computational approaches (MC and fast approximations) were performed. The developed software could represent an important tool for the evaluation of the biological effectiveness of charged particles in ion beam therapy, in particular when coupled with treatment simulations. Its modular architecture facilitates benchmarking and inter-comparison between different models and evaluation approaches. The code is open source (GPL2 license) and available at https://github.com/batuff/Survival.

ICTR-PHE 2016 S13The finite range of a proton beam or heavier charged particle beam can be a double-edged sword.While it is the biggest physical/dosimetric advantage of particle beam therapy, the range over-or undershoot typically requires extra margins that can compromise the conformality of the dose distributions substantially.Recent developments aim at imaging the range of the particle beam in the patient, to (1) verify the range, and (2) potentially correct for any range discrepancies.We will review the state of the art of imaging for range assessment, focusing in particular on prompt gamma imaging.Significant progress has recently been made in the field of prompt gamma imaging.Range errors of 1 mm are detectable in phantoms.Spectroscopic methods potentially allow one to obtain information about the elemental composition of the tissue.Commercial systems are under development and have recently been tested in the clinic.In addition to systems consisting of a detector-collimator combination, Compton cameras are under development for the 3D reconstruction of the prompt gamma sources.Prompt gamma imaging competes with other range imaging techniques such as PET imaging of oxygen-15 and carbon-11, and MRI imaging of the biological changes resulting from irradiating tissues.Another recent development is thermoacoustic imaging, utilizing the ultrasound signal that a high intensity particle beam produces in tissue due to quick thermal expansion.Once the particle range in the patient can be measured (a) in the patient, (b) in realtime, (c) with millimeter accuracy, the range margins can be reduced, resulting in significant improvements of treatment quality for many disease sites.Unfortunately, none of the techniques developed so far fulfill all criteria (a,b,c) above.Millimeter accuracy is sometimes achievable but not in patients or not in all patients.Realtime measurement and control is only feasible with prompt gamma and thermo-acoustic imaging.Of the techniques currently under development, it appears that prompt gamma imaging has the best chance of fulfilling all the criteria above and becoming a valuable tool for particle range control in the clinic in the near future.

Introduction: The increasing complexity of radiotherapy equipment (e.g. IMRT) requires increasing accuracy in QA procedures. The system used to verify the adherence between planned and delivered dose should be independent of the treatment planning system. Monte Carlo (MC) algorithms are the reference choice. They allow computing the dose taking into account patient heterogeneities and dosimetric effects generated by multi-leaf collimators (MLCs). In line with these motivations, we developed a MC-based tool for dosimetric purposes and the independent verification of planned dose distributions.

Material and Method: This work was carried out with MCNP and Geant4 codes.The 10x10x10 cm3 cubic water phantom and a tumor region with a size of 1x1x1 cm3 were simulated.Factors such as different concentrations and GNP sizes were implemented into the simulation, so as to obtain the optimum results, specifying the maximum absorbed dose within the tumor while sparing healthy tissue.In a certain concentration, different sizes of GNPs including 30, 50, 70 and 100 nm were defined within the tumor and the absorbed dose by the GNPs-loaded tumor were calculated for different sizes.Similarly, the absorbed dose was calculated for different concentrations of 7, 10, 18 and 30 (mg Au/ gram of tumor) in a certain size of GNPs.The dose enhancement factor which is defined as the ratio of the absorbed dose by the tumor in the presence of nanoparticles to the absorbed dose by the same organ in the absence of nanoparticles was estimated for different concentrations and sizes of GNPs. Results and Conclusion:The calculations show results for different sizes and concentrations and a comparison is made between the two Monte Carlo codes (MCNP and Geant4).In a certain diameter of GNPs the higher concentration made more increase in absorbed does by the tumor.In a certain concentration, higher size of GNPs made higher absorbed dose by the tumor.Given the fact that therapeutic applications of GNPs in acquiring the proper DEF have demanded much attention in recent years, defining the proper size and concentration would be considered extremely vital for pre-treatment plans.

The calculation algorithm of a modern treatment planning system for ion-beam radiotherapy should ideally be able to deal with different ion species (e.g. protons and carbon ions), to provide relative biological effectiveness (RBE) evaluations and to describe different beam lines. In this work we propose a new approach for ion irradiation outcomes computations, the beamlet superposition (BS) model, which satisfies these requirements. This model applies and extends the concepts of previous fluence-weighted pencil-beam algorithms to quantities of radiobiological interest other than dose, i.e. RBE- and LET-related quantities. It describes an ion beam through a beam-line specific, weighted superposition of universal beamlets. The universal physical and radiobiological irradiation effect of the beamlets on a representative set of water-like tissues is evaluated once, coupling the per-track information derived from FLUKA Monte Carlo simulations with the radiobiological effectiveness provided by the microdosimetric kinetic model and the local effect model. Thanks to an extension of the superposition concept, the beamlet irradiation action superposition is applicable for the evaluation of dose, RBE and LET distributions. The weight function for the beamlets superposition is derived from the beam phase space density at the patient entrance. A general beam model commissioning procedure is proposed, which has successfully been tested on the CNAO beam line. The BS model provides the evaluation of different irradiation quantities for different ions, the adaptability permitted by weight functions and the evaluation speed of analitical approaches. Benchmarking plans in simple geometries and clinical plans are shown to demonstrate the model capabilities.

Purpose:The reduced concentration of oxygen in cells (hypoxia) results in a lower cell death rate after irradiation that can lead to treatment failure. The effect can be expressed by the oxygen enhancement ratio (OER). So far, only few attempts to include OER in treatment planning for ion beam therapy were made, which are based on the dose averaged LET estimates and do not distinguish among ion species and fractionation schemes. To overcome these limitations, we implemented a new OER model and used it to estimate tumor control in clinical cases.Methods:The model, based on the microdosimetric kinetic model, was benchmarked with in‐vitro data from different ions irradiation. It was included in the simulation of treatments of a set of clinical cases (glioblastoma) using p, Li, He, C and O ion beams. Tumor Control Probability (TCP) was estimated as a function of oxygen partial pressure, dose per fraction and primary ion type.Results:The modelized OER was found to be strongly dependent on both LET and ion type, and showed a decreasing OER for increasing dose per fraction with a slope that depends on the LET and ion type, in good agreement with the experimental data. In the clinical cases studied, an increase in TCP by increasing ion charge and dose per fraction (more than 30% variation from p to O for moderate hypoxia) was found. Higher OER decrease rates as function of dose per fraction were found for lighter ions (up to 20% varying from 2 to 8 Gy(RBE)).Conclusions:A novel modeling of the OER that explicitly includes the dependence on ion type and dose per fraction was implemented. The model was exploited to evaluate the impact of hypoxia in ion beam radiotherapy, facilitating the identification of the treatment condition optimality, including fractionation scheme and ion type.

3rd ESTRO Forum 2015 doubling of risk of hematologic grade ≥3 toxicity based on previously published NTCP parameter estimates.NTCP modeling well predicted toxicity using IMRT in this sample.Predicted toxicity using CRT lay marginally outside 95% CI NTCP modeling in this sample.

PURPOSEThis paper describes the system for the dose delivery currently used at the Centro Nazionale di Adroterapia Oncologica (CNAO) for ion beam modulated scanning radiotherapy.METHODSCNAO Foundation, Istituto Nazionale di Fisica Nucleare and University of Torino have designed, built, and commissioned a dose delivery system (DDS) to monitor and guide ion beams accelerated by a dedicated synchrotron and to distribute the dose with a full 3D scanning technique. Protons and carbon ions are provided for a wide range of energies in order to cover a sizable span of treatment depths. The target volume, segmented in several layers orthogonally to the beam direction, is irradiated by thousands of pencil beams which must be steered and held to the prescribed positions until the prescribed number of particles has been delivered. For the CNAO beam lines, these operations are performed by the DDS. The main components of this system are two independent beam monitoring detectors, called BOX1 and BOX2, interfaced with two control systems performing the tasks of real-time fast and slow control, and connected to the scanning magnets and the beam chopper. As a reaction to any condition leading to a potential hazard, a DDS interlock signal is sent to the patient interlock system which immediately stops the irradiation. The essential tasks and operations performed by the DDS are described following the data flow from the treatment planning system through the end of the treatment delivery.RESULTSThe ability of the DDS to guarantee a safe and accurate treatment was validated during the commissioning phase by means of checks of the charge collection efficiency, gain uniformity of the chambers, and 2D dose distribution homogeneity and stability. A high level of reliability and robustness has been proven by three years of system activity needing rarely more than regular maintenance and working with 100% uptime. Four identical and independent DDS devices have been tested showing comparable performances and are presently in use on the CNAO beam lines for clinical activity.CONCLUSIONSThe dose delivery system described in this paper is one among the few worldwide existing systems to operate ion beam for modulated scanning radiotherapy. At the time of writing, it has been used to treat more than 350 patients and it has proven to guide and control the therapeutic pencil beams reaching performances well above clinical requirements. In particular, in terms of dose accuracy and stability, daily quality assurance measurements have shown dose deviations always lower than the acceptance threshold of 5% and 2.5%, respectively.

The relative biological effectiveness (RBE) concept is commonly used in treatment planning for ion beam therapy. Whether models based on in vitro/in vivo RBE data can be used to predict human response to treatments is an open issue. In this work an alternative method, based on an effective radiobiological parameterization directly derived from clinical data, is presented. The method has been applied to the analysis of prostate cancer trials with protons and carbon ions.Prostate cancer trials with proton and carbon ion beams reporting 5 year-local control (LC5) and grade 2 (G2) or higher genitourinary toxicity rates (TOX) were selected from literature to test the method. Treatment simulations were performed on a representative subset of patients to produce dose and linear energy transfer distribution, which were used as explicative physical variables for the radiobiological modelling. Two models were taken into consideration: the microdosimetric kinetic model (MKM) and a linear model (LM). The radiobiological parameters of the LM and MKM were obtained by coupling them with the tumor control probability and normal tissue complication probability models to fit the LC5 and TOX data through likelihood maximization. The model ranking was based on the Akaike information criterion.Results showed large confidence intervals due to the limited variety of available treatment schedules. RBE values, such as RBE = 1.1 for protons in the treated volume, were derived as a by-product of the method, showing a consistency with current approaches. Carbon ion RBE values were also derived, showing lower values than those assumed for the original treatment planning in the target region, whereas higher values were found in the bladder. Most importantly, this work shows the possibility to infer the radiobiological parametrization for proton and carbon ion treatment directly from clinical data.