Traditionally, dose in proton radiotherapy is prescribed as Gy(RBE) by scaling up the physical dose by 10%. The relative biological effectiveness (RBE) of protons is considered to vary with dose-averaged linear energy transfer (LET(d)), dose (d) and (α/β)(x). The increase of RBE with depth causes a shift of the falloff of the beam, i.e. a change of the beam range. The magnitude of this shift will depend on dose and (α/β)(x). The aim of this project was to quantify the dependence of the range shift on these parameters. Three double-scattered beams of different ranges incident on a computational phantom consisting of different regions of interest (ROIs) were used. Each ROI was assigned with (α/β)(x) values between 0.5 and 20 Gy. The distribution of LET(d) within each ROI was obtained from a Monte Carlo simulation. The LET(d) distribution depends on the beam energy and thus its nominal range. The RBE values within the ROIs were calculated for doses between 1 and 15 Gy using an in-house developed biophysical model. Dose-volume histograms of the RBE-weighted doses were extracted for each ROI for a 'fixed RBE' (RBE = 1.1) and a 'variable RBE' (RBE = f (d, α/β, LET(d))), and the percentage difference in range was obtained from the difference of the percentage volumes at the distal 80% of the dose. Range differences in normal tissue ((α/β)(x) = 3 Gy) of the order of 3-2 mm were obtained, respectively, for a shallow (physical range 4.8 cm) and a deep (physical range 12.8 cm) beam, when a dose of 1 Gy normalized to the mid-SOBP was delivered. As the dose increased to 15 Gy, the variable RBE decreases below 1.1 which induces ranges of about 1 mm shorter than those obtained with an RBE of 1.1. The shift in the range of an SOBP when comparing biological dose distributions obtained with a fixed or a variable RBE was quantified as a function of dose, (α/β)(x) and physical range (as a surrogate of the initial beam energy). The shift increases with the physical range but decreases with increasing dose or (α/β)(x). The results of our study allow a quantitative consideration of RBE-caused range uncertainties as a function of treatment site and dose in treatment planning.

We demonstrated feasibility of super-resolution imaging through high-index microspheres embedded in transparent elastomers. We performed imaging, with resolution improvement by a factor of two, by using implanted barium titanate glass microspheres (diameters $\sim 30–150\mathrm{\mu m}$ and refractive index $\sim 1.9–2.1$) in a thin film of polydimethylsiloxane elastomer placed over the specimen. Microsphere-assisted imaging technique is a promising candidate for applications in cancer research. As a proof-of-principle, we used microsphere-assisted imaging technique for the observation of radiation-induced $\gamma$-H2AX foci formation in U87 human glioblastoma cells irradiated by clinical proton beams.

Proton relative biological effectiveness (RBE) is known to depend on the (α/β)x of irradiated tissues, with evidence of ∼60% variation over (α/β)x values from 1-10 Gy. The range of (α/β)x values reported for prostate tumors (1.2-5.0 Gy), brain tumors (10-15 Gy) and liver tumors (13-17 Gy) imply that the proton RBE for these tissues could vary significantly compared to the commonly used generic value of 1.1. Our aim is to evaluate the impact of this uncertainty on the proton dose in Gy(RBE) absorbed in normal and tumor tissues. This evaluation was performed for standard and hypofractionated regimens. RBE-weighted total dose (RWTD) distributions for 15 patients (five prostate tumors, five brain tumors and five liver tumors) were calculated using an in-house developed RBE model as a function of dose, dose-averaged linear energy transfer (LETd) and (α/β)x. Variations of the dose-volume histograms (DVHs) for the gross tumor volume (GTV) and the organs at risk due to changes of (α/β)x and fractionation regimen were calculated and the RWTD received by 10% and 90% of the organ volume reported. The goodness of the plan, bearing the uncertainties, was then evaluated compared to the delivered plan, which considers a constant RBE of 1.1. For standard fractionated regimens, the prostate tumors, liver tumors and all critical structures in the brain showed typically larger RBE values than 1.1. However, in hypofractionated regimens lower values of RBE than 1.1 were observed in most cases. Based on DVH analysis we found that the RBE variations were clinically significant in particular for the prostate GTV and the critical structures in the brain. Despite the uncertainties in the biological input parameters when estimating RBE values, the results show that the use of a variable RBE with dose, LETd and (α/β)x could help to further optimize the target dose in proton treatment planning. Most importantly, this study shows that the consideration of RBE variations could influence the comparison of proton and photon treatments in clinical trials, in particular in the case of the prostate.

The development of rotational proton therapy plans based on a pencil-beam-scanning (PBS) system has been limited, among several other factors, by the energy-switching time between layers, a system-dependent parameter that ranges between a fraction of a second and several seconds. We are investigating mono- and bi-energetic rotational proton modulated arc therapy (PMAT) solutions that would not be affected by long energy switching times. In this context, a systematic selection of the optimal proton energy for each arc is vital. We present a treatment planning comparison of four different range selection methods, analyzing the dosimetric outcomes of the resulting treatment plans created with the ranges obtained. Given the patient geometry and arc definition (gantry and couch trajectories, snout elevation) our in-house treatment planning system (TPS) FoCa was used to find the maximum, medial and minimum water-equivalent thicknesses (WETs) of the target viewed from all possible field orientations. Optimal ranges were subsequently determined using four methods: (1) by dividing the max/min WET interval into equal steps, (2) by taking the average target midpoints from each field, (3) by taking the average WET of all voxels from all field orientations, and (4) by minimizing the fraction of the target which cannot be reached from any of the available angles. After the range (for mono-energetic plans) or ranges (for bi-energetic plans) were selected, the commercial clinical TPS in use in our institution (Varian Eclipse ^™ ) was used to produce the PMAT plans using multifield optimization. Linear energy transfer (LET) distributions of all plans were also calculated using FoCa and compared among the different methods. Mono- and bi-energetic PMAT plans, composed of a single 180° arc, were created for two patient geometries: a C-shaped target located in the mediastinal area of a thoracic tissue-equivalent phantom and a small brain tumor located directly above the brainstem. All plans were optimized using the same procedure to (1) achieve target coverage, (2) reduce dose to OAR and (3) limit dose hot spots in the target. Final outcomes were compared in terms of the resulting dose and LET distributions. Data shows little significant differences among the four studied methods, with superior results obtained with mono-energetic plans. A streamlined systematic method has been implemented in an in-house TPS to find the optimal range to maximize target coverage with rotational mono- or bi-energetic PBS rotational plans by minimizing the fraction of the target that cannot be reached by any direction.

In order to integrate radiobiological modelling with clinical treatment planning for proton radiotherapy, we extended our in-house treatment planning system FoCa with a 3D analytical algorithm to calculate linear energy transfer (LET) in voxelized patient geometries. Both active scanning and passive scattering delivery modalities are supported. The analytical calculation is much faster than the Monte-Carlo (MC) method and it can be implemented in the inverse treatment planning optimization suite, allowing us to create LET-based objectives in inverse planning. The LET was calculated by combining a 1D analytical approach including a novel correction for secondary protons with pencil-beam type LET-kernels. Then, these LET kernels were inserted into the proton-convolution-superposition algorithm in FoCa. The analytical LET distributions were benchmarked against MC simulations carried out in Geant4. A cohort of simple phantom and patient plans representing a wide variety of sites (prostate, lung, brain, head and neck) was selected. The calculation algorithm was able to reproduce the MC LET to within 6% (1 standard deviation) for low-LET areas (under 1.7 keV μ m ⁻¹ ) and within 22% for the high-LET areas above that threshold. The dose and LET distributions can be further extended, using radiobiological models, to include radiobiological effectiveness (RBE) calculations in the treatment planning system. This implementation also allows for radiobiological optimization of treatments by including RBE-weighted dose constraints in the inverse treatment planning process.

Proton arc therapy (PAT) has been proposed as a possible evolution for proton therapy. This commentary uses dosimetric and cancer risk evaluations from earlier studies to compare PAT with intensity modulated proton therapy. It is concluded that, although PAT may not produce better physical dose distributions than intensity modulated proton therapy, the radiobiological considerations associated with particular PAT techniques could offer the possibility of an increased therapeutic index.