Citations

Abstract The repair of DNA double-strand breaks is a crucial yet delicate process which is affected by a multitude of factors. In this study, our goal is to analyse the influence of the linear energy transfer (LET) on the DNA repair kinetics. By utilizing the database of repair of DNA and aggregating the results of 84 experiments, we conduct various model fits to evaluate and compare different hypothesis regarding the effect of LET on the rejoining of DNA ends. Despite the considerable research efforts dedicated to this topic over the past decades, our findings underscore the complexity of the relationship between LET and DNA repair kinetics. This study leverages big data analysis to capture overall trends that single experimental studies might miss, providing a valuable model for understanding how radiation quality impacts DNA damage and subsequent biological effects. Our results highlight the gaps in our current understanding, emphasizing the pressing need for further investigation into this phenomenon.

Proton therapy is increasingly widespread and requires an accelerator to provide the high energy protons. Most often, the accelerators used for proton therapy are cyclotrons and the maximum initial beam energy (MIBE) is about 230 MeV or more to be able to achieve a range of approximately 30 cm in water. We ask whether such a high energy is necessary for adequate dosimetry for pathologies to be treated with proton beams. Eight patients of different clinical sites (brain, prostate, and head and neck cancers) were selected to conduct this study. We analyzed the tumor dose coverage and homogeneity, as well as healthy tissue protection for MIBE values of 120, 160, 180, 200 and 230 MeV. For each patient, a proton plan was developed using the particular MIBE and then using multifield optimization (MFO). In this way, 34 plans in total were generated to fulfill the unique clinical goals. This study found that MIBE of 120 MeV for brain tumors; 160 MeV for head and neck cancer; and remarkably, for prostate cancer, only 160 MeV for one patient case and 180 MeV for the remainder satisfied the clinical goals (words: 187 < approx. 200 words or less)

AbstractBackgroundAdaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model.PurposeTo address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation.MethodsThe PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients.ResultsImplementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.810.05 with the general model, 0.83 for the continual model, 0.83 for the conventional IDOL model to an average of 0.87 with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06 with the general model, 2.84 for the continual model, 2.79 for the conventional IDOL model and 2.36 for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model.ConclusionThe PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows.

AbstractBackgroundPhenomenological relative biological effectiveness (RBE) models for proton therapy, based on the dose‐averaged linear energy transfer (LET), have been developed to address the apparent RBE increase towards the end of the proton range. The results of these phenomenological models substantially differ due to varying empirical assumptions and fitting functions. In contrast, more theory‐based approaches are used in carbon ion radiotherapy, such as the microdosimetric kinetic model (MKM). However, implementing microdosimetry‐based models in LET‐based proton therapy treatment planning systems poses challenges.PurposeThis work presents a LET‐based version of the MKM that is practical for clinical use in proton radiotherapy.MethodsAt first, we derived an approximation of the Mayo Clinic Florida (MCF) MKM for relatively‐sparsely ionizing radiation such as protons. The mathematical formalism of the proposed model is equivalent to the original MKM, but it maintains some key features of the MCF MKM, such as the determination of model parameters from measurable cell characteristics. Subsequently, we carried out Monte Carlo calculations with PHITS in different simulated scenarios to establish a heuristic correlation between microdosimetric quantities and the dose averaged LET of protons.ResultsA simple allometric function was found able to describe the relationship between the dose‐averaged LET of protons and the dose‐mean lineal energy, which includes the contributions of secondary particles. The LET‐based MKM was used to model the in vitro clonogenic survival RBE of five human and rodent cell lines (A549, AG01522, CHO, T98G, and U87) exposed to pristine and spread‐out Bragg peak (SOBP) proton beams. The results of the LET‐based MKM agree well with the biological data in a comparable or better way with respect to the other models included in the study. A sensitivity analysis on the model results was also performed.ConclusionsThe LET‐based MKM integrates the predictive theoretical framework of the MCF MKM with a straightforward mathematical description of the RBE based on the dose‐averaged LET, a physical quantity readily available in modern treatment planning systems for proton therapy.

AbstractBackgroundIn carbon ion radiation therapy (CIRT) the predominant method of irradiation is raster scanning, called dose driven continuous scanning (DDCS) by Hitachi, allowing for continuous synchrotron extraction. The reduction in irradiation time is highly beneficial in minimizing the impact of patient and target movement on dose distribution. The RF knock out (RFKO) slow‐extraction method is commonly used for beam on/off control. When the Hitachi synchrotron receives a beam off signal the control system stops the RFKO and after some delay time (t‐delay) during which the beam intensity declines, a high‐speed steering magnet (HSST) is used to sweep the remaining beam from isocenter to a beam dump for safety reasons. Mayo Clinic Florida (MCF) will use a very short delay of the HSST operation from the RFKO beam OFF signal to minimize the delay time and delayed dose. MCF clinical beam intensity, a tenfold increase over HIMAK, is still less than 100 mMU/ms (approximately 4.9 × 109 pps for 430 MeV/u).PurposeThe rapid beam off control (RBOC) proposed for MCF is associated with the occurrence of flap dose (FD), which refers to the asymmetric shoulder of the spot dose profile formed from the beam bent by HSST deviating from its planned spot position on the isocenter plane. In this study, we quantitatively assessed FD, proposed a treatment planning system (TPS) implementation using a flap spot (FS) and evaluated its impact on dose distribution.MethodThe experiments were conducted at the Osaka Heavy Ion Therapy Center (HIMAK) varying the t‐delay from 0.01 to 1 ms in a research environment to simulate the MCF RBOC. We studied the dependence of FD position on beam transport and its dependence on energy and beam intensity. FD was generated by delivering 10000 continuous spots on the central axis that are occasionally triggered by an external 10 Hz gate signal. Measurements were conducted using an oscilloscope, and the nozzle's spot position monitor (SPM) and dose monitor (DM).ResultAll spot profile data were corrected for the gain of the SPM's beam intensity dependence. FD was determined by fitting the (SPM) Profile data to a double Gaussian. The position of the FS was found to be transport path dependent, with FS occurring on the opposite sides of the scanning x‐direction for vertical and horizontal ports, respectively, as predicted by transport calculations. It was observed that the FD increases with beam intensity and did not exhibit a significant dependence on energy. The effect of FD on treatment planning is shown to have no significant dose impact on the organs at risk (OARs) near the target for clinical beam intensities and a modest increase for very high intensities.ConclusionUsing HIMAK in research mode the implications are that the FD has no clinical impact on the clinical CIRT beam intensities for MCF and maybe planned for higher intensities by incorporating FS into the TPS to predict the modest increased dose to OARs. A method for commissioning and quality assurance of FD has been proposed.

The analytical microdosimetric function (AMF) implemented in the Monte Carlo code PHITS is a unique tool that bridges the gap between macro- and microscopic scales of radiation interactions, enabling accurate microdosimetric calculations over macroscopic bodies. The original AMF was published in 2006, based on the results of track structure calculations. Recently, a newer version of the AMF was proposed, incorporating an improved description of the energy loss at the microscopic scale. This study compares the older and the newer AMFs in computing microdosimetric probability distributions, mean values, and the relative biological effectiveness (RBE). To this end, 16000 microdosimetric lineal energy probability density distributions were simulated with PHITS for ions from 1H to 238U over a broad energy range (1–1000 MeV/n). The newer AMF was found to offer superior performance, particularly for very heavy ions, producing results that align more closely with published in vitro clonogenic survival experiments. These findings suggest that the updated AMF provides a more reliable tool for microdosimetric calculations and RBE modeling, essential for ion radiation therapy and space radiation protection.

AbstractBackgroundIntensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and efficiency.PurposeThis study introduces an innovative Two‐Stage Mixed Integer Linear Programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing strategies.MethodsOur approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three‐phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L∞ strategies. The method's efficacy was tested in eight study cases, using Dose‐Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established methods.ResultsThe proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high‐quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB constraints.ConclusionsOur Two‐Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for improved patient outcomes through more precise and efficient radiation therapy plans.

Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy’s potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice.

AbstractPurposeSkin collimation is a useful tool in electron beam therapy (EBT) to decrease the penumbra at the field edge and minimize dose to nearby superficial organs at risk (OARs), but manually fabricating these collimation devices in the clinic to conform to the patient's anatomy can be a difficult and time intensive process. This work compares two types of patient‐specific skin collimation (in‐house 3D printed and vendor‐provided machined brass) using clinically relevant metrics.MethodsAttenuation measurements were performed to determine the thickness of each material needed to adequately shield both 6 and 9 MeV electron beams. Relative and absolute dose planes at various depths were measured using radiochromic film to compare the surface dose, flatness, and penumbra of the different skin collimation materials.ResultsClinically acceptable thicknesses of each material were determined for both 6 and 9 MeV electron beams. Field width, flatness, and penumbra results between the two systems were very similar and significantly improved compared to measurements performed with no surface collimation.ConclusionBoth skin collimation methods investigated in this work generate sharp penumbras at the field edge and can minimize dose to superficial OARs compared to treatment fields with no surface collimation. The benefits of skin collimation are greatest for lower energy electron beams, and the benefits decrease as the measurement depth increases. Using bolus with skin collimation is recommended to avoid surface dose enhancement seen with collimators placed on the skin surface. Ultimately, the appropriate choice of material will depend on the desire to create these devices in‐house or outsource the fabrication to a vendor.

Abstract Background Rescanning is a common technique used in proton pencil beam scanning to mitigate the interplay effect. Advances in machine operating parameters across different generations of particle therapy systems have led to improvements in beam delivery time (BDT). However, the potential impact of these improvements on the effectiveness of rescanning remains an underexplored area in the existing research. Methods We systematically investigated the impact of proton machine operating parameters on the effectiveness of layer rescanning in mitigating interplay effect during lung SBRT treatment, using the CIRS phantom. Focused on the Hitachi synchrotron particle therapy system, we explored machine operating parameters from our institution's current (2015) and upcoming systems (2025A and 2025B). Accumulated dynamic 4D dose were reconstructed to assess the interplay effect and layer rescanning effectiveness. Results Achieving target coverage and dose homogeneity within 2% deviation required 6, 6, and 20 times layer rescanning for the 2015, 2025A, and 2025B machine parameters, respectively. Beyond this point, further increasing the number of layer rescanning did not further improve the dose distribution. BDTs without rescanning were 50.4, 24.4, and 11.4 s for 2015, 2025A, and 2025B, respectively. However, after incorporating proper number of layer rescanning (six for 2015 and 2025A, 20 for 2025B), BDTs increased to 67.0, 39.6, and 42.3 s for 2015, 2025A, and 2025B machine parameters. Our data also demonstrated the potential problem of false negative and false positive if the randomness of the respiratory phase at which the beam is initiated is not considered in the evaluation of interplay effect. Conclusion The effectiveness of layer rescanning for mitigating interplay effect is affected by machine operating parameters. Therefore, past clinical experiences may not be applicable to modern machines.