Citations

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.

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.

Abstract Objectives . (1) To examine to what extent the cell- and exposure- specific information neglected in the phenomenological proton relative biological effectiveness (RBE) models could influence the computed RBE in proton therapy. (2) To explore similarities and differences in the formalism and the results between the linear energy transfer (LET)-based phenomenological proton RBE models and the microdosimetry-based Mayo Clinic Florida microdosimetric kinetic model (MCF MKM). (3) To investigate how the relationship between the RBE and the dose-mean proton LET is affected by the proton energy spectrum and the secondary fragments. Approach . We systematically compared six selected phenomenological proton RBE models with the MCF MKM in track-segment simulations, monoenergetic proton beams in a water phantom, and two spread-out Bragg peaks. A representative comparison with in vitro data for human glioblastoma cells (U87 cell line) is also included. Main results . Marked differences were observed between the results of the phenomenological proton RBE models, as reported in previous studies. The dispersion of these models’ results was found to be comparable to the spread in the MCF MKM results obtained by varying the cell-specific parameters neglected in the phenomenological models. Furthermore, while single cell-specific correlation between RBE and the dose-mean proton LET seems reasonable above 2 keV μ m −1 , caution is necessary at lower LET values due to the relevant contribution of secondary fragments. The comparison with in vitro data demonstrates comparable agreement between the MCF MKM predictions and the results of the phenomenological models. Significance . The study highlights the importance of considering cell-specific characteristics and detailed radiation quality information for accurate RBE calculations in proton therapy. Furthermore, these results provide confidence in the use of the MCF MKM for clonogenic survival RBE calculations in proton therapy, offering a more mechanistic approach compared to phenomenological models.

AbstractBackgroundUsing the pencil beam raster scanning method employed at most carbon beam treatment facilities, spots can be moved without interrupting the beam, allowing for the delivery of a dose between spots (move dose). This technique is also known as Dose‐Driven‐Continuous‐Scanning (DDCS). To minimize its impact on HIMAK patient dosimetry, there's an upper limit to the move dose. Spots within a layer are grouped into sets, or “break points,” allowing continuous irradiation. The beam is turned off when transitioning between sets or at the end of a treatment layer or spill. The control system beam‐off is accomplished by turning off the RF Knockout (RFKO) extraction and after a brief delay the High Speed Steering Magnet (HSST) redirects the beam transport away from isocenter to a beam dump.PurposeThe influence of the move dose and beam on/off control on the dose distribution and irradiation time was evaluated by measurements never before reported and modelled for Hitachi Carbon DDCS.MethodWe conducted fixed‐point and scanning irradiation experiments at three different energies, both with and without breakpoints. For fixed‐point irradiation, we utilized a 2D array detector and an oscilloscope to measure beam intensity over time. The oscilloscope data enabled us to confirm beam‐off and beam‐on timing due to breakpoints, as well as the relative timing of the RFKO signal, HSST signal, and dose monitor (DM) signals. From these measurements, we analyzed and modelled the temporal characteristics of the beam intensity. We also developed a model for the spot shape and amplitude at isocenter occurring after the beam‐off signal which we called flap dose and its dependence on beam intensity. In the case of scanning irradiation, we measured move doses using the 2D array detector and compared these measurements with our model.ResultWe observed that the most dominant time variation of the beam intensity was at 1 kHz and its harmonic frequencies. Our findings revealed that the derived beam intensity cannot reach the preset beam intensity when each spot belongs to different breakpoints. The beam‐off time due to breakpoints was approximately 100 ms, while the beam rise time and fall time (tdecay) were remarkably fast, about 10 ms and 0.2 ms, respectively. Moreover, we measured the time lag (tdelay) of approximately 0.2 ms between the RFKO and HSST signals. Since tdelay ≈ tdecay at HIMAK then the HSST is activated after the residual beam intensity, resulting in essentially zero flap dose at isocenter from the HSST. Our measurements of the move dose demonstrated excellent agreement with the modelled move dose.ConclusionWe conducted the first move dose measurement for a Hitachi Carbon synchrotron, and our findings, considering beam on/off control details, indicate that Hitachi's carbon synchrotron provides a stable beam at HIMAK. Our work suggests that measuring both move dose and flap dose should be part of the commissioning process and possibly using our model in the Treatment Planning System (TPS) for new facilities with treatment delivery control systems with higher beam intensities and faster beam‐off control.