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The original rationale for proton therapy was its highly conformal depth‐dose distributions compared to photons, which allow greater sparing of normal tissues and escalation of tumor doses, thus potentially improving outcomes. Additionally, recent research has revealed previously unrecognized advantages of proton therapy. For instance, the higher relative biological effectiveness (RBE) near the end of the proton range can be exploited to increase the difference in biologically effective dose in tumors versus normal tissues. Moreover, the smaller “dose bath,” that is, the compact nature of proton dose distributions, has been found to reduce the exposure of circulating lymphocytes and the immune organs at risk. There is emerging evidence that the resulting sparing of the immune system has the potential to improve outcomes. Protons accelerated to energies ranging from 70 to 250 MeV enter the treatment head mounted typically on a rotating gantry. Initially, the beams of protons are narrow and, to be suitable for treatments, must be spread laterally and longitudinally and shaped appropriately. Such spreading and shaping may be accomplished electro‐mechanically for the “passively scattered proton therapy” (PSPT) mode; or it may be achieved through magnetic scanning of thin “beamlets” of protons. Intensities of scanning beamlets are optimized to deliver intensity‐modulated proton therapy (IMPT), which optimally balances tumor dose and the sparing of normal tissues. IMPT is presumably the most powerful form of proton therapy. The planning and evaluation of proton dose distributions require substantially different techniques compared to photon therapy. This is mainly due to the fact that proton dose distributions are highly sensitive to inter‐ and intra‐fractional variations in anatomy. In addition, for the same physical dose, the biological effectiveness of protons is different from photons. In the current practice of proton therapy, the RBE is simplistically assumed to have a constant value of 1.1. In reality, the RBE is variable and a highly complex function of numerous variables including energy of protons, dose per fraction, tissue and its environment, cell type, end point, and possibly other factors. While the theoretical potential of proton therapy is high, the clinical evidence in support of its use has so far been mixed. The uncertainties and assumptions mentioned above and the limitations of the still evolving technology of proton therapy may have diminished its true clinical potential. Although promising results have been reported for many types of cancers, they are often based on small studies. At the same time, there have been reports of unforeseen toxicities. Furthermore, because of the high cost of proton therapy, questions are often raised about its value. The general consensus is that there is a need for continued improvement in the state of the art of proton therapy. There is also a need to generate high level evidence of the potential of protons. Fortuitously, such efforts are taking place currently. Current research, aimed at enhancing the therapeutic potential of proton therapy, includes the determination and mitigation of the impact of the physical uncertainties on proton dose distributions through advanced image‐guidance and adaptive radiotherapy techniques. Since residual uncertainties will remain, robustness evaluation and robust optimization techniques are being developed to render dose distributions more resilient and to improve confidence in them. The ongoing research also includes improving our understanding of the biological and immunomodulatory effects of proton therapy. Such research and continuing technological advancements in planning and delivery methods are likely to help demonstrate the superiority of protons.

Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.

The choice of appropriate physical quantities to characterize the biological effects of ionizing radiation has evolved over time coupled with advances in scientific understanding. The basic hypothesis in radiation dosimetry is that the energy deposited by ionizing radiation initiates all the consequences of exposure in a biological sample (e.g., DNA damage, reproductive cell death). Physical quantities defined to characterize energy deposition have included dose, a measure of the mean energy imparted per unit mass of the target, and linear energy transfer (LET), a measure of the mean energy deposition per unit distance that charged particles traverse in a medium. The primary advantage of using the “dose and LET” physical system is its relative simplicity, especially for presenting and recording results. Inclusion of additional information such as the energy spectrum of charged particles renders this approach adequate to describe the biological effects of large dose levels from homogeneous sources. The primary disadvantage of this system is that it does not provide a unique description of the stochastic nature of radiation interactions. We and others have used dose-averaged LET (LET d ) as a correlative physical quantity to the relative biological effectiveness (RBE) of proton beams. This approach is based on established experimental findings that proton RBE increases with LET d . However, this approach might not be applicable to intensity-modulated proton therapy or other applications in which the proton energy spectrum is highly heterogeneous. In the current study, we irradiated cancer cells with scanning proton beams with identical LET d (3.4 keV/µm) but arising from two different proton energy/LET spectra (a narrow spectrum in group 1 and a widespread heterogeneous spectrum in group 2). Clonogenic survival after irradiation revealed significant differences in RBE at any cell surviving fraction: e.g., at a surviving fraction of 0.1, the RBE was 0.97 ± 0.03 in group 1 and 1.16 ± 0.04 in group 2 ( p ≤0.01), validating our hypothesis that LET d alone may not adequately indicate proton RBE. Further analysis showed that microdosimetric spectrum (the probability density function of the stochastic physical quantity lineal energy y ) was helpful for interpreting observed differences in biological effects. However, more accurate use of microdosimetric spectrum to quantify RBE requires a cell line–specific mechanistic model.

The physical properties of particles used in radiation therapy, such as protons, have been well characterized and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE.

We introduce an approach for global fitting of the recently published high-throughput and high accuracy clonogenic cell-survival data for therapeutic scanned proton beams. Our fitting procedure accounts for the correlation between the cell-survival, the absorbed (physical) dose and the proton linear energy transfer (LET). The fitting polynomials and constraints have been constructed upon generalization of the microdosimetric kinetic model (gMKM) adapted to account for the low energy and high lineal-energy spectrum of the beam where the current radiobiological models may underestimate the reported relative biological effectiveness (RBE). The parameters ( α , β ) of the linear-quadratic (LQ) model calculated by the presented method reveal a smooth transition from low to high LETs which is an advantage of the current method over methods previously employed to fit the same clonogenic data. Finally, the presented approach provides insight into underlying microscopic mechanisms which, with future study, may help to elucidate radiobiological responses along the Bragg curve and resolve discrepancies between experimental data and current RBE models.

In current treatment plans of intensity-modulated proton therapy, high-energy beams are usually assigned larger weights than low-energy beams. Using this form of beam delivery strategy cannot effectively use the biological advantages of low-energy and high-linear energy transfer (LET) protons present within the Bragg peak. However, the planning optimizer can be adjusted to alter the intensity of each beamlet, thus maintaining an identical target dose while increasing the weights of low-energy beams to elevate the LET therein. The objective of this study was to experimentally validate the enhanced biological effects using a novel beam delivery strategy with elevated LET. We used Monte Carlo and optimization algorithms to generate two different intensity-modulation patterns, namely to form a downslope and a flat dose field in the target. We spatially mapped the biological effects using high-content automated assays by employing an upgraded biophysical system with improved accuracy and precision of collected data. In vitro results in cancer cells show that using two opposed downslope fields results in a more biologically effective dose, which may have the clinical potential to increase the therapeutic index of proton therapy.

Background To establish the feasibility of the dosimetric compliance criteria of the RTOG 1308 trial through testing against Intensity Modulation Radiation Therapy (IMRT) and Passive Scattering Proton Therapy (PSPT) plans. Methods Twenty-six lung IMRT and 26 proton PSPT plans were included in the study. Dose Volume Histograms (DVHs) for targets and normal structures were analyzed. The quality of IMRT plans was assessed using a knowledge-based engineering tool. Results Most of the RTOG 1308 dosimetric criteria were achieved. The deviation unacceptable rates were less than 10 % for most criteria; however, a deviation unacceptable rate of more than 20 % was computed for the planning target volume minimum dose compliance criterion. Dose parameters for the target volume were very close for the IMRT and PSPT plans. However, the PSPT plans led to lower dose values for normal structures. The dose parameters in which PSPT plans resulted in lower values than IMRT plans were: lung V 5Gy (%) (34.4 in PSPT and 47.2 in IMRT); maximum spinal cord dose (31.7 Gy in PSPT and 43.5 Gy in IMRT); heart V 5Gy (%) (19 in PSPT and 47 in IMRT); heart V 30Gy (%) (11 in PSPT and 19 in IMRT); heart V 45Gy (%) (7.8 in PSPT and 12.1 in IMRT); heart V 50% (Gy) (7.1 in PSPT and 9.8 in IMRT) and mean heart dose (7.7 Gy in PSPT and 14.9 Gy in IMRT). Conclusions The revised RTOG 1308 dosimetric compliance criteria are feasible and achievable.

The EBT‐XD model of Gafchromic™ films has a broader optimal dynamic dose range, up to 40 Gy, compared with its predecessor models. This characteristic has made EBT‐XD films suitable for high‐dose applications, such as stereotactic body radiotherapy and stereotactic radiosurgery, as well as ultra‐high dose rate FLASH radiotherapy. The purpose of the current study was to characterize the dependence of EBT‐XD film response on linear energy transfer (LET) and dose rate of therapeutic protons from a synchrotron. A clinical spot‐scanning proton beam was used to study LET dependence at three dose‐averaged LET values of 1.0 keV/μm, 3.6 keV/μm, and 7.6 keV/μm. A research proton beamline was used to study dose rate dependence at 150 Gy/s in the FLASH mode and 0.3 Gy/s in the non‐FLASH mode. Film response data from dose‐averaged LET values of 0.9 keV/μm and 9.0 keV/μm of the proton FLASH beam were also compared. Film response data from a clinical 6‐MV photon beam were used as a reference. Both the gray value method and optical density (OD) method were used in film calibration. Calibration results using a specific OD calculation method and a generic OD calculation method were compared. The four‐parameter NIH Rodbard function and three‐parameter rational function were compared in fitting the calibration curves. Experimental results showed that the response of EBT‐XD film is proton LET dependent, but independent of dose rate. Goodness‐of‐fit analysis showed that using the NIH Rodbard function is superior for both protons and photons. Using the “specific OD + NIH Rodbard function” method for EBT‐XD film calibration is recommended. The results of dose‐rate independence of EBT‐XD film to protons are shown in Figure G1. The FLASH dose rate is 150 Gy/s and the non‐FLASH dose rate is 0.3 Gy/s. The linear energy transfer dependence of EBT‐XD film to proton beams is shown in Figure G2. Film calibration curves from three different dose‐averaged linear energy transfer values (1.0 keV/μm, 3.6 keV/μm, and 7.6 keV/μm) using clinical 87.2 MeV protons are compared in Figure G2(A). Film calibration curves from two dose‐averaged linear energy transfer values (0.9 keV/μm and 9.0 keV/μm) using FLASH 87.2 MeV protons are compared in Figure G2(B).

Electric vehicles (EVs) are rapidly becoming the future of transportation. To support this shift, EV systems must be made more reliable, efficient, and autonomous. One of the key limitations of current EVs is their reliance on wired charging, which is impractical for vehicles that are expected to operate with minimal human intervention. To address this, wireless power transfer (WPT) has emerged as a promising alternative, especially in the form of dynamic wireless charging systems that enable EVs to recharge while in motion. This work focuses on reviewing and analyzing different dynamic wireless electric vehicle charging (DWEVC) systems for EVs. Unlike static charging, where the vehicle remains parked, dynamic systems allow seamless energy transfer during transit. By enabling continuous charging, the need for large battery capacities is reduced without compromising travel range, leading to lighter vehicles and better efficiency. The implementation also considers safety standards and adverse impacts, ensuring that the system is both practical and safe for everyday use. The novelty lies in combining wireless dynamic charging with vehicle automation, creating a self-sustaining charging model for future EVs. The results obtained from the system show successful wireless power transfer and integration with sensor-based vehicle detection and payment mechanisms, marking a step forward toward scalable and intelligent EV infrastructure.