Citations

Abstract Purpose Modeling relative biological effectiveness (RBE) is central to carbon ion radiotherapy treatment planning. The modified Microdosimetric Kinetic Model (mMKM) is a clinically established RBE framework that has guided treatment protocols at many existing carbon centers, while the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF MKM) is a recently developed alternative. This work aims to implement the MCF MKM in the open‐source treatment planning system matRad and to quantitatively compare its RBE‐weighted dose predictions with those of the clinically established mMKM using identical physical dose distributions across multiple disease sites. These findings will help assess their dosimetric equivalence and inform protocol development for carbon ion radiotherapy at MCF. Methods Monte Carlo simulations of the MCF carbon beamline were performed to generate physical (IDD, LET, lateral spread) and biological base data for integration of each RBE model into matRad . Treatment plans were generated for six patients, each corresponding to a different disease site, using clinical beam configurations with carbon‐reference dose prescriptions, and plans were optimized using the MCF MKM. To isolate differences attributable solely to the RBE model, the resulting physical dose distributions were held fixed and RBE‐weighted doses were recalculated using the mMKM. Dose volume histogram (DVH) metrics and spatial dose‐difference maps were used to compare target coverage and organ‐at‐risk doses between the two models. Results Across patient cases, RBE‐weighted dose distributions from MCF MKM and mMKM showed strong agreement. Differences in target coverage were small, with CTV D95% differing by less than 1.6% across all disease sites and maximum target dose differences not exceeding 0.88%. Organ‐at‐risk dose deviations were limited, with differences of 3.0% or less across evaluated DVH metrics. Spatial dose‐difference maps showed that the largest discrepancies occurred in regions of steep dose gradients near target to organ‐at‐risk interfaces, while overall dose conformity and plan quality remained comparable between the two models. Conclusions: This study served as the first systematic model comparison of the MCF MKM and mMKM within a treatment planning environment. These findings suggest that the MCF MKM and mMKM produce dosimetrically consistent RBE‐weighted dose predictions under realistic planning conditions using carbon‐reference parameters. Accordingly, the fractionation schemes developed from years of clinical experience with mMKM implementations may serve as a practical foundation for protocol development at MCF.

Abstract Purpose This study investigates a novel optimization framework that integrates the newly developed MCF MKM relative biological effectiveness (RBE) model into intensity modulated particle therapy (IMPT) planning for carbon ion therapy. Unlike conventional RBE models such as MKM or LEM, the MCF MKM provides detailed microdosimetric information, which improves predictive accuracy of cell survival but elevates both memory and time requirement in the optimization process. Furthermore, to the implicit nature of the MCF MKM, the gradient information, which is essential in inverse planning, is hardly accessible. In response, a randomized gradient‐free optimization method (RGFM) was developed to mitigate the computational challenge and achieve improved RBE dose distributions. Materials and Methods An RBE dose optimization problem was formulated based on the discretized MCF MKM, known as the Abridged Microdosimetry Distribution Model (AMDM). The RGFM was derived to find feasible and high‐quality settings of monitor unit for IMPT plans without the need for explicit gradient calculations. To verify the clinical plausibility of the proposed method, it was tested on two clinical scenarios—prostate and brain cases in comparison with benchmark plans derived solely from physical dose optimization. Computational times and dose‐volume histograms (DVHs) for both physical and RBE doses were evaluated. Results The RGFM algorithm successfully optimized the MCF MKM‐based IMPT plans. Compared to the benchmark, the RBE dose distributions showed improved coverage of the target and reduced hot spots. Such improvements were observed to coincide with trade‐offs in physical dose distributions. The complexity of the MCF MKM extended the computational time, yet high‐quality solutions were attainable in a clinically reasonable timeframe. Multi‐field optimization (MFO) further enhanced target coverage and critical organ sparing over single‐field optimization (SFO). Conclusions This proof‐of‐principle study confirmed the feasibility of using a randomized gradient‐free method to optimize RBE dose based on the MCF MKM in IMPT planning for carbon ion therapy. The approach provides a pathway for clinical adoption of advanced RBE models, potentially leading to more biologically informed and effective carbon ion therapy treatments. Future work involves refining algorithms, incorporating parallel computing, and performing additional case studies to improve computational efficiency and generalizability.

Abstract Background The RefleXion X1 is a novel PET/CT‐guided ring gantry linac that introduces unique geometry and workflow compared to conventional systems. Existing QA guidelines (AAPM TG‐142, 148, 306) cover standard linacs and tomotherapy but do not address this platform's needs. Therefore, an automated daily QA solution is required to ensure accurate output, imaging guidance, and couch positioning, which motivated the development and evaluation of the workflow presented in this study. Purpose To develop and evaluate an automated, efficient, and comprehensive daily quality assurance (QA) workflow for a novel PET/CT‐guided ring gantry linear accelerator (RefleXion X1), incorporating tests for dosimetry, imaging guidance, and couch positioning accuracy. Methods An IMRT QA plan was designed following the CT scan of the TomoDose 2D diode detector array (Sun Nuclear, Melbourne, FL) within the RefleXion treatment planning system (TPS). Several spherical targets of varying sizes were contoured to conform the dose distribution to these targets and evaluate beam output, laser localization, profile constancy, and CT‐MV isocenter coincidence. Linear models correlating spatial location of dose peak with intentional shifts in different directions were developed and integrated into an in‐house analysis software, iQA, written in C#. The software was validated for robustness and successfully implemented in clinical practice. Results Validation with 42 intentional shifts showed submillimeter accuracy, with mean deviations of 0.2 mm (X), 0.1 mm (Y), and 0.79 mm (Z), and R 2 values ≥ 0.98. Over 1 year, daily QA data showed stable output constancy (1.006 ± 0.016), left/right target dose constancy (1.007 ± 0.005 and 0.996 ± 0.004), and consistent CT‐MV isocenter offsets (–0.335 ± 0.23 mm in X, 0.19 ± 0.19 mm in Y, and –0.544 ± 0.36 mm in Z). The software also detected a gradual output drift in between, prompting a timely linac adjustment. Conclusion This fully automated, single‐delivery QA workflow enables efficient and robust evaluation of multiple IGRT performance parameters. It improves standardization, reduces human error, and reliably monitors long‐term machine stability. The tools and methodology may be adopted by other clinics seeking a practical and automated daily QA solution. Future developments will focus on incorporating PET QA and rotational couch validations to further enhance the system's comprehensiveness and adaptability.

Abstract Background Carbon ion radiotherapy (CIRT), characterized by its Bragg peak and a reduced lateral scattering compared to proton radiotherapy (PT), offers excellent dose conformity in radiation treatment. Meanwhile, compared to PT, CIRT causes more complex and severe DNA damage in tumor cells, which results in a higher relative biological effectiveness (RBE), making it particularly advantageous in treating radioresistant and hypoxia tissues than conventional radiotherapy. However, CIRT's high RBE and the nuclear fragmentation dose tail outside the target presents challenges in sparing organ at risk (OARs). Combining PT and CIRT offers a potential hybrid modality that leverages the biological effectiveness of carbon ions while minimizing normal tissue toxicity with high biological damage. Purpose Despite the potential of combining PT and CIRT, balancing their respective advantages while mitigating disadvantages remains a significant challenge. This study aims to develop an optimized treatment approach that automatically adjusts the relative contributions of protons and carbon ions, improving target dose conformity while minimizing biological impact on OARs. Methods We propose a biological optimization framework for multi‐modality combining PT and CIRT (CCIPRT). The biological dose is modeled as the product of the physical dose and RBE. Through iterative Jacobian‐based linearization, the biological dose of carbon ion is expressed in a linear model, enabling the formulation of a linear least‐square objective. The biological dose of proton is formulated as a linear model using the physical dose and dose‐averaged linear energy transfer (LET). The joint optimization problem is then solved using iterative convex relaxation and the alternating direction method of multipliers. For comparison, single‐modality PT and CIRT were also implemented. Results When the target dose of CCIPRT is prescribed in terms of the physical dose, compared to PT, CCIPRT increased the mean biological dose in the target by 37.7%, 29.6%, and 31.1% for brain, lung, and abdomen, respectively; compared to CIRT, CCIPRT reduced the biological dose in CTV1cm (ring surrounding CTV with 1 cm width) by 47.2%, 32.3%, and 40.4% for brain, lung, and abdomen, respectively. When the target dose of CCIPRT is prescribed in terms of the biological dose, compared to PT, CCIPRT achieved higher conformity index (CCIPRT: 0.96 and 0.97; PT: 0.93 and 0.96 for brain and liver cases, respectively) and reduced the dose in CTV1cm by 9.5% and 7.0% for brain and liver cases, respectively; compared to CIRT, CCIPRT enhanced healthy tissue sparing, reducing the dose to the body by 7.5% and 9.2% for brain and liver cases, respectively. Conclusions A novel multi‐modality CCIPRT treatment planning is developed with demonstrated superior performance over single‐modality PT or CIRT, with enhanced biological effectiveness and dose conformity compared to PT and reduced OAR dose compared to CIRT.

Abstract Background Dose‐driven continuous scanning (DDCS) enhances the efficiency and precision of proton pencil beam delivery by reducing beam pauses inherent in discrete spot scanning (DSS). However, current DDCS optimization studies using traveling salesman problem (TSP) formulations often rely on fixed beam intensity and computationally expensive interpolation for move spot generation, limiting efficiency and methodological robustness. Purpose This study introduces a Break Spot‐Guided (BSG) method, combined with two acceleration strategies—dose rate skipping and bounding—to optimize beam intensity while minimizing beam delivery time (BDT). In addition, a ‐method is proposed for efficient move spot generation, aiming to balance computational efficiency and dose calculation accuracy. Methods The BSG framework simplifies the original mixed‐integer nonlinear programming (MINLP) problem by transforming it into a series of TSP evaluations using break‐spot dose rates. The two acceleration strategies are integrated to reduce computational complexity. The ‐method is introduced to efficiently generate move spots without compromising dose fidelity. The performance of the BSG method, with and without the acceleration strategies, and the ‐method, was evaluated across multiple clinical cases. Results By jointly optimizing the scan path and dose rate, the proposed BSG‐S‐B method offered the most efficient delivery, achieving a 64% reduction in BDT compared to DSS and a 57% reduction compared to Liu's method in the prostate case. These gains were consistently observed across all cases. Utilizing acceleration strategies, BSG‐S‐B significantly reduced computation time from hours to minutes (e.g., from 10 525.7 to 108.5 s in the prostate case, and from 6202.4 to 129.8 s in the head‐and‐neck case). When combined with the ‐method, it reduced the number of move spots by more than 50%, while still ensuring dosimetric quality. Conclusions The proposed framework significantly improves computational efficiency, making scan path optimization with dose rate selection feasible for clinical DDCS delivery. The ‐method effectively reduces computational complexity while maintaining dose calculation accuracy, demonstrating strong potential for clinical adoption in proton therapy treatment planning.

Abstract Background Cone‐beam computed tomography (CBCT) is essential for image‐guided particle therapy (IGPT), providing daily patient positioning, anatomical monitoring, and treatment verification. However, conventional single‐detector CBCT suffers from poor soft‐tissue contrast, long acquisition times, and motion artifacts, reducing its effectiveness for adaptive radiotherapy workflows. Addressing these challenges requires an advanced CBCT reconstruction approach capable of enhancing image quality while reducing scan time and radiation exposure. Purpose This study introduces a Dual Detector Combined Scanning (DDCS) CBCT reconstruction algorithm to overcome conventional CBCT limitations. By integrating a dual‐source, orthogonal imaging setup, DDCS significantly reduces scanning time and imaging artifacts. A modified Parker‐weighting algorithm further improves image reconstruction accuracy, ensuring high‐fidelity visualization. The goal is to enhance adaptive radiotherapy by improving real‐time patient positioning, dose verification, and motion management. Methods A dual‐detector CBCT system was designed to acquire orthogonal projections simultaneously, reducing scan time and improving data completeness. A modified Parker‐weighting algorithm corrected angular overlap distortions, ensuring better reconstruction accuracy. The system was evaluated using numerical and physical phantom studies to assess spatial resolution, contrast‐to‐noise ratio (CNR), and artifact reduction. Additionally, a dynamic phantom simulated respiratory motion, validating motion robustness for adaptive radiotherapy. Performance was compared against single‐detector CBCT, focusing on image fidelity, noise suppression, and computational efficiency. Results DDCS‐CBCT demonstrated higher CNR, reduced motion artifacts, and improved spatial resolution, leading to more accurate anatomical visualization. The system's efficiency enables faster, more reliable patient setup in IGPT while maintaining a lower imaging dose. Conclusion The proposed DDCS‐CBCT approach significantly improves imaging accuracy, reduces scan time, and enhances real‐time volumetric guidance in IGRT/IGPT. These findings support the clinical feasibility of the DDCS system and its integration into online adaptive radiotherapy workflows.

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.

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.

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.

The scarcity of liver grafts has prompted developments in living donor liver transplantations (LDLT), with previous literature illustrating similar outcomes in recipients compared to deceased donor transplants. However, significant concerns regarding living donor morbidity and mortality have yet to be examined comprehensively. This study aims to provide estimates of the incidence of various outcomes in living liver donors. In this meta-analysis, Medline and Embase were searched from inception to July 2022 for articles assessing the incidence of outcomes in LDLT donors. Complications in the included studies were classified into respective organ systems. Analysis of incidence was conducted using a generalized linear mixed model with Clopper-Pearson intervals. Eighty-seven articles involving 60,829 living liver donors were included. The overall pooled incidence of complications in LDLT donors was 24.7% (CI: 21.6%–28.1%). The incidence of minor complications was 17.3% (CI: 14.7%–20.3%), while the incidence of major complications was lower at 5.5% (CI: 4.5%–6.7%). The overall incidence of donor mortality was 0.06% (CI: 0.0%–0.1%) in 49,027 individuals. Psychological complications (7.6%, CI: 4.9%–11.5%) were the most common among LDLT donors, followed by wound-related (5.2%, CI: 4.4%–6.2%) and respiratory complications (4.9%, CI: 3.8%–6.3%). Conversely, cardiovascular complications had the lowest incidence among the subgroups at 0.8% (CI: 0.4%–1.3%). This study presents the incidence of post-LDLT outcomes in living liver donors, illustrating significant psychological, wound-related, and respiratory complications. While significant advancements in recent decades have contributed towards decreased morbidity in living donors, our findings call for targeted measures and continued efforts to ensure the safety and quality of life of liver donors post-LDLT.