Tan, Jun

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 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.

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.

Abstract Objective . To investigate the impact of scan path optimization on the dose accuracy and beam delivery time (BDT) of proton pencil beam scanning in the dose-driven continuous scanning (DDCS). Approach . A diverse set of six clinical plans, representing various spot patterns and treatment sites, was used to evaluate the effectiveness of scan time optimization and scan length optimization. The DDCS dose discrepancy and BDT with optimized scan paths was compared to the default serpentine scan path. Main results . Both scan time optimization and scan path optimization were able to reduce the DDCS dose discrepancy compared to the default serpentine scan path. All plans, except for the layer repainting lung plan, achieved a 2%/2 mm gamma pass rate of over 99% and less than 1% PTV DVH root mean square error (RMSE) through scan path optimization. In the case of the layer repainting lung plan, when compared to the default serpentine scan path, the 2%/2 mm gamma pass rate showed improvements from 91.3% to 93.1% and 95.8%, while the PTV DVH RMSE decreased from 2.1% to 1.7% and 1.1% for scan time optimization and scan length optimization, respectively. Although scan time optimization resulted in shorter total scan times for all plans compared to the default scan path and scan length optimization tended to have longer total scan times. However, due to the short total scan times and their minimal contribution to the total BDT, the impact of scan path optimization on the total BDT was practically negligible. Significance . Both scan time optimization and scan length optimization proved to be effective in minimizing DDCS dose discrepancy. No definitive winner can be determined between these two optimization approaches. Both scan time and scan length optimization had minimal effect on the total BDT.

Pencil beam scanning delivered with continuous scanning has several advantages over conventional discrete spot scanning. Such advantages include improved beam delivery efficiency and reduced beam delivery time. However, a move dose is delivered between consecutive spots with continuous scanning, and current treatment planning systems do not take this into account. Therefore, continuous scanning and discrete spot plans have an inherent dose discrepancy. Using the operating parameters of the state-of-the-art particle therapy system, we conducted a proof-of-concept study in which we systematically generated 28 plans for cubic targets with different combinations of plan parameters and simulated the dose discrepancies between continuous scanning and a planned one. A nomograph to guide the selection of plan parameters was developed to reduce the dose discrepancy. The effectiveness of the nomograph was evaluated with two clinical cases (one prostate and one liver). Plans with parameters guided by the nomograph decreased dose discrepancy than those used standard plan parameters. Specifically, the 2%/2 mm gamma passing rate increased from 96.3% to 100% for the prostate case and from 97.8% to 99.7% for the liver case. The CTV DVH root mean square error decreased from 2.2% to 0.2% for the prostate case and from 1.8% to 0.9% for the liver case. The decreased dose discrepancy may allow the relaxing of the delivery constraint for some cases, leading to greater benefits in continuous scanning. Further investigation is warranted.