Citations

ABSTRACT Background Radiotherapy is a cornerstone of treatment for pediatric central nervous system (CNS) tumors, but dose selection remains uniform despite interpatient variability in tumor radiosensitivity. This is consequential in children, for whom toxicity can have lifelong effects. Genomic-adjusted radiation dose (GARD) integrates tumor genomics with delivered radiation dose to quantify the biological effect of radiotherapy and has been validated across adult malignancies. Its relevance in pediatric CNS tumors is unknown. Methods We performed a cohort study using gene expression and data from 246 pediatric patients with high-grade glioma, medulloblastoma, or ependymoma from the Children’s Brain Tumor Network. GARD was calculated using a sequencing-adapted radiosensitivity index integrated with radiation dose through the linear–quadratic model. Associations of GARD and physical radiation dose with event-free and overall survival were evaluated using Cox proportional hazards models stratified by tumor type and anatomic location. Patients who did not receive radiotherapy comprised a negative control cohort (sham-GARD). Results Among patients receiving radiotherapy, physical dose was uniform, whereas GARD showed substantial interpatient variability in biological effect. Higher GARD was associated with improved event-free survival (hazard ratio [HR] 0·90, 95% CI 0·83–0·97; p=0·004) and overall survival (HR 0·90, 95% CI 0·83–0·99; p=0·018). Physical dose was not associated with either endpoint. Among patients who did not receive radiotherapy, sham-GARD was not associated with outcomes, supporting GARD as a treatment-specific predictor rather than a prognostic biomarker. Conclusions In pediatric CNS tumors, the biological effect of radiotherapy quantified by GARD was associated with outcomes, whereas physical dose was not. These findings challenge uniform radiotherapy dosing and support genomically informed dose individualization. Prospective evaluation of GARD-guided radiotherapy is warranted to optimize tumor control while minimizing long-term toxicity in children.

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.

The sharp dose gradients that underpin the dosimetric advantage of particle therapy over photon therapy can be undermined by the interplay effects due to intra-fraction motion in modern pencil beam scanning systems. Fluoroscopy-Guided Particle Therapy (FGPT) offers a promising path to improved motion management through real-time tracking of tumors or surrogate signals. The advent of flat-panel detector (FPD)-based technology has enabled tighter integration of fluoroscopy/fluorography into treatment units and accelerated clinical adoption and research, with commercial systems such as Hitachi’s Real-time Gated Particle Therapy (RGPT) now available. However, the need for implanted fiducial markers, with the associated invasiveness and risk of complications, limits the utility of RGPT to a few anatomic sites in selected patients. The full potential of FGPT, therefore, depends on reliable marker-less tumor tracking, which remains challenging because soft-tissue targets are obscured by overlapping anatomy along the X-ray path, leading to reduced reliability of traditional image-registration algorithms in the projection domain. Recent advances in deep learning and AI-driven image registration have renewed hope for overcoming these barriers, enabling real-time marker-less tracking for particle therapy. This review outlines the evolution of fluoroscopy technology from image intensifier (II) to FPD-based systems, summarizes historical and recent vendor-supported FGPT strategies, and surveys emerging AI-based algorithms in the literature. A general review of machine learning-based image registration is provided, challenges in generalizability and interpretability are highlighted, and potential paths toward reliable, clinically deployable FGPT are discussed.

Introduction Proton dose driven continuous scanning (DDCS) is a form of proton pencil beam scanning (PBS) in which the beam may remain on during transitions between successive spots. Mayo Clinic Florida (MCF) is preparing to commission a synchrotron-based proton therapy system designed for DDCS, incorporating novel irradiation control features. Given the novelty and complexity of this system, a systematic risk-based analysis was performed to inform upcoming commissioning strategies. Methods A prospective risk analysis was conducted using the American Association of Physicists in Medicine (AAPM) Task Group 100 (TG 100) framework. A comprehensive process map of the DDCS irradiation control workflow was developed. Failure modes and effects analysis (FMEA) was performed to identify potential delivery system failure modes and to score each failure mode based on occurrence, severity, and lack of detectability. Targeted mitigation and commissioning strategies were then developed to address the highest-risk items. Results Twenty-nine delivery system failure modes were identified. The highest-risk failure modes were specific to DDCS operation. Targeted commissioning strategies were defined, including scan path measurement and modeling, validation of move and flap dose, dose monitor collection efficiency testing, spot position monitor performance evaluation, and verification of control system intervention behavior. Incorporation of these strategies into the commissioning plan reduced the mean RPN by more than 50%. Discussion This work provides a detailed description of the MCF proton DDCS irradiation control system and uses a TG 100 based approach for identifying system-specific risks. As new systems incorporate technologies for which limited guidance exists, institutions must perform their own risk analyses to determine appropriate quality management strategies.

Abstract Background Toxicities to organs at risk adjacent to the target volume represent a critical limitation to dose escalation in radiotherapy. While external beam therapy with accelerated ions allows for a reduced dose to surrounding healthy organs, its biological effectiveness can be markedly higher than that of conventional x‐rays and should be considered during treatment planning. Current biophysical models possess limitations in computing in vivo normal tissue complication probability (NTCP) curves for clinically relevant ion beams. Purpose This study introduces and validates a novel computational framework capable of predicting NTCP curves for serial organs following ion exposures. Methods The proposed framework integrates the linear‐quadratic model, the critical volume NTCP model, a novel two‐stage fitting process of fractionated photon NTCP data, histological data, and the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF MKM) of relative biological effectiveness. The model was applied to predict the NTCP for the spinal cord myelopathy of rats exposed at different depths along the spread‐out Bragg peaks (SOBPs) of protons, helium, carbon, and oxygen ions. Radiation transport simulations were conducted to assess the microdosimetric pattern of energy deposition at the subcellular scale. Results The two‐stage photon fitting process yielded a α photons /β photons ratio of 2.3 ± 0.6 Gy and NTCP model parameters for calculations considering either endothelial or glial cells as the primary targets for the spinal cord myelopathy. Without using any ion data for calibration, the predicted NTCP showed an overall good agreement with the corresponding animal data. The model successfully reproduced the experimentally observed trends in the NTCP dependence with respect to fractionation, ion type, and exposure position along the SOBPs. Conclusions The model's ability to predict the NTCP curves for ion exposures, based solely on photon in vivo data, provides mechanistic insights and can guide preclinical investigations into the biological effects of different ions and fractionation schemes.

Abstract Background Fluoroscopic‐gated proton therapy (FGPT) enables precise dose delivery to tumors affected by respiratory motion by tracking internal fiducial markers and delivering the proton beam when the marker is within a gating window. However, scatter radiation from fluoroscopic x‐rays may be detected by the dose monitor (DM) and mistakenly counted as proton monitor units (MU). To mitigate this issue, proton beam delivery is typically interrupted during the fluoroscopy pulse, a method known as interrupted continuous delivery (ICD). If the contamination from scattered fluoroscopic x‐rays is sufficiently low compared to the proton beam current, uninterrupted continuous delivery (UCD), in which fluoroscopic x‐rays are delivered concurrently with proton beams, may be feasible. UCD can enhance beam stability and improve treatment efficiency in particular for synchrotron‐based proton therapy systems using continuous beams. Purpose This study aimed to measure the contamination of scattered fluoroscopic x‐rays in DM and to evaluate the dose distribution when fluoroscopic x‐rays and proton beams are delivered simultaneously in FGPT. Methods x‐ray contamination was measured using a proton therapy system equipped with the FGPT system. Fluoroscopic x‐rays were delivered for 30 s under both in‐air and solid phantom conditions, with thicknesses of 10 and 30 cm, and DM counts were recorded. The dose rate was evaluated under various fluoroscopic conditions, both with and without the range shifter and mini ridge filter. To assess the dosimetric impact, six virtual treatment plans were created using anthropomorphic phantoms for cases involving the prostate, lung (both superficial and deep), liver (both superficial and deep), and pancreas. Scatter fluoroscopic x‐ray contamination was measured at the beam angles specified in the treatment plans under clinical setup conditions. Dose distributions were recalculated assuming simultaneous delivery with proton beam currents of 0.1, 1, 2, 4, 8, and 40 MU/s, and these were compared to the treatment plans. Results The scattered fluoroscopic x‐ray contamination in the DM increased with higher tube voltage, tube current, frame rate, and field size. The maximum dose rate was 0.247 MU/s with 125 kV tube voltage, 80 mA tube current, 30 s −1 frame rate, 19 × 19 cm 2 field size, and solid phantom of 10 cm. The dose rate of scattered fluoroscopic x‐rays peaked at a solid phantom thickness of approximately 5 cm. In an anthropomorphic phantom, scattered fluoroscopic x‐ray contamination varies based on anatomical site and beam angle. Dose evaluations indicated that if the proton beam current was ≥2 MU/s at the target and ≥1 MU/s at organs at risk, the differences in dose metrics were within 1% compared to the treatment plans. These beam currents are achievable with clinical systems. Conclusions Scattered fluoroscopic x‐rays were confirmed to contaminate the DM and be counted as proton MUs. However, under clinically realistic beam current conditions, their impact on dose distribution in UCD FGPT was negligible. These findings support the feasibility of implementing UCD FGPT.

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 Heavy ion radiotherapy offers distinct advantages over conventional treatments due to its superior dose distribution and enhanced biological effectiveness. However, the microdosimetric characteristics of high linear energy transfer (LET) ions, particularly near the Bragg peak, remain poorly characterized. Most experimental microdosimetry has been conducted at lower LETs, leaving a critical gap in high‐LET microdosimetric data. Purpose This study aims to extend microdosimetry to several different high‐LET ions (700–2500 keV/µm) using the novel ENCORE detector. Methods Microdosimetry was performed at the Florida State University John D. Fox Accelerator Laboratory using the ENCORE detector, a multi‐sampling ionization chamber filled with low‐pressure tissue‐equivalent gas capable of resolving energy deposition from approximately 200 keV to 3 MeV. 12 C, 16 O, and 28 Si beams were accelerated to 17–55 MeV and delivered to the segmented detector. Energy deposition patterns near the Bragg peak were recorded, and microdosimetric spectra were reconstructed. Monte Carlo was used to model the experimental setup. From each measured and simulated spectra, dose‐mean lineal energy () was calculated and compared. Results The 16 strips of the ENCORE corresponded to a tissue‐equivalent thickness of 17.6 µm, enabling resolution of energy deposition immediately proximal and distal to the Bragg peak. In six measurement configurations, the ion beam stopped within this range, allowing precise mapping of the profile around the peak; in the remaining configurations, the beam fully traversed the detector. Across all beams, difference between measured and simulated averaged 12%, based on one measurement per strip. Peak measured reached 920 ± 220, 1240 ± 206, and 2460 ± 620 keV/µm for 12 C, 16 O, and 28 Si beams, respectively. Conclusion This study demonstrated that ENCORE can resolve microdosimetric spectra with high spatial and LET resolution across a range of ion energies. Measured values reasonably agreed with Monte Carlo simulations, validating the potential of ENCORE for position‐resolved microdosimetry and model benchmarking in particle therapy.

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.