Citations

Background: Ibrutinib, a potent Bruton’s tyrosine kinase inhibitor with marked efficacy against hematological malignancies, is associated with the heightened risk of atrial fibrillation (AF). Although ibrutinib-induced AF is linked to enhanced oxidative stress, the underlying mechanisms remain unclear. Objective: This research aimed to explore the molecular mechanism and regulatory target in ibrutinib-induced AF. Methods: We performed in vivo electrophysiology studies using ibrutinib-treated mice, and then employed proteomic and single-cell transcriptomic analyses to identify the underlying targets and mechanisms. The effects of A-kinase anchoring protein 1 (AKAP1) depletion on mitochondrial quality surveillance (MQS) were evaluated using both in vivo and ex vivo AKAP1 overexpression models. Results: Atrial AKAP1 expression was significantly reduced in ibrutinib-treated mice, leading to inducible AF, atrial fibrosis, and mitochondrial fragmentation. These pathological changes were effectively mitigated in an overexpression model of ibrutinib-treated mice injected with an adeno-associated virus carrying Akap1. In ibrutinib-treated atrial myocytes, AKAP1 down-regulation promoted dynamin-related protein 1 (DRP1) translocation into mitochondria by facilitating DRP1 dephosphorylation at Ser637, thereby mediating excessive mitochondrial fission. Impaired MQS was also suggested by defective mitochondrial respiration, mitochondrial metabolic reprogramming, and suppressed mitochondrial biogenesis, accompanied by excessive oxidative stress and inflammatory activation. The ibrutinib-mediated MQS disturbance can be markedly improved with the inducible expression of the AKAP1 lentiviral system. Conclusions: Our findings emphasize the key role of AKAP1-mediated MQS disruption in ibrutinib-induced AF, which explains the previously observed reactive oxygen species overproduction. Hence, AKAP1 activation can be employed to prevent and treat ibrutinib-induced AF.

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.

AbstractPurposeThe time structures of proton spot delivery in proton pencil beam scanning (PBS) radiation therapy are essential in many clinical applications. This study aims to characterize the time structures of proton PBS delivered by both synchrotron and synchrocyclotron accelerators using a non‐invasive technique based on scattered particle tracking.MethodsA pixelated semiconductor detector, AdvaPIX‐Timepix3, with a temporal resolution of 1.56 ns, was employed to measure time of arrival of secondary particles generated by a proton beam. The detector was placed laterally to the high‐flux area of the beam in order to allow for single particle detection and not interfere with the treatment. The detector recorded counts of radiation events, their deposited energy and the timestamp associated with the single events. Individual recorded events and their temporal characteristics were used to analyze beam time structures, including energy layer switch time, magnet switch time, spot switch time, and the scanning speeds in the x and y directions. All the measurements were repeated 30 times on three dates, reducing statistical uncertainty.ResultsThe uncertainty of the measured energy layer switch times, magnet switch time, and the spot switch time were all within 1% of average values. The scanning speeds uncertainties were within 1.5% and are more precise than previously reported results. The measurements also revealed continuous sub‐milliseconds proton spills at a low dose rate for the synchrotron accelerator and radiofrequency pulses at 7 µs and 1 ms repetition time for the synchrocyclotron accelerator.ConclusionThe AdvaPIX‐Timepix3 detector can be used to directly measure and monitor time structures on microseconds scale of the PBS proton beam delivery. This method yielded results with high precision and is completely independent of the machine log files.

AbstractINTRODUCTIONThe apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimer's disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid‐beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex‐dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research.METHODSIn 2023, the Alzheimer's Association convened multidisciplinary researchers at the “AAIC Advancements: APOE” conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE‐targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function.RESULTSThis manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field.DISCUSSIONUnderstanding apoE's multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine.Highlights APOE is a central player in the pathogenesis of Alzheimer's disease. APOE exerts a numerous effects throughout the brain on amyloid‐beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.

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 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.

AbstractBackgroundKilo‐voltage cone‐beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely‐used Feldkamp‐Davis‐Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary‐based methods, and prior information‐based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART.PurposeOne of the primary challenges in IR‐based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high‐resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels.MethodsThe HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp‐Davis‐Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process.ResultsWe evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms.ConclusionThe proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high‐definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.

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.