Citations

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 Flap complications following maxillectomy, reconstruction, and adjuvant proton beam therapy (PBT) for primary maxillary and sinonasal malignancies are not well described. Methods This retrospective cohort study included consecutive patients treated between 2016 and 2023 from a single‐institutional database. Results Thirteen patients were identified with a median follow‐up of 26 months. No immediate post‐operative complications occurred, with a median time between surgery and PBT of 61 days. Nine patients (69.2%) had high‐risk postoperative pathology and required 66+ Gy(RBE), including five (38.5%) with gross disease receiving 70 Gy(RBE). Flap necrosis and fistulization occurred in three patients (23.1%) following PBT (median time from PBT to complication, 112 days). Higher flap mean and minimum dose, flap volume receiving at least 66 Gy[RBE], and minimum dose to 50% flap volume were associated with complications. Conclusions Careful patient selection and planning strategies should be considered to mitigate flap complication risk following adjuvant PBT for high‐risk post‐operative pathology.

Abstract We investigated the influence of 55 583 autophagy-related single-nucleotide polymorphisms (SNPs) on chronic lymphocytic leukemia (CLL) risk across 4 independent populations comprising 5472 CLL cases and 726 465 controls. We also examined their impact on overall survival (OS), time to first treatment (TTFT), autophagy flux, and immune responses. A meta-analysis of the 4 populations identified, to our knowledge, for the first time, significant associations between CDKN2A (rs3731204) and BCL2 (rs4940571, rs12457371, and rs1026825) SNPs and CLL risk, with CDKN2A showing the strongest association (P = 1.57 × 10−12). We also validated previously reported associations for FAS, BCL2, and BAK1 SNPs with CLL risk (P = 4.73 × 10−21 to 3.39 × 10−9). The CDKN2Ars3731204 and FASrs1926194 SNPs associated with increased CDKN2A and ACTA2 messenger RNA expression levels in the whole blood and/or lymphocytes (P = 5.1 × 10−7, P = 1.58 × 10−21, and P = 7.8 × 10−41), although no significant effect on autophagy flux was observed. However, associations were found between CDKN2A, BCL2, and FAS SNPs and various T-cell subsets, cytokine production, and circulating concentrations of interferon gamma, tumor necrosis factor–related apoptosis-inducing ligand, CD40, chemokine ligand 20, and interleukin-2 receptor subunit β proteins (P ≤ .005). No significant association was detected between autophagy variants and OS or TTFT, suggesting that these variants drive disease initiation rather than progression. In conclusion, this study identified 4 novel associations for CLL and provided insights into the biological pathways that influence CLL development.

BACKGROUND: Whereas antiphospholipid syndrome (APS) is best known for increasing the risk of macrovascular thrombosis, APS vasculopathy is characterized by the abnormal proliferation of endothelial and smooth muscle cells, leading to occlusion of small blood vessels in the skin, kidneys, and heart, among other organs. The underlying mechanisms remain unclear, and targeted treatment options for patients with APS are lacking. METHODS: To identify and analyze APS microvascular endothelial cells (MVECs), skin biopsies of patients with APS complicated by livedo racemosa were characterized using single-cell RNA sequencing. CCN1 (cellular communication network factor 1) and CCN2 were identified in skin and kidney biopsies by immunofluorescence microscopy and in plasma by ELISA. Healthy dermal MVECs were cultured with APS patient–derived serum or immunoglobulin G (IgG), and relevant signaling pathways were characterized using quantitative PCR, immunoblotting, and immunofluorescence microscopy. The proliferation and migration of vascular smooth muscle cells were determined after exposure to conditioned medium from APS IgG–stimulated MVECs. A mouse model of APS IgG–accelerated neointima formation was developed. Anti-CCN2 monoclonal antibodies were tested in vascular smooth muscle cell functional assays and the mouse model. RESULTS: Increased endothelial cell expression of CCN1 and CCN2 was identified by single-cell RNA sequencing and further confirmed in APS skin by microscopy as well as in APS plasma by ELISA. Exposure of healthy MVECs to patient IgG triggered the upregulation of CCN1 and CCN2 via Toll-like receptor 4– and YAP1 (yes-associated protein 1)–mediated signaling. CCN2, originating from APS IgG–stimulated MVECs led to the proliferation and migration of vascular smooth muscle cells, phenotypes that were inhibited by an anti-CCN2 antibody or depletion of EGFR (epidermal growth factor receptor). We further observed increased expression of CCN2, along with evidence of YAP1 nuclear translocation, in kidney vessels of APS nephropathy biopsies. Finally, CCN2 inhibition with an anti-CCN2 monoclonal antibody significantly reduced neointima thickening and cell proliferation in a mouse model of APS IgG–accelerated neointima formation. CONCLUSIONS: This study revealed activation of YAP1-mediated signaling in APS dermal microvessels and demonstrated that the YAP1 target CCN2 plays a role in facilitating pro-proliferative communication between MVECs and vascular smooth muscle cells. These findings offer insights into the cellular and molecular mechanisms underlying APS vasculopathy, providing potential therapeutic targets for patients.

ABSTRACT Background Prompt detection and intervention are crucial for improving outcomes in acute invasive fungal rhinosinusitis (AIFS). Diagnostic prediction models assist in risk‐stratification, but their accuracy requires testing through external validation. This study aims to validate a previously published diagnostic prediction model for AIFS in an independent cohort. Methods A retrospective chart review was conducted at a tertiary care center (2008–2023) to identify patients with an otolaryngology consult for suspected AIFS. Of 65 patients identified, 11 (16.9%) were diagnosed with AIFS based on histopathology. Risk was calculated using Yin et al.’s predictive model. Predictive performance was assessed by calibration and discrimination. Results Patients had significantly higher rates of diabetes (46.2% vs. 26.1%, p = 0.002), long‐term steroid use (60% vs. 28.2%, p < 0.0001), and solid organ transplantation (38.5% vs. 8.5%, p < 0.001), compared with the development cohort, with conversely lower rates of hematologic malignancy (29.2% vs. 58.7, p < 0.001) and neutropenia (19.4% vs. 41%, p = 0.001). Despite these differences, both the three‐variable (C‐index: 0.844; 95% CI, 0.736–0.952) and four‐variable models (C‐index: 0.963; 95% CI, 0.919–1) showed adequate discrimination. Both models exhibited slight overprediction of risk, with a calibration‐in‐the‐large predicted risk of 24.1% (95% CI, 13.68–34.46) for the three‐variable model and 24.2% (95% CI, 13.76–34.57) for the four‐variable model. Calibration plots confirmed overprediction. Conclusion The AIFS diagnostic model demonstrates acceptable discrimination and calibration on external validation, with generalizability to patients with different comorbidities. Larger studies are recommended to further test the model's predictive performance and clinical applicability.

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Abstract Background Dose‐driven continuous scanning (DDCS) is a type of beam delivery in Hitachi pencil beam scanning (PBS) particle therapy system. Particle beam is irradiated continuously between spots to achieve a higher effective dose rate and a shorter beam delivery time. The beam is only turned off at break spots, the end of a treatment layer and during spill change. The break spot is a unique feature of the DDCS introduced by the treatment planning system (TPS) to interrupt continuous scanning. DDCS separates the dose delivery of a spot into a move dose and stop dose components. The move dose is controlled by the scanning speed and beam current, while the stop dose delivers the remaining monitor units (MUs). Hence, rigorous quality assurance (QA) of the scanning speed and functionality of break spot are required to ensure safe and successful DDCS delivery. Purpose We aim to report for the first time the QA methodology and result for DDCS, focusing on the scanning speed and break spot validation. Methods A rectangular spiral irradiation pattern with different degree of break spots were irradiated on a 2D strip ionization chamber (CROSSmini) detector using 50 time resolution to validate the break spots. Four methods of measuring scanning speeds were proposed. Using a spiral irradiation pattern, methods one and two applied linear regression to the scanning time measured by the CROSSmini for different inter‐spot distances, with measurements taken at sampling rates of 5 and 20 kHz, respectively. Methods three and four measured the scanning time of single inter‐spot distances in the X and Y directions. Method three measured the scanning time with the CROSSmini while method four measured the rise and fall time of the scanning magnet (SCM) current. All measurements were performed with 70.2, 150.2 and 228.7 MeV proton beam energies and three different beam currents of 8, 14 and 20 MU/s. Lastly, the scanning speed tolerance limit was calculated analytically under the requirement of avoiding a beam abort when the stop dose was less than zero. Results The data recorded by the CROSSmini under the spiral irradiation patterns with break spots was reconstructed into a 2D dose, and the break spots’ operations were visually validated for all the energies and beam currents. There were no statistically significant difference in the measured X and Y scanning speeds across the four methods. However, the last method involving the SCM current yielded the largest type A uncertainty of up to 25% () due to the noisy signal. The first method with the linear regression and 5 kHz acquisition rate was preferred due to a smaller type A uncertainty of up to 2.0% and a lower false negative rate in detecting the rising and falling edges during scanning. The calculated tolerance limit based on our current DDCS setting was found to be 0.50 times of the expected scanning speed. Conclusion We have introduced QA protocols for DDCS, designed to ensure complete and safe beam delivery without interruptions caused by move doses exceeding planned spot doses.