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OBJECTIVE Degenerative lumbar spondylolisthesis is a common cause of back and leg pain influenced by factors like instability, disc degeneration, facet arthropathy, and psychosocial phenotype. Patients’ experience of high-severity pain is complex, with significant implications for surgical planning. The goal of this study was to assess the impact of back pain catastrophizing on long-term outcomes following lumbar spondylolisthesis surgery. METHODS This study uses the Quality Outcomes Database (QOD) lumbar spondylolisthesis dataset and includes prospectively collected data from the 12 highest-enrolling clinical sites participating in the QOD lumbar module. The authors reviewed patients undergoing single-segment surgery for grade 1 degenerative lumbar spondylolisthesis. Severe baseline numeric rating scale for back pain (NRS-BP) scores (≥ 8) were classified as "catastrophizing," and mild to moderate pain scores (< 8) were classified as "non-catastrophizing." Patient-reported outcomes (PROs) were compared for catastrophizing versus non-catastrophizing patients at 60 months. Univariate and multivariate analyses were conducted to assess the impact of catastrophizing on PROs, with multivariate analysis controlling for variables initially reaching a p value < 0.10. RESULTS Of the 608 patients in this analysis, 260 (42.8%) experienced catastrophizing while 348 (57.2%) did not. Catastrophizing patients were significantly younger (59.9 ± 12.2 years vs 63.9 ± 11.7 years, p < 0.001), less often had ≥ 4 years of college education (31.2% vs 42.5%, p = 0.004), and more often used private insurance (58.8% vs 47.7%, p = 0.01). The catastrophizing cohort also had higher rates of depression (24.6% vs 17.0%, p = 0.02) and back pain predominance (45.4% vs 32.2%, p < 0.001). Surgical and perioperative characteristics did not significantly differ between cohorts. Although catastrophizing patients typically reported worse PRO scores at both baseline and 60 months, they demonstrated significantly greater improvement and minimal clinically important difference (MCID) achievement across almost all PROs at 60 months (p < 0.05). However, North American Spine Society (NASS) satisfaction scores at 60 months did not differ significantly between cohorts. Multivariate analysis found that catastrophizing significantly predicted 60-month NRS-BP change (β −1.45, 95% CI −1.81 to −1.09; p < 0.001) and MCID achievement (OR 1.98, 95% CI 1.52–2.58; p < 0.001) but not mean NRS-BP score or other metrics, including NASS satisfaction. CONCLUSIONS Despite presenting with worse baseline symptoms, patients with pain catastrophizing experienced substantial and clinically meaningful improvement following surgery for degenerative lumbar spondylolisthesis. Patient satisfaction was comparable between cohorts, reflecting a nuanced balance between greater absolute improvement and persistence of residual symptoms. These findings underscore that catastrophizing should not be viewed as a barrier to surgical success, while pointing to the need for proactive expectation setting and shared decision-making.

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.