Qing Zhou

Objective: To develop a patient-specific static and dynamic 3D-printing workflow for preoperative planning of endovascular repair in complex aortic disease with physician-modified stent grafts, to assess the respective roles of rigid anatomical and dynamic models, and to explore its association with intraoperative efficiency compared with conventional image-based planning. Methods: This retrospective study included 46 consecutive patients with complex aortic disease encompassing various segments between the aortic arch and the abdominal aorta, who underwent endovascular repair with physician-modified stent grafts. Patients were assigned to a 3D printing-guided group ( n = 22) or a conventional image-guided group ( n = 24) based on the preoperative planning strategy. In the 3D printing group, patient-specific 1:1 rigid anatomical models and compliant dynamic models were generated from computed tomography angiography data. Rigid models were used for anatomical visualization, device sizing, and fenestration planning. Dynamic models, fabricated using silicone material (Shore hardness 10A) and integrated into a pulsatile flow system, were used to simulate device passage, deployment, and device–vessel interaction under physiological flow conditions. Model accuracy was evaluated by surface deviation analysis, and dynamic model performance was evaluated by Doppler-based flow comparison with in vivo measurements. The two groups were compared regarding operative time, radiation dose, contrast volume, perioperative complications, and short-term outcomes. Results: Rigid anatomical models demonstrated high geometric fidelity, with over 97% of surface deviations within 0.2 mm compared with CTA data. Dynamic models showed close agreement with in vivo hemodynamics, with comparable peak systolic velocities at the inlet (1.48 ± 0.20 m/s vs. 1.52 ± 0.18 m/s, p = 0.274), aneurysmal segment (1.20 ± 0.18 m/s vs. 1.25 ± 0.15 m/s, p = 0.312), and outlet (1.35 ± 0.17 m/s vs. 1.38 ± 0.16 m/s, p = 0.401). In the 3D printing-guided group, simulation findings were concordant with intraoperative findings in 21 of 22 cases. Compared with rigid static models, dynamic models provided additional planning information in anatomically challenging cases, including severe angulation of arch or neck, severe luminal stenosis or true lumen collapse, target vessels arise from the aneurysm sac, complex target vessel proximal segment and severe access tortuosity. In the exploratory clinical comparison, the 3D-printing-guided group had significantly shorter operative time (133.6 ± 43.3 min vs. 171.4 ± 72.1 min, p = 0.039) and lower contrast volume (181.7 ± 68.6 mL vs. 234.9 ± 101.0 mL, p = 0.044) than the conventional image-guided group. The need for intraoperative device adjustment did not differ significantly between groups. No major adverse events occurred within 30 days in either group. Conclusion: A patient-specific dual-model 3D printing workflow combining rigid anatomical models and dynamic models is feasible for planning endovascular repair of complex aortic disease with physician-modified stent grafts. The dynamic model provides complementary information beyond static anatomical assessment, particularly in anatomically complex cases requiring simulation of device–vessel interaction. The observed reductions in operative time and contrast use suggest potential intraoperative benefits.

Objectives: To develop and validate a nomogram integrating ultrasound microvascular imaging (super‐resolution ultrasound [SRUS], superb microvascular imaging [SMI]), resistive index (RI), and clinical features for the non‐invasive assessment of renal fibrosis severity in chronic kidney disease (CKD). Materials and Methods: A total of 208 CKD patients (141 in the training cohort and 67 in the external validation cohort) were included in the study. All patients underwent renal ultrasound and biopsy. Based on the pathological findings, patients were classified into mild and moderate‐to‐severe fibrosis groups. Candidate predictors were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by correlation and multicollinearity analysis. A multivariable logistic regression model was used to construct a nomogram. Model performance was evaluated by receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: The nomogram, incorporating SRUS, SMI, RI, hypertension, blood urea nitrogen (BUN), and estimated glomerular filtration rate (eGFR), showed good predictive performance. The area under the curve (AUC) for the nomogram was 0.944 (95% confidence interval [CI]: 0.908–0.981) in the training cohort and 0.907 (95% CI: 0.835–0.980) in the external validation cohort. Calibration analyses showed acceptable agreement between predicted and observed probabilities, with calibration intercepts/slopes of 0.003/1.002 in the training cohort and −0.296/0.767 in the external validation cohort. Conclusion: This nomogram may provide a non‐invasive approach for assessing renal fibrosis severity and supporting risk stratification in patients with CKD.

1. Glioblastoma (GBM) is the most aggressive and common primary brain tumor in adults. It presents a formidable challenge in oncology because of its infiltrative nature, rapid progression, and resi...

Background/Objectives: Rectovaginal fistula (RVF) is a challenging condition that can substantially impair quality of life and often requires individualized surgical management. Accurate preoperative imaging is essential for identifying fistulous tracts, assessing pelvic floor involvement, and guiding surgical planning. However, conventional imaging modalities, including magnetic resonance imaging (MRI), may still have limitations in detecting small, occult, intermittently patent, or anatomically complex fistulas, particularly under static imaging conditions. This study aimed to evaluate the diagnostic performance of transperineal pelvic floor ultrasound (TPUS) combined with ultrasound contrast agent enema (UCAE) and its potential value in preoperative assessment of RVF. Methods: In this prospective single-center study, 62 women with surgically confirmed RVF were enrolled between January 2022 and April 2025. All patients underwent TPUS combined with UCAE before surgery, while subsets also underwent contrast-enhanced CT, contrast-enhanced MRI, or barium enema according to clinical indications. Imaging findings were compared with intraoperative findings as the reference standard. Fistula detection, morphological classification, size, anatomical location, pelvic floor injury assessment, and concordance between ultrasound-based surgical proposals and actual surgical procedures were systematically analyzed. Comparative analyses with conventional imaging modalities were performed based on available non-paired subgroups. Results: UCAE detected RVF in 60 of 62 patients, yielding a detection rate of 96.8%. In the available comparative cohort, UCAE demonstrated higher detection rates than conventional imaging modalities, particularly in small and complex fistulas. UCAE showed high agreement with intraoperative findings in terms of fistula morphology, size, and anatomical location. TPUS provided additional and complementary information regarding levator ani and anal sphincter injuries and yielded higher sensitivity and accuracy in pelvic floor injury assessment within the studied cohort. Ultrasound-based surgical proposals showed directional concordance with final intraoperative decisions in 88.7% of cases. No adverse events were observed during UCAE. Conclusions: TPUS combined with UCAE appears to be a safe and feasible preoperative imaging approach for RVF. By integrating fistula detection, anatomical classification, pelvic floor assessment, and surgical planning, this combined ultrasound approach may serve as a complementary imaging strategy to conventional imaging modalities, particularly for small or intermittently patent fistulas. Further multicenter studies and standardized imaging protocols are warranted to validate its clinical utility and generalizability.

Background: Intradialytic hypotension (IDH) is a frequent and severe complication in hemodialysis patients. Traditional prediction models rely on single-modal data or simple machine learning methods, while Transformer-based deep learning approaches suffer from high computational complexity and poor efficiency in processing long sequential data. Therefore, this study aimed to develop a stable, efficient, and generalizable deep learning model for the early prediction of IDH. Methods: We proposed MI-Net, a dual stream feature network fusing Mamba and convolutional neural network (CNN) for IDH prediction using multimodal data (echocardiography videos and clinical parameters). The CNN branch extracted local spatial features, and the Mamba branch captured long range temporal dependencies. A cross attention fusion module was used to integrate imaging and clinical features. The model was validated with internal data from the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture (CHETM) and external data from Renmin Hospital of Wuhan University (RHWU). Results: On the CHETM test set, MI Net achieved an accuracy of 0.924 and an area under the curve (AUC) of 0.952. On the external RHWU test set, it obtained an accuracy of 0.894 and an AUC of 0.851. Ablation studies confirmed that Mamba, cross attention, and dual stream structure significantly improved performance. Conclusions: MI Net efficiently combines Mamba and CNN, showing high accuracy, strong specificity, and good generalizability. It provides a reliable intelligent tool for early prediction of IDH.

Objective: To evaluate the clinical value of patient-specific 3D-printed anatomical and dynamic functional models for preoperative planning and intraoperative simulation in complex aortic aneurysm repair. Methods: A total of 46 patients with complex aortic aneurysms were retrospectively included and allocated to either a 3D printing–guided group (n = 22) or a conventional image-guided group (n = 24) based on the preoperative planning strategy. Patients in the conventional group underwent standard imaging evaluation before endovascular repair. In the 3D printing group, patient-specific 1:1 rigid anatomical and dynamic functional models were generated from computed tomography angiography (CTA) data. Rigid anatomical models were used for stent sizing and fenestration planning. Dynamic models, fabricated using compliant silicone materials and integrated into a pulsatile flow system, were employed to replicate key physiological hemodynamic conditions and device–vessel interactions. Model accuracy was validated using surface deviation analysis and Doppler-based flow comparison with in vivo measurements. Operative time, fluoroscopy time, radiation dose, contrast volume, perioperative complications, and short-term outcomes were compared between groups. Results: Rigid anatomical models demonstrated high geometric fidelity, with over 97% of surface deviations within 0.2 mm compared with CTA data. Dynamic models showed close agreement with in vivo hemodynamics, with comparable peak systolic velocities at the inlet (1.48±0.20 m/s vs. 1.52±0.18 m/s, p = 0.274), aneurysmal segment (1.20±0.18 m/s vs. 1.25±0.15 m/s, p = 0.312), and outlet (1.35±0.17 m/s vs. 1.38±0.16 m/s, p = 0.401). Compared with the conventional group, the 3D printing group had significantly reduced operative time (133.6±43.3 min vs. 171.4±72.1 min, p = 0.040) and contrast volume (181.7±68.6 mL vs. 234.9±101.0 mL, p = 0.040). Stent and fenestration planning accuracy reached 95.5%. No major adverse events occurred within 30 days in either group. Conclusion: A patient-specific dual-model 3D printing strategy combining rigid anatomical and dynamic functional models enhances preoperative planning accuracy and intraoperative efficiency in complex aortic aneurysm repair, with potential benefits for procedural safety and short-term clinical outcomes.

Purpose: To assess the added diagnostic value of contrast‐enhanced ultrasound (CEUS) in differentiating small breast lesions initially categorized as ACR BI‐RADS 4A. Materials and Methods: A total of 1595 patients with small breast lesions (≤ 20 mm) from 37 tertiary hospitals were enrolled from August 2021 to August 2022. B‐mode ultrasound, color Doppler, and CEUS were performed to evaluate the lesions. The integration of CEUS led to the reclassification of BI‐RADS 4A lesions into three pathways: upgrade to 4B, downgrade to 3, or no change in category. The diagnostic performance of CEUS was evaluated, and the contrast modes which could be used to correctly downgrade or upgrade BI‐RADS 4A lesions were explored. Results: A total of 1340 lesions were finally included in the analysis of CEUS performance. The average age of all included participants was 43 ± 8 (range from 20 to 84). The average diameter of the lesions was 12.4 ± 3.8 mm (range from 5 mm to 20 mm). The diagnostic performances of CEUS + BI‐RADS were 98.3% for sensitivity, 81.7% for specificity, 34.1% for positive predictive value, 99.8% for negative predictive value, and 83.1% for overall accuracy, respectively. The sensitivity was slightly downgraded (98.3% vs. 100%, p = 0.1555), while the specificity and accuracy were greatly elevated ( p < 0.05). The receiver operating characteristic curve (ROC) value was calculated using a binary classification threshold, with BI‐RADS 3 as low‐risk and BI‐RADS 4A as high‐risk. The area under ROC was 0.896 (95% CI: 0.878, 0.911) for BI‐RADS + CEUS. The decision curve analysis showed that using CEUS + BI‐RADS to guide biopsy decisions provided net benefit compared to the default strategy of biopsying all lesions. Conclusion: The addition of CEUS allows for the correct downgrading of most benign BI‐RADS 4A lesions, thereby avoiding unnecessary biopsies. An upgrade from BI‐RADS 4A to 4B on CEUS warrants immediate biopsy to ensure prompt diagnosis and treatment. Trial Registration: Chinese Clinical Trial Registry: ChiCTR2100050719

Purpose: This study sought to investigate the predictive factors for atrial fibrillation late recurrence (AFLR) and major adverse cardiovascular events (MACEs) in atrial fibrillation (AF) patients after radiofrequency catheter ablation (RFCA) and construct a nomogram prediction model for providing precious information of screening the high risk patients and giving appropriate preventive interventions. Patients and Methods: A total of 128 patients with atrial fibrillation (AF) who underwent RFCA were enrolled. Univariate and multivariate Cox regression were used to screen the predictors of AFLR and MACEs (including rehospitalization due to AF recurrence, heart failure, myocardial infarction, coronary revascularization, stroke and all-cause mortality). The nomogram model was constructed to predict AFLR after RFCA. Risk stratification based on the nomogram further predicted AFLR and MACEs after RFCA. Subgroup analysis and survival analysis of high and low risk groups in individuals with AF after RFCA were performed. Results: During median follow-up of 76.50 (5.75) months, 71 (55.47%) patients experienced AFLR, while 56 (43.75%) suffered from MACEs. Early recurrence, maximum left atrial volume index (LAVImax) and E/Vp were the independent risk factors for predicting AFLR after RFCA. And AFLR was the only independent predictor for MACEs. Accordingly, a nomogram prediction model based on early recurrence, LAVImax and E/Vp was constructed, the 1-year, 3-year and 5-year AUC of AFLR were 0.904, 0.826 and 0.793, respectively. Risk stratification based on the nomogram had high predictive value for AFLR and MACEs. The Kaplan–Meier survival curves showed great discrimination between the low and high risk groups in the probability of free from AFLR and MACEs. Conclusion: The nomogram model based on early recurrence, LAVImax and E/Vp can be used to predict the AFLR and MACEs after RFCA accurately and individually, in order to provide scientific and effective patient management basis for AF patients after RFCA.