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http://dx.doi.org/10.1016/j.hrcr.2025.02.012 | DOI Listing |
World J Urol
September 2025
Department of Urology and Transplantation Surgery, Nantes University Hospital, Nantes, France.
Purpose: In 5-10% of cases, renal cancer extends into the venous system, particularly the inferior vena cava (IVC), which worsens prognosis. This study aims to assess morbidity, mortality, and oncological outcomes of patients treated surgically for renal cancer with IVC extension over a 30-year period, in two experienced centers.
Materials And Methods: This bicentric, retrospective study analyzed patients treated between 1988 and 2020 for renal cancer involving the IVC.
JTCVS Open
August 2025
Department of Cardiothoracic Surgery, Palestine Medical Complex, Ramallah, West Bank, Palestine.
World J Surg
September 2025
Department of Hepatobiliary-Pancreatic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
Background: Achieving R0 resection in hepatopancreatobiliary (HBP) surgery frequently necessitates venous resection and reconstruction. Autologous grafts offer a promising solution, particularly in complex resections where infection risk or graft availability limit the use of synthetic or donor grafts. However, clinical data on the outcomes of autologous venous grafts remain limited.
View Article and Find Full Text PDFBMJ Open
September 2025
Arrhythmia Center, Chinese Academy of Medical Sciences Fuwai Hospital, Beijing, China.
Objectives: To evaluate the efficacy and safety of adding Superior Vena Cava Isolation (SVCI) to Pulmonary Vein Isolation (PVI) in patients with drug-refractory paroxysmal atrial fibrillation (PAF).
Design: Systematic review and meta-analysis of randomised controlled trials (RCTs) using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, supplemented with Trial Sequential Analysis (TSA) to assess evidence sufficiency.
Data Sources: We searched PubMed, EMBASE, the Cochrane Library (CENTRAL) and Web of Science for relevant studies published up to 13 July 2025.
J Thorac Imaging
September 2025
Department of Radiology, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University.
Purpose: To establish an explainable machine learning (ML) approach using patient-related and noncontrast chest CT-derived features to predict the contrast material arrival time (TARR) in CT pulmonary angiography (CTPA).
Materials And Methods: This retrospective study included consecutive patients referred for CTPA between September 2023 to October 2024. Sixteen clinical and 17 chest CT-derived parameters were used as inputs for the ML approach, which employed recursive feature elimination for feature selection and XGBoost with SHapley Additive exPlanations (SHAP) for explainable modeling.