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Article Synopsis
- Machine learning, particularly deep learning with convolutional neural networks (CNNs), is being used to detect prostate cancer in tissue slides, but sample type differences affect model accuracy.
- Research tested whether CNNs trained on one type of sample (biopsy or radical prostatectomy) could effectively analyze the other type, revealing a significant drop in performance across sample types.
- Results indicated that models performed well on their own sample but poorly on the alternative type, highlighting the need to consider morphological differences in training to improve cancer detection accuracy in clinical settings.*
Prostate cancer in solid organ transplant recipients: Results from a multicenter series.
Fr J Urol
December 2024
Departments of Urology and Renal Transplantation, Nantes University Hospital Center, Nantes, France.
Introduction: Prostate cancer incidence in immunosuppressed transplant recipients increases as life expectancy improves in this population. However, the management of treatments and immunosuppressive (IS) regimens for solid organ transplant recipients diagnosed with prostate cancer remains poorly defined. Therefore, we conducted a multicentric study to investigate these parameters more thoroughly.
View Article and Find Full Text PDFPreoperative risk factors for venous thromboembolism in major urologic cancer surgeries.
World J Urol
December 2024
Department of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon.
Article Synopsis
- The study analyzes the risk factors for venous thromboembolism (VTE) in patients undergoing major urologic cancer surgeries, particularly highlighting cystectomy as having the highest risk.
- It utilized data from the ACS-NSQIP database to examine over 207,861 cases, finding that 1.2% of patients experienced VTE post-surgery, with specific peaks for pulmonary embolism and deep venous thrombosis on certain days post-operation.
- Key identified risk factors included blood transfusions, older age (≥ 80 years), high BMI (≥ 40 kg/m²), and certain medical histories like congestive heart failure, indicating a need for thorough pre-operative assessments for better thromboprophyl
Application of deep learning for semantic segmentation in robotic prostatectomy: Comparison of convolutional neural networks and visual transformers.
Investig Clin Urol
November 2024
Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Purpose: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery.
View Article and Find Full Text PDFIntrathecal Dexmedetomidine as an Adjuvant to Low Dose Hyperbaric 0.5% Bupivacaine on Haemodynamic Parameters in Patients Undergoing Transurethral Resection of Prostate.
J Coll Physicians Surg Pak
September 2024
Department of Anaesthesiology, SICU, and Pain Management, Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan.
Objective: To determine the role of dexmedetomidine in potentiating the local anaesthetic efficacy of a low dose of bupivacaine when used as an adjuvant.
Study Design: A prospective, double-blind, randomised study. Place and Duration of the Study: Department of Anaesthesia, Sindh Institute of Urology and Transplantation, Karachi, Pakistan, from July 2021 to February 2022.
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