Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up).

Enhancing Data Completeness in Early Detection Pathway of Prostate Cancer: Integration of a Dashboard-Driven Feedback Tool to Improve Quality of Care.

: Quality assurance in data collection is essential as data quality directly impacts the accuracy and reliability of outcomes. In the context of early detection of prostate cancer, improving data completeness is a key focus for enhancing patient care. This study aimed to evaluate the effectiveness of a data-driven feedback tool, visualized through a dashboard, in improving the completeness of data collection by healthcare professionals.

Surgical navigation for targeted retroperitoneal lymph-node removal: a randomised, controlled, phase 3 trial.

Background: Metastatic retroperitoneal lymph node dissection (LND) for nodal recurrence is applied for a variety of cancers, such as urological, gynaecological and rectal cancer. Precise localisation and resection of these lymph nodes (LNs) during surgery can be challenging, especially after previous radiotherapy or surgery. The objective of this study was to assess the added value of surgical navigation for targeted LND in the retroperitoneum.

Early Oncological Outcomes in Patients who Underwent Staging Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography Compared with Conventional Imaging Before Radical Prostatectomy and Extended Pelvic Lymph Node Dissection.

Background And Objective: Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) is increasingly used for primary staging in prostate cancer. Owing to accurate detection of small metastases on PSMA-PET/CT, patient selection for robot-assisted radical prostatectomy (RARP) has likely changed. This study analyzes oncological outcomes in patients undergoing RARP and extended pelvic lymph node dissection (ePLND) after PSMA-PET/CT staging, compared with those without PSMA-PET/CT.