Early Cost-Effectiveness Analysis of Using Whole Genome Sequencing for Patients with Castration-Resistant Prostate Cancer.

Objective: This study aims to assess the potential cost-effectiveness of using Whole-genome sequencing (WGS) guided systemic therapy in metastatic castrate-resistant prostate cancer (mCRPC) compared with the European Association of Urology guideline recommended diagnostics from a Dutch societal perspective.

Methods: A decision analytic model combining a decision tree and partitioned survival models was developed to link diagnostic results with subsequent biomarker-guided treatments. Two diagnostic strategies, WGS and guideline-recommended practice - the genomic testing for BRCA1/2 and dMMR, were simulated to compare the health outcome and cost.

Prognostic Value of Cribriform and Intraductal Carcinoma in Grade Group 2 Prostate Cancer With and Without Synchronous Nodal Metastases at Radical Prostatectomy: Results From a Case-control Matched, Multicenter Study.

Objective: To evaluate the occurrence and the oncological predictive value of cribriform growth and/or intraductal carcinoma (CR/IDC) in patients with ISUP grade group (GG) 2 prostate cancer (PCa) at radical prostatectomy (RP) with and without synchronous nodal metastases in a multicenter, international cohort.

Methods: We identified 1060 patients who underwent RP with ISUP GG2 PCa at histopathology and a pelvic lymph node dissection from 3 tertiary referral centers. Of these, 79 (7.

Intravesical Instillation of Chemotherapy Before Radical Surgery for Upper Urinary Tract Urothelial Carcinoma: The REBACARE Trial.

Background And Objective: Intravesical instillation of chemotherapy (IIC) after radical surgery for upper urinary tract urothelial carcinoma (UTUC) reduces the risk of intravesical recurrence (IVR). However, compliance is low because of possible extravesical leakage after bladder cuff excision. The aim of this study was to evaluate the efficacy of preoperative IIC in reducing the risk of IVR.

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.