Purpose: Several strategies of prostate biopsy (PBx) have been introduced to improve prostate cancer (PCa) detection rates. However, studies comparing cancer detection rates (CDRs) according to biopsy methods in real-world practice are scarce. This study aimed to investigate CDRs according to the biopsy methods for patients with prostate-specific antigen (PSA) <10.0 ng/mL.
Materials And Methods: From 2006 to 2015, patients who underwent PBx were initially selected. All patients were categorized according to the biopsy methods performed (magnetic resonance imaging targeted biopsy [MR-TBx], 12+2 hypoechoic lesion target biopsy, saturation biopsy [sPBx], extended biopsy, and 12-core PBx). The CDR of MR-TBx was compared to that of sPBx and other protocols. Volume per core (VPC) was defined as prostate volume divided by the number of biopsy cores. Patients previously diagnosed with PCa were excluded.
Results: Of the 1,598 patients (median PSA, 5.41 ng/mL), 401 (25.1%) were diagnosed with PCa. Among the biopsy methods, MR-TBx has the highest CDR and proportion of Gleason score ≥7 (3+4). Biopsy methods, VPC, age, prostate volume, and PSA were associated with PCa detection. In the sub-analysis for initial biopsy, MR-TBx had no significant difference with sPBx, but had higher CDR than the other biopsy protocols. For repeat biopsy, VPC, rather than the biopsy method, was associated with CDR.
Conclusions: This study reaffirmed the efficacy of MR-TBx on CDR in real-world practice. In cases with barriers to performing magnetic resonance imaging, VPC might be useful for adjusting the optimal number of biopsy cores in repeat biopsy.
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http://dx.doi.org/10.4111/icu.2020.61.1.28 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Urology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Frankfurt, Germany.
Purpose: Lutetium-177 Prostate-specific membrane antigen (Lu-PSMA) radioligand therapy is EMA-approved for metastatic castration resistant prostate cancer (mCRPC) after androgen receptor pathway inhibition (ARPI) and taxan-based chemotherapy. However, its effect in taxan-naïve patients is under current investigation.
Methods: We relied on the FRAMCAP database to elaborate Lu-PSMA therapy outcomes of progression-free (PFS) and overall (OS) in taxan-naïve mCRPC patients after previous ARPI treatment.
Int J Urol
January 2025
Department of Urology, Steve Biko Academic Hospital, University of Pretoria, Pretoria, South Africa.
Background: Studies comparing oncological outcomes between robot-assisted radical prostatectomy (RARP) and open radical prostatectomy (ORP) are often limited by bias because of their multi-institutional and multiple surgeon design. Studies from a single institution and single surgeon are uncommon.
Objective: To compare oncological outcomes between RARP and ORP at a single institution by a single surgeon.
Cancers (Basel)
January 2025
Department of Pharmacology, Faculty of Medicine, Wrocław Medical University, Mikulicza-Radeckiego 2, 50-345 Wrocław, Poland.
Background/objective: The COVID-19 pandemic significantly disrupted healthcare systems worldwide including cancer diagnostics. This study aimed to assess the impact of the pandemic on histopathological cancer diagnostics in Lower Silesia, Poland, specifically focusing on prostate, breast, and colorectal cancer cases from 2018 to 2022. The objective was to evaluate diagnostic volumes and trends before, during, and after the pandemic and to understand the effect of healthcare disruptions on cancer detection.
View Article and Find Full Text PDFBackground: The Prostatype score (P-score) is a prognostic biomarker that integrates a three-gene (IGFBP3, F3, and VGLL3) signature derived from prostate biopsy samples, with key clinical parameters, including prostate-specific antigen (PSA) levels, Gleason grade, and tumor stage at diagnosis. The test has demonstrated superior predictive accuracy for prostate cancer outcomes compared with traditional risk categorization systems such as D'Amico. Notably, it reclassifies a higher proportion of patients into the low-risk category, making them eligible for active surveillance.
View Article and Find Full Text PDFRadiol Imaging Cancer
January 2025
Department of Radiology, University Medical Center Groningen, Groningen, the Netherlands.
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).
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