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Purpose: To evaluate the additional value of systematic biopsies (SB) when performing transperineal MRI/TRUS fusion biopsies (MRI/TRUS TPBx) with needle tracking.
Methods: From January 2019 to March 2021 969 Patients after a MRI/TRUS TPBx were evaluated separately for target biopsies (TB) and systematic biopsies regarding PCa detection and PCa risk evaluation. Needle tracking in the axial sequences of multiparametric MRI was used to assess the localisation of the detected PCa in the biopsy cores related to the reported PI-RADS lesions.
Results: The overall cancer detection rate (CDR) for PCa and clinically significant (cs) PCa (ISUP ≥2) with the combination of TB and SB were 66 and 49%. TB detected 46% csPCa and SB 22% csPCa. SB identified 1.5% additional csPCa outside of the reported PI-RADS lesions. 16 patients (1.7%) showed a relevant upgrading from clinically insignificant PCa in TB to csPCa. In 736 patients with unilateral suspicious lesions on MRI, 145 patients (20%) were detected with contralateral PCa-positive SB. 238 patients (25%) showed PCa positive systematic biopsy cores outside of the described PI-RADS lesions.
Conclusions: Needle tracking optimizes the 3D-localisation of cancer in the prostate. Our results show that the added value of SB with a reduced systematic biopsy scheme is low with regard to prostate cancer (PCa) detection and PCa risk evaluation. However, there is a relevant added value for localizing multifocal PCa in the primary diagnostic by a MRI/TRUS fusion biopsy of the prostate.
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http://dx.doi.org/10.1007/s11255-022-03309-y | DOI Listing |
Can Assoc Radiol J
September 2025
University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network-Sinai Health System-Women's College Hospital, University of Toronto, ON, Canada.
Maedica (Bucur)
June 2025
Department of Urology, "Prof. Dr. Th. Burghele" Clinical Hospital, Bucharest, Romania.
Objectives: This study aimed to evaluate the clinical utility of the SelectMDx urinary biomarker test in men with PI-RADS 3 lesions identified through multiparametric magnetic resonance imaging (mpMRI), a subgroup in which prostate cancer diagnosis remains uncertain. The primary objective was to assess whether SelectMDx can improve risk stratification for clinically significant prostate cancer and thereby reduce unnecessary prostate biopsies.
Materials And Methods: A prospective cohort of 40 patients with serum prostate-specific antigen (PSA) levels ≥3 ng/mL and PI-RADS ≥ 3 lesions on mpMRI was analyzed.
Can Assoc Radiol J
August 2025
Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.
Introduction: This study aimed to determine the positive predictive value (PPV) of magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) machine fusion prostate biopsies, and to identify factors associated with a positive biopsy.
Methods: With ethics approval, we retrospectively evaluated all MRI-TRUS machine fusion prostate biopsies at our institution from September 2022 to April 2025. True positive clinically significant prostate cancers (csPCa) were defined as Gleason ≥7.
Zhonghua Nan Ke Xue
January 2025
Department of Radiology, Huai'an First Hospital Affiliated to Nanjing Medical University, Huai'an, Jiangsu 223001, China.
Objective: To assess the value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score combined with PSA density (PSAD) in the diagnosis of clinically significant prostate cancer (CSPCa) in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy.
View Article and Find Full Text PDFJ Imaging Inform Med
July 2025
L3IA Laboratory, Department of Informatics, University of Sidi Mohamed Ben Abdellah, Faculty of Sciences Dhar El Mahraz, Fez, Morocco.
Multimodal image registration is crucial in medical imaging, particularly for aligning Magnetic Resonance Imaging (MRI) and Transrectal Ultrasound (TRUS) data, which are widely used in prostate cancer diagnosis and treatment planning. However, this task presents significant challenges due to the inherent differences between these imaging modalities, including variations in resolution, contrast, and noise. Recently, conventional Convolutional Neural Network (CNN)-based registration methods, while effective at extracting local features, often struggle to capture global contextual information and fail to adapt to complex deformations in multimodal data.
View Article and Find Full Text PDF