Related Experiment Video
Updated: May 1, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Identifying Optimal Prostate Biopsy Strategy for the Detection Rate of Clinically Significant Prostate Cancer: A
Wafa D Aloufi1,2, Abdulrahman Al Mopti1,3, Anas Al-Tawil4
1Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.
Background:
The growing role of multiparametric MRI (mpMRI) and MRI-targeted biopsy (MRI-TBx) suggests they may replace random systematic biopsy (SBx), specifically detection and subsequent treatment of clinically significant prostate cancer (csPCa).
Objectives:
To perform a systematic review and meta-analysis of randomised controlled trials (RCTs) comparing the detection rates (DR) of csPCa using MRI-TBx alone, SBx alone, or their combination in biopsy naïve patients suspected of having prostate cancer (PCa).
Methods:
PubMed, MEDLINE, Embase, and the Cochrane Library were searched up to 23 March 2023, for RCTs comparing PCa DR between biopsy strategies in patients with suspected prostate cancer. Detection rates were pooled using random/fixed effect models, and the study quality was assessed using the Cochrane risk of bias revised tool.
Results:
Ten RCTs (involving 3646 patients) were analysed, revealing that the combined biopsy method achieved higher overall csPCa DR compared to the SBx method alone (RR = 1.40 [95% CI = 1.15-1.71] and 1.47 [95% CI = 1.13-1.92], respectively). However, there was no significant difference in DR for clinically insignificant prostate cancer (ciPCa) between the two methods.
Conclusions:
This review concludes that MRI-TBx and SBx detect overall and clinically significant prostate cancer (csPCa) better than SBx alone. The variety of factors requires cautious interpretation, yet these findings are the strongest evidence. The combination technique is recommended for biopsy-naïve groups, but more study is needed to optimise execution and overcome uncertainties.
