Increasing evidence supports the use of magnetic resonance (MR)-targeted prostate biopsy. The optimal method for such biopsy remains undefined, however.

To prospectively compare targeted biopsy outcomes between MR imaging (MRI)-ultrasound fusion and visual targeting.

From June 2012 to March 2013, prospective targeted biopsy was performed in 125 consecutive men with suspicious regions identified on prebiopsy 3-T MRI consisting of T2-weighted, diffusion-weighted, and dynamic-contrast enhanced sequences.

Two MRI-ultrasound fusion targeted cores per target were performed by one operator using the ei-Nav|Artemis system. Targets were then blinded, and a second operator took two visually targeted cores and a 12-core biopsy.

Biopsy information yield was compared between targeting techniques and to 12-core biopsy. Results were analyzed using the McNemar test. Multivariate analysis was performed using binomial logistic regression.

Among 172 targets, fusion biopsy detected 55 (32.0%) cancers and 35 (20.3%) Gleason sum ≥7 cancers compared with 46 (26.7%) and 26 (15.1%), respectively, using visual targeting (p=0.1374, p=0.0523). Fusion biopsy provided informative nonbenign histology in 77 targets compared with 60 by visual (p=0.0104). Targeted biopsy detected 75.0% of all clinically significant cancers and 86.4% of Gleason sum ≥7 cancers detected on standard biopsy. On multivariate analysis, fusion performed best among smaller targets. The study is limited by lack of comparison with whole-gland specimens and sample size. Furthermore, cancer detection on visual targeting is likely higher than in community settings, where experience with this technique may be limited.

Fusion biopsy was more often histologically informative than visual targeting but did not increase cancer detection. A trend toward increased detection with fusion biopsy was observed across all study subsets, suggesting a need for a larger study size. Fusion targeting improved accuracy for smaller lesions. Its use may reduce the learning curve necessary for visual targeting and improve community adoption of MR-targeted biopsy.