PURPOSE: Many software for US/MRI guided fusion prostate biopsy (FPB), have been developed in the last years. However, there are few data comparing diagnostic accuracy of different fusion systems. We assessed diagnostic performance of elastic (EF) versus rigid fusion (RF) PB in a propensity score matched (PSM) analysis. METHODS: A total of 314 FPB were prospectively collected from two different centers. All patients were biopsy naïve and all mpMRI reported a single suspicious area. Overall, 211 PB were performed using a RF system and 103 using an EF software. The two groups were compared for the main clinical features. A 1:1 PSM analysis was employed to reduce covariate imbalance to < 10%. Detection rate (DR) for any prostate cancer (PCa) and clinically significant (cs) PCa were compared and stratified for PI-RADS Score. A per target univariable and multivariable regression analyses were applied to identity predictors of anyPCa and csPCa. RESULTS: After applying the PSM, two cohorts of 83 cases were selected. DR of any PCa cancer and csPCa were comparable between the two cohorts (all p > 0.077) as well as DR of csPCa for every PIRADS score. At univariable regression analysis lesion size, PI-RADS Score, PSA Density and EF system were predictors of any PCa (all p < 0.001); however, at multivariable analysis only PI-RADS Score was independent predictor of any PCa (p = 0.027). At multivariable analysis only PI-RADS score was independent predictor of csPCa. CONCLUSIONS: Fusion PB guarantees high diagnostic accuracy for csPCa, regardless of the fusion technology. Prospective randomized study is needed to confirm these data.
PURPOSE: Many software for US/MRI guided fusion prostate biopsy (FPB), have been developed in the last years. However, there are few data comparing diagnostic accuracy of different fusion systems. We assessed diagnostic performance of elastic (EF) versus rigid fusion (RF) PB in a propensity score matched (PSM) analysis. METHODS: A total of 314 FPB were prospectively collected from two different centers. All patients were biopsy naïve and all mpMRI reported a single suspicious area. Overall, 211 PB were performed using a RF system and 103 using an EF software. The two groups were compared for the main clinical features. A 1:1 PSM analysis was employed to reduce covariate imbalance to < 10%. Detection rate (DR) for any prostate cancer (PCa) and clinically significant (cs) PCa were compared and stratified for PI-RADS Score. A per target univariable and multivariable regression analyses were applied to identity predictors of anyPCa and csPCa. RESULTS: After applying the PSM, two cohorts of 83 cases were selected. DR of any PCa cancer and csPCa were comparable between the two cohorts (all p > 0.077) as well as DR of csPCa for every PIRADS score. At univariable regression analysis lesion size, PI-RADS Score, PSA Density and EF system were predictors of any PCa (all p < 0.001); however, at multivariable analysis only PI-RADS Score was independent predictor of any PCa (p = 0.027). At multivariable analysis only PI-RADS score was independent predictor of csPCa. CONCLUSIONS: Fusion PB guarantees high diagnostic accuracy for csPCa, regardless of the fusion technology. Prospective randomized study is needed to confirm these data.