MP58-09: Diagnostic performance of Fusion (US/MRI guided) prostate Biopsy: propensity score matched comparison of elastic versus rigid fusion system

Mariaconsiglia Ferriero*, Rome, Italy, Cristian Fiori, Orbassano, Turin, Italy, Gabriele Tuderti, Umberto Anceschi, Alfredo Maria Bove, Aldo Brassetti, Leonardo Misuraca, Salvatore Guaglianone, Riccardo Mastroianni, Michele Gallucci, Rome, Italy, Sabrina Decillis, Enrico Checcucci, Francesco Porpiglia, Orbassano, Turin, Italy, Giuseppe Simone, Rome, Italy

Introduction: Many software for US/MRI guided prostate biopsy (PB), available for elastic or rigid fusion technology, have been developed in the last years. However, there are few data comparing diagnostic accuracy of different systems based on fusion software. We assessed diagnostic performance of elastic (EF) versus rigid fusion (RF) PB in a propensity score matched  (PSM) analysis.

Methods: A total of  314 fusion PB were prospectively collected from 2 different centres.

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 a 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.

Chi-square and Mann-Whitney test were used to compare categorical and continuous variables, respectively. A per target Univariable and Multivariable regression analysis were applied to identity predictors of any PCa and cs PCa.

Results: The two cohorts were compared for the main clinical variables. After applying the PSM  two cohorts of 83 cases were selected (Table 1). DR of any PCa cancer and csPCa was comparable between the two cohorts (all p>0.077). DR of csPCa was comparable between the two cohorts for every PIRADS score. (Table 2).  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 univariable regression analysis PI-RADS Score (p < 0.001) and PSA-density (p=0.02) were predictors of csPCa while at multivariable analysis only PI-RADS score was independent predictors of csPCa (Table 3).

Conclusions: Fusion PB guarantee high diagnostic accuracy for csPCa, regardless the fusion technology. Prospective randomized study are needed to confirm these data. PI-RADS score remains the only independent predictor of csPCa.

Source of Funding: None