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OBJECTIVE: To demonstrate the key steps to perform robot-assisted magnetic resonance imaging-ultrasound fusion transperineal prostate biopsy. MATERIALS AND METHODS: Men with suspicion of prostate cancer underwent 3-Tesla multi-parametric MRI and were assigned a Prostate Imaging Reporting and Data System v2 score (PI-RADS). The prostate outline and suspicious lesions were marked by our radiologist using our software to produce a 3-dimensional prostate MRI model. All biopsies were performed under general anaesthesia and the real-time transrectal ultrasound model is created and subsequently fused with the MRI model using non-rigid software fusion. Transperineal targeted and systematic biopsy were then performed under stereotactic guidance using our robot-assisted prostate biopsy platform. Our clinically significant prostate cancer (Grade group ≥2) detection rates were previously described.1 RESULTS: Out of the 433 patients who underwent targeted and systematic biopsy, clinically-significant cancer detection rate was 46% (85% for PI- RADS 5 vs 38% for PI-RADS 4 vs 16% for PI-RADS 3; P < .001). Our overall complication rate was 13%, out of which the majority were Clavien-Dindo I (99%). The most common complications encountered were urinary retention (10%) and significant gross hematuria requiring bladder irrigation (2%). A higher prostate volume was associated with greater odds of urinary retention (OR 1.4, 95% CI: 1.21-1.65, P < .001 for every 10 mL increase in prostate volume). There was only 1 reported case of mild urinary tract infection. CONCLUSION: Robot-assisted transperineal prostate biopsy has established itself as a reliable and accurate method of prostate cancer detection with minimal morbidity.

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OBJECTIVES: To evaluate the clinically-significant prostate cancer (csCaP) detection rate of systematic (SBx) vs. targeted biopsy (TBx), after accounting for the overlapping systematic cores within the MRI regions of interest. MATERIALS AND METHODS: We identified 398 consecutive men who underwent both transperineal systematic and targeted biopsy between January 2015 to January 2019. We reclassified overlapping systematic cores in the MRI regions of interest as target cores. The detection rates of SBx and TBx were compared using McNemar's test. RESULTS: Detection rate of csCaP (grade group ≥2) was 42% (168/398). Median number of systematic and targeted cores were 23 (IQR 19-29) and 9 (IQR 6-12) respectively. A median of 3 (IQR 2-4) overlapping systematic cores were reclassified as targeted cores. After accounting for overlap, csPC detection rate on SBx decreased from 37% and 21% while the csCaP detection rate of TBx increased from 34% to 39% (both P < 0.001), with TBx having a better detection rate (39% vs. 21%, P < 0.001). A previous negative biopsy was associated with a lower risk of having csCaP on non-targeted SBx (OR 0.27, 95% CI: 0.12 - 0.58, P = 0.001). Only 5% (13/243) of those who had no cancer detected on TBx had csCaP on non-targeted SBx compared to 45% (70/155) of those who had csCaP on TBx (P< 0.001). CONCLUSIONS: The utility of SBx in detecting csCaP decreases after accounting for overlap into the MRI region of interest, especially in men with a prior negative biopsy. Overlapping systematic cores improve the csCaP detection rate on TBx.

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PURPOSE: Several multiparametric magnetic resonance imaging (mpMRI)-based models have been developed with significant improvements in diagnostic accuracy for clinically significant prostate cancer (csCaP), but lack proper external validation. We therefore sought to externally validate and compare all published mpMRI-based csCaP risk prediction models in an independent Asian population. PATIENTS AND METHODS: A total of 449 men undergoing combined transperineal fusion-targeted/systematic prostate biopsy at our specialist center between 2015 to 2019 were retrospectively analyzed. csCaP was defined as lesions with ISUP (International Society of Urological Pathology) grade group ≥2. The performance of 6 mpMRI-based risk models (MRI-ERSPC-3/4, Distler, Radtke, Mehralivand, van Leeuwen and He) were evaluated in terms of discrimination, calibration and clinical utility, using area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analyses. RESULTS: A total of 202 (45%) subjects were diagnosed with csCaP. All models demonstrated excellent accuracy with AUCs ranging from 0.75 to 0.86, and most significantly outperformed mpMRI PIRADSv2.0 (Prostate Imaging Reporting and Data System version 2.0) alone. The models by Mehralivand and He showed good calibration to our validation population, with respective intercepts of -0.08 and -0.84. All models were nevertheless recalibrated to the csCaP prevalence in our population for analysis. Decision curve analysis showed that above a threshold probability of 10%, all mpMRI-based models demonstrated superior net benefit compared to mpMRI PIRADSv2.0 or a biopsy-all-men strategy. The van Leeuwen model had the greatest net benefit, avoiding 39% of unnecessary biopsies while missing only 4% of csCaP, at a threshold probability of 15%. CONCLUSIONS: The mpMRI-based risk models demonstrate excellent discrimination and clinical utility and are easy to apply in practice, suggesting that individualized risk-based approaches can be considered over mpMRI alone to avoid unnecessary biopsies.

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