The Learning Curve for Magnetic Resonance Imaging/Ultrasound Fusion-guided Prostate Biopsy.

Magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) is more accurate at detecting clinically significant prostate cancer than conventional transrectal ultrasound-guided systematic biopsy. However, learning curves for attaining accuracy may limit the generalizability of published outcomes.

To delineate and quantify the learning curve for FBx by assessing the targeted biopsy accuracy and pathological quality of systematic biopsy over time.

We carried out a retrospective analysis of 173 consecutive men who underwent Artemis FBx with computer-template systematic sampling between July 2015 and May 2017.

The accuracy of targeted biopsy was determined by calculating the distance between planned and actual core trajectories stored on Artemis. Systematic sampling proficiency was assessed via pathological analysis of fibromuscular tissue in all cores and then comparing pathology elements from individual cores from men in the first and last tertiles. Polynomial linear regression models, change-point analysis, and piecewise linear regression were used to quantify the learning curve.

A significant improvement in targeted biopsy accuracy occurred up to 98 cases (p<0.01). There was a significant decrease in fibromuscular tissue in the systematic biopsy cores up to 84 cases (p<0.01) and an improvement in pathological quality when comparing systematic cores from the first and third tertiles. Use of a different fusion platform may limit the generalizability of our results.

There is a significant learning curve for targeted and systemic biopsy using the Artemis platform. Improvements in accuracy of targeted biopsy and better sampling for systematic biopsy can be achieved with greater experience.

We define the learning curve for magnetic resonance imaging/ultrasound-guided fusion biopsy (FBx) using targeted biopsy accuracy and systematic core sampling quality as measures. Our findings underscore the importance of overcoming learning curves inherent to FBx to minimize patient discomfort and biopsy risk and improve the quality of care for accurate risk stratification, active surveillance, and treatment selection.

European urology oncology. 2018 Aug 17 [Epub]

Khushabu Kasabwala, Neal Patel, Eliza Cricco-Lizza, Adrian A Shimpi, Stanley Weng, Rose M Buchmann, Samaneh Motanagh, Yiyuan Wu, Samprit Banerjee, Francesca Khani, Daniel J Margolis, Brian D Robinson, Jim C Hu

Department of Urology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA., Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA., Department of Pathology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA., Department of Healthcare Policy and Research, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA., Department of Urology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA; Department of Pathology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA., Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA., Department of Urology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA. Electronic address: .