Purpose: The aim of this study was to assess the impact of experience on the outcome of image fusion-guided prostate biopsies performed by urologists working at a high-volume medical center.
Methods: The first 210 consecutive fusion biopsies were analyzed following installation of the software-based biopsy platform Artemis™ (Eigen, USA). The impact of training was measured in terms of changes in prostate cancer detection rates and biopsy duration over time. We sought to identify a threshold of experience for urologists, which predicts higher detection rates of targeted biopsies. The influence of various factors on prostate cancer detection rates was evaluated using bi- and multivariate analysis.
Results: Twenty-two urologists (n = 9 senior urologists, n = 13 urological residents) performed targeted biopsies followed by systematic 12-core biopsies. Overall, targeted biopsies yielded a positive result in 39.6% of 260 suspicious MRI lesions. A subgroup analysis of the six urologists who performed more than ten biopsies was then conducted, and their level of experience (i.e., performance of more than eight biopsies) was found to be associated with higher detection rates than those with less experience (49.0% and 23.0%, respectively; p < 0.001) in the targeted biopsies. Experience was likewise a significant and independent predictor of a cancer-positive targeted biopsy (p = 0.002). Experienced senior physicians did not outperform residents in their targeted biopsy results. Further, biopsy duration correlated negatively (r = - 0.5931, p < 0.001) with the total number of biopsies performed for all subgroups during the period of assessment.
Conclusions: Experience is an important predictor of the rate of detection in targeted biopsies using software-based biopsy platforms with semi-robotic assistance. Moreover, the performance of just a few procedures appears sufficient to increase biopsy effectiveness significantly. Lastly, supervision by experts is recommended during the training phase.
Keywords: Biopsy; Fusion; Learning curve; Multiparametric MRI; Prostatic neoplasm.