You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment III1 Apr 2017PD46-12 VIRTUAL SIMULATION IMPROVES A NOVICE′S ABILITY TO LOCALIZE RENAL TUMORS IN 3D PHYSICAL MODELS – A MULTI-INSTITUTIONAL PROSPECTIVE RANDOMIZED CONTROLLED STUDY Arun Rai, Jason Scovell, Adithya Balasubramanian, Ang Xu, Ryan Siller, Taylor P. Kohn, Young Moon, Naveen Yadav, and Richard Link Arun RaiArun Rai More articles by this author , Jason ScovellJason Scovell More articles by this author , Adithya BalasubramanianAdithya Balasubramanian More articles by this author , Ang XuAng Xu More articles by this author , Ryan SillerRyan Siller More articles by this author , Taylor P. KohnTaylor P. Kohn More articles by this author , Young MoonYoung Moon More articles by this author , Naveen YadavNaveen Yadav More articles by this author , and Richard LinkRichard Link More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2384AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Efficient robotic partial nephrectomy requires a precise understanding of tumor location and relationship to vital structures. Translating standard imaging into a reliable 3D mental model is challenging, especially for inexperienced surgeons. We sought to determine if renal tumor visualization and manipulation within a robotic virtual simulator improves the ability of novices to accurately identify tumor location in 3D space. METHODS We recruited medical students from Baylor College of Medicine and UT McGovern Medical School. Using a custom-built algorithm, two volumetric reconstructions from CT imaging were generated and imported into the dV-Trainer. For each tumor, 9 different model variations were 3D printed (1 real, 8 with modified tumor locations). Subjects were randomized 1:1 into two groups, dV-Trainer and non dV-Trainer, and were given 5 minutes to review CT images. Subjects in the dV-Trainer group were allowed to manipulate the virtual model for an additional 5 minutes. They were then asked to identify the model corresponding to the real tumor in each case and to assign a nephrometry score. The primary outcome was distance of the tumor selected from the correct location. RESULTS 100 subjects participated and all were included for analysis. There was no difference in subject age (mean: 23.6 ± 2.2) or training year between groups. Subjects in the dV-Trainer group more accurately visualized tumor location (Normalized distance: Model 1: sim 0.17 ± 0.23 vs. no-sim 0.31 ± 0.31, p=0.012; Model 2: sim 0.12 ± 0.28 vs. no-sim 0.34 ± 0.39, p=.001). These findings were not affected by age or year of training. Surprisingly, subjects in the dV-Trainer group had more difficulty assigning the correct nephrometry score than those in the non-dV-Trainer group. CONCLUSIONS In this prospective randomized trial, exposure to a patient-specific virtual model improves the novice ability to accurately visualize tumor location when compared to interpreting standard planar CT images alone. This workflow, including our novel reconstruction algorithm, provides a streamlined method for generating patient-specific kidney anatomic simulations which may be valuable for teaching surgical trainees and visualizing complex tumor cases before surgery. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e895 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Arun Rai More articles by this author Jason Scovell More articles by this author Adithya Balasubramanian More articles by this author Ang Xu More articles by this author Ryan Siller More articles by this author Taylor P. Kohn More articles by this author Young Moon More articles by this author Naveen Yadav More articles by this author Richard Link More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...