Abstract: PD46-12
Sources of Funding: Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM088129. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health._x000D_

Introduction

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.

Funding

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM088129. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health._x000D_