Patient-Specific Virtual Simulation—A State of the Art Approach to Teach Renal Tumor Localization
Arun Rai, Jason M. Scovella, AngXua Adithy Balasubramaniana, Ryan Siller, Taylor Kohn, Young Moon, Naveen Yadav, Richard E. Linka
To test whether a novel visuospatial testing platform improves trainee ability to convert two-dimensional to three-dimensional (3D) space.
Medical students were recruited from Baylor College of Medicine and McGovern Medical School (Houston, TX). We 3D reconstructed 3 partial nephrectomy cases using a novel, rapid, and highly accurate edge-detection algorithm. Patient-specific reconstructions were imported into the dV-Trainer (Mimics Technologies, Seattle, WA) as well as used to generate custom 3D printed physical models. Tumor location was altered digitally to generate 9 physical models for each case, 1 with the correct tumor location and 8 with sham locations. Subjects were randomized 1:1 into the dV-Trainer (intervention) and No-dV-Trainer (control) groups. Each subject completed the following steps: (1) visualization of computed-tomographic images, (2) visualization of the reconstructed kidney and tumor in the dV-Trainer (intervention group only), and (3) selection of the correct tumor location on the 3D printed models (primary outcome). Normalized distances from the correct tumor location were quantified and compared between groups.
A total of 100 subjects were randomized for this study. dV-Trainer use significantly improved subjects ability to localize tumor position (tumor localization score: 0.24 vs 0.38, P < .001). However, subjects in the No-dV-Trainer group more accurately assigned R.E.N.A.L. scores.
Even brief exposure to interactive patient-specific renal tumor models improves a novice’s ability to localize tumor location. Virtual reality simulation prior to surgery could benefit trainees learning to localize renal masses for minimally invasive partial nephrectomy.
Financial Disclosure: 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 as well as the loan of the dV-Trainer simulator platform from Mimics Technologies, Inc. (Seattle, WA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Mimics Technologies.
The authors declare no conflict of interest.
AR and JMS contributed equally to this work.