Category: Education, Simulation & Virtual Reality

MP19-16 - Patient-specific virtual reality simulation improves visuospatial recognition of renal masses: A multi-institutional, prospective, randomized controlled study

Sat, Sep 22
2:00 PM - 4:00 PM

Introduction & Objective : Robotic-assisted laparoscopic partial nephrectomy (RALPN) has become the established standard of care for nephron-sparing therapy for renal cancer. Difficulty in visualizing tumor location can result in unnecessarily lengthy operative and ischemic times, damage to important renal structures and unnecessary removal of viable and uninvolved parenchyma. To potentially accelerate the learning curve for RALPN, we hypothesized that manipulating a patient-specific tumor/kidney model within a 3D virtual simulator (dV-Trainer) improves a novice’s ability to localize tumor location on a physical model as compared to interpreting standard 2D planar imaging alone.

Methods : Medical students were recruited from two institutions in Houston. The three patient-specific 3D models were reconstructed based on preoperative CT imaging and were converted into both a virtual reality model within the dV-Trainer environment (intervention) and a physical 3D printed plaster model (primary outcome). Participants were randomized 1:1 into two groups – “No dV-Trainer” and “dV-Trainer” .Upon review of the CT imaging, the “dV-Trainer” group was then allowed to review and manipulate the renal model in the dV-Trainer virtual reality trainer then asked to select the best 3D printed plaster model that represented the model viewed in the trainer and assign nephrometry score. The “No dV-Trainer” group was asked to complete the same exercises immediately after viewing the CT imaging only.

Results : Both experimental groups were well matched. Overall, subjects in the dV-Trainer group demonstrated improved tumor localization compared to subjects that did not use the dV-Trainer(Tumor Localization Score: 0.24 vs. 0.38, p<0.001). We found that only dV-Trainer use was associated with improved tumor localization (β = 0.137, 95% CI: 0.070 – 0.203, p < 0.001). Age, 3D aptitude score, dV-Trainer warm-up scores, ability to assign the correct R.E.N.A.L. Nephrometry score, MS year, and desired future specialty did not associate with tumor localizing ability.

Conclusions : Visualizing patient-specific kidneys and tumors within the dV-Trainer 3D surgical simulation environment improves a novice’s ability to localize correct tumor location in the physical world. Use of a virtual reality simulator such as the dV-Trainer prior to surgery may benefit trainees learning how to properly localize and resect renal masses during minimally invasive partial nephrectomy.


Resident Physician
Baylor College of Medicine
Houston, Texas

Jason Scovell

Houston, Texas

Richard Link

Houston, Texas