Virtual bladder tumor transurethral resection: an objective evaluation tool to overcome learning curves with and without photodynamic diagnostics

Abstract

Introduction: Virtual reality (VR) simulation has been known to shorten learning curves. The aim of this research was to determine whether VR transurethral bladder tumor resection (TURBT) with and without photodynamic diagnostic (PDD) can improve endourological skills in novice and trained endoscopists.

Materials and methods: Residents without experience (n = 15) completed five 5-min white light VR-TURBT scenarios. The trained endoscopists (≥25 TURBTs; n = 15) performed five 5-min VR resections using white light and PDD cystoscopy. The VR-TURBT performance with the Uro-Trainer® (Karl Storz GmbH, Germany) was compared between the first and last 5 cases using computer-generated parameters [inspected area (%), resected tumor mass (%) and bleeding (ml)]. The trained endoscopists' parameters were compared with white light versus PDD-VR-TURBT.

Results: Among novices, the inspected area increased from 36.8 (±12.9) to 54.3% (±7.3), p < 0.05. Significant improvements were noted for resection rates from 26.5 (±9.6) to 52.0% (±6.0), p < 0.05. Trained urologists showed superior inspection [52.2 (±3.4) vs. 62.7% (±4.3), p = 0.003] and resection rates [43.8 (±3.3) vs. 57.1% (±5.3), p = 0.002] with PDD.

Conclusions: The opportunity to evaluate objective patient-independent measurements establishes the Uro-Trainer as a beneficial tool in enhancing TURBT quality. Results demonstrated that novices improved their ability to perform complete resection, and the TURBT rates of trained urologists were superior in PDD compared with a white light setting.