Validation of a 3D-printed robot-assisted partial nephrectomy training model.

Objectives: Most renal tumours can be treated with a partial nephrectomy, with robot-assisted partial nephrectomy becoming the new gold standard. This procedure is challenging to learn in a live setting, especially the enucleation and renorraphy phases. In this study, we attempted to evaluate face, content, and preliminary construct validity of a 3D-printed silicone renal tumour model in robotic training for robot-assisted partial nephrectomy.

Materials And Methods: We compared the operative results of three groups of surgeons with different experience levels (>20 partial nephrectomies, 1-20 partial nephrectomies and no experience at all) performing a robotic tumour excision of a newly developed silicone model with four embedded 3D-printed renal tumours. We evaluated the participants' performance using surgical margins, excision time, total preserved parenchyma, tumour injury and GEARS score (as assessed by two blinded experts) for construct validity. Postoperatively, the participants were asked to complete a survey to evaluate the usefulness, realism and difficulty of the model as a training and/or evaluation model. NASA-TLX scores were used to evaluate the operative workload.

Results: Thirty-six participants were recruited, each group consisting of 10-14 participants. The operative performance was significantly better in the expert group as compared to the beginner group. NASA-TLX scores proved the model to be of an acceptable difficulty level.Expert group survey results showed an average score of 6.3/10 on realism of the model, 8.2/10 on the usefulness as training model and 6.9/10 score on the usefulness as an evaluation tool. GEARS scores showed a non-significant tendency to improve between trials, emphasizing its potential as a training model.

Conclusion: Face and content validity of our 3D renal tumour model were demonstrated. The vast majority of participants found the model realistic and useful for training and for evaluation. To evaluate construct and predictive validity, we require further research, aiming to compare the results of 3D-model trained surgeons with those of untrained surgeons in real-life surgery.