**6. Future directions**

interactive VR exercises and virtual instruments in five modules: colon mobilisation, kocherisation of duodenum, hilar dissection, kidney mobilisation, tumour resection and renorrhaphy. In the final module, an embedded VR exercise was developed, whereby a mobile sponge could be manipulated around a central pivot point (renal hilum) and sutured. This platform was internally validated throughout development, and concurrent validity was assessed by comparison to an in-vivo porcine model. Expert surgeons rated the platform a useful tool for training residents and fellows particularly with respect to teaching the steps of the procedure and surgical anatomy. Performance in the VR renorrhaphy task correlated with that of the invivo porcine model in the intermediate and expert groups. While this platform is a significant progression towards procedure-specific VR simulation, further advances are needed before this could feasibly replace wet lab training. Allowing the user to alter the surgical view and perform embedded tasks for each step of the procedure would likely increase validity.

With substantial progress having being made in surgical simulation, the next challenge is formally integrating this into surgical training programmes. At present, access to simulation is often limited and certainly is not routinely incorporated into trainee assessment and technical skill development [59]. The learning curves for minimally invasive renal cancer surgery and in particular partial nephrectomy are well documented, and subsequently complications early in the surgical experience are more likely [43]. Progressing training surgeons along the learning curve in the safety of the simulation environment has obvious benefits to patient outcomes. Simulators can also be utilised at the convenience of the trainee accommodating theatre and on-call commitments and local work-time directives. Furthermore, multiple studies have demonstrated the positive attitude of trainees towards simulation with benefits reported in learning anatomy, procedural steps, skill acquisition and confidence for subsequent perfor-

An ideal training programme needs to match the trainee with appropriate levels of simulation and operating theatre exposure [60]. Initially, trainees should acquire basic skills on lower fidelity VR simulators, with higher fidelity bench models and whole procedure simulation on live animals or human cadavers introduced with subsequent progression [10]. Advancement through simulation platforms should be coupled with, or followed by, a modular training programme for live operative cases. Modular training involves the breakdown of a procedure into sequential steps of increasing difficulty. Novice trainees begin with a period of observation and assistance and subsequently progress through each graded step of the procedure [61]. Under this structure, a whole procedure shall only be attempted once a trainee has indi-

The European Association of Urology (EAU) Robotic Urology Section (ERUS) training curriculum has been endorsed by British Association of Urological Surgeons (BAUS) and incorporates such an approach (**Figure 1**) [62]. This programme has already been validated for

**5. Training in renal cancer surgery**

84 Evolving Trends in Kidney Cancer

mance in the operating theatre [19].

vidually mastered all steps of the procedure.

robotic-assisted radical prostatectomy [63].

Robotic surgery is set to become even more widespread as new competitors enter the market and the demand for training will subsequently increase [65]. Surgical simulation will no doubt play a critical role meeting this demand, and an increase in the commercial availability of new platforms is anticipated. The ultimate simulation platform would be high-fidelity, low cost, readily available and translate to improved performance in the operating theatre. The validation process for new developments needs to be robust as resources are finite, and training time needs to be optimised. Even with the recent advancements in simulation, only limited evidence exists to establish the correlation between simulation performance and actual intraoperative performance [66]. This is the ultimate end-goal of the simulation process, and future research needs to focus on establishing this link.

[3] European Union. European Work Time Directive. Available from: https://eur-lex.europa. eu/legal-content/EN/ALL/?uri=CELEX:32003L0088. Accessed 7th February 2019

Simulation and Training in Kidney Cancer Surgery http://dx.doi.org/10.5772/intechopen.85683 87

[4] Al Bareeq R, Jayaraman S, Kiaii B, Schlachta C, Denstedt JD, Pautler SE. The role of surgical simulation and the learning curve in robot-assisted surgery. Journal of Robotic

[5] Schreuder HWR, Wolswijk R, Zweemer RP, Schijven MP, Verheijen RHM. Training and learning robotic surgery, time for a more structured approach: A systematic review. BJOG: An International Journal of Obstetrics and Gynaecology. Jan 2012;**119**(2):137-149 [6] Moglia A, Ferrari V, Morelli L, Ferrari M, Mosca F, Cuschieri A. A systematic review of virtual reality simulators for robot-assisted surgery. European Urology. Jun 2016;**69**(6):

[7] Wass V, Van der Vleuten C, Shatzer J, Jones R. Assessment of clinical competence. Lancet.

[8] McDougall EM. Validation of surgical simulators. Journal of Endourology. 2007;**21**(3):

[9] Abboudi H, Khan MS, Aboumarzouk O, Guru KA, Challacombe B, Dasgupta P, et al. Current status of validation for robotic surgery simulators—A systematic review. BJU

[10] Aydin A, Raison N, Khan MS, Dasgupta P, Ahmed K. Simulation-based training and assessment in urological surgery. Nature Reviews. Urology. 2016 Sep 23;**13**(9):503-519 [11] Golab A, Smektala T, Kaczmarek K, Stamirowski R, Hrab M, Slojewski M. Laparoscopic partial nephrectomy supported by training involving personalized silicone replica poured in three-dimensional printed casting mold. Journal of Laparoendoscopic &

[12] Fernandez A, Chen E, Moore J, Cheung C, Erdeljan P, Fuller A, et al. First prize: A phantom model as a teaching modality for laparoscopic partial nephrectomy. Journal of

[13] Maddox MM, Feibus A, Liu J, Wang J, Thomas R, Silberstein JL. 3D-printed soft-tissue physical models of renal malignancies for individualized surgical simulation: A feasibil-

[14] Monda SM, Weese JR, Anderson BG, Vetter JM, Venkatesh R, Du K, et al. Development and validity of a silicone renal tumor model for robotic partial nephrectomy training.

[15] Hidalgo J, Belani J, Maxwell K, Lieber D, Talcott M, Baron P, et al. Development of exophytic tumor model for laparoscopic partial nephrectomy: Technique and initial experi-

[16] Hung AJ, Ng CK, Patil MB, Zehnder P, Huang E, Aron M, et al. Validation of a novel robotic-assisted partial nephrectomy surgical training model. BJU International. 2012;

Surgery. May 2008;**2**(1):11-15

1065-1080

244-247

2001;**357**(9260):945-949

International. 2013 Feb;**111**(2):194-205

Endourology. 2012;**26**(1):1-5

Urology. Apr 2018;**114**:114-120

**110**:870-874

ence. Urology. May 2005;**65**(5):872-876

Advanced Surgical Techniques. 2017;**27**(4):420-422

ity study. Journal of Robotic Surgery. 2018;**12**(1):27-33

Patient-specific simulation has already arrived with the advent of 3D printing, and progress in this field is likely to be rapid as the technology becomes more readily available and cost effective [14, 26, 58]. It is conceivable that in the near future, patient's anatomical and oncological variations will be able to be reproduced in a model with incredible accuracy and detail. Advancements in model complexity are also anticipated, and the possibility of incorporating perinephric fat and vascular perfusion will no doubt increase the utility of this technology.

Finally, artificial intelligence (AI) has had large impacts outside of medicine and is starting to be adapted into the surgical field. From autonomous surgery to virtual assistants, the possibilities are seemingly infinite. Of particular interest in training and simulation is the use of machine learning algorithms to assess and track surgical performance. These algorithms are able to rapidly analyse vast quantities of data in order to determine relationships that may not be apparent to the human eye or traditional statistical methodology [67]. Recently, Hung and colleagues [68] were able to use intraoperative data captured from a recording device (dVLogger; Intuitive Surgical, Inc.) to develop automated performance metrics (APMs) for robotic prostatectomy. Using these APMs, the authors were able to predict clinical outcomes including length of stay, procedural time and catheter duration. Such sophisticated procedural feedback could be very beneficial for training purposes and allow bespoke tailoring of training based on the identified needs of the individual.
