**3. Different modalities of simulation**

Simulators can broadly be divided into two categories: physical and 'virtual reality' simulators. Physical (or mechanical) simulators use physical objects as substitutes for patients and include bench-top models, animal tissue, live animals and human cadavers. Virtual reality simulators use a computer-based platform with artificially generated virtual environments to interact [9]. This group includes the recent introduction of 'augmented reality' platforms, which integrate real-life patient data into a virtual reality environment. The range of different modalities, as well as their perceived advantages and disadvantages is summarised in **Table 1**.

of minimally invasive surgical techniques, such as laparoscopy and robotic-assisted surgery, and their associated learning curves has further compounded the issue. As a result, the development of quality surgical training opportunities in the non-clinical setting has long been on the agenda of the profession, and today, surgical simulation has ascended to occupy a central role in the modern surgical curriculum [4, 5]. For *trainees*, simulation allows the opportunity to develop surgical skills in an environment free of risk to the patient. It overcomes the limitations of operating room exposure and affords flexibility in an often chaotic work schedule. For *trainers,* the controlled nature of simulation allows objective appraisal of performance and

The ideal simulator should have a significant educational impact, improve subsequent performance in the operating room, shorten the procedural learning curve and subsequently increase patient safety. For novices, it should offer a realistic introduction to basic technical skills, allowing part-task training, while becoming increasingly procedure-specific and

Simulators must be rigorously evaluated across a number of parameters before they can be used for training and assessment. Validity is a measure of the extent a simulator succeeds in teaching the skill for which it was designed [7]. An ideal simulator would perform well in all

• Content validity: the extent to which the simulator's content is representative of the skill

• Construct validity: the extent to which experienced and novice operators can be differentiated. • Concurrent validity: the extent to which the simulation correlates with the current gold

• Predictive validity: the extent to which future performance can be predicted by simulator

With the increased pressure on healthcare expenditure and efficiency, the importance of independent and robust validation is critical to ensure that resources are invested in simulator

Simulators can broadly be divided into two categories: physical and 'virtual reality' simulators. Physical (or mechanical) simulators use physical objects as substitutes for patients and

progression, as well as a tailored approach to meet individual learning needs.

**2. Development and validation of simulators**

patient-specific for the more experienced operator [6].

• Face validity: the extent to which the simulator is realistic.

platforms that provide the highest levels of educational impact [9].

of the following aspects of validity [8];

standard test used to measure the skill.

**3. Different modalities of simulation**

required to be learnt.

76 Evolving Trends in Kidney Cancer

performance.


dvSS, da Vinci skills simulator; RoSS, robotic surgical simulator; HoST, Hands-On Surgical Simulator; SEP, SimSurgery Educational Platform; 3D, three-dimensional.

**Table 1.** Available simulation modalities (adapted from Aydin et al. [10]).

#### **3.1. Physical simulators (mechanical)**

#### *3.1.1. Bench-top/'dry-lab' models*

Bench-top models are synthetic models that can vary from simple (i.e. peg-transfer) to more complex tasks (i.e. suturing and knot-tying) in order to acquire surgical skills. These are often incorporated into different surgical platforms via a box-trainer allowing the utilisation of actual surgical instruments and giving the trainee an opportunity to familiarise with the controls and limitations of that platform [12]. Higher-fidelity synthetic models can be utilised for more advanced skills and part-procedural simulation. With the advent of 3D printing, several authors have described high-fidelity partial-nephrectomy models whereby tumour excision and renorrhaphy can be rehearsed [13, 14, 25]. Patient-specific models have even been utilised by expert surgeons to pre-operatively rehearse RAPN in order to determine feasibility of PN and predict warm-ischaemia times [26].

*3.1.5. Virtual reality (VR) and augmented reality (AR) simulators*

commercially available products as outlined in **Table 2**.

Basic skills, procedural specific simulation (RARP, cystectomy, lymph node dissection)

Basic skills

Procedural simulation

Augmented Reality Procedural simulation (RAPN, RARP) [22]

radical prostatectomy; RAPN, robotic-assisted partial nephrectomy.

Basic skills Standalone

Basic skills Standalone

**Simulation model**

dV-Trainer Mimic

dvSS Intuitive

RoSS/HoST Simulated

SEP robot SimSurgery, Norway

Pro-MIS CAE

Maestro AR

RobotiX mentor

Technologies, USA

Surgical, USA

Surgical Systems, USA

Simbionix, USA

Healthcare, Canada

Mimic Technologies, USA

**Table 2.** Available VR simulation platforms.

Robotic surgery in particular lends itself to VR simulation, and as such, there has been a significant development in this modality in recent times. At present, there exist a number of

In recent years, the introduction of augmented reality (AR) simulators has provided increasingly realistic and procedure-specific platforms for simulation. The two AR systems in common use are the Hands-On Surgical Training (HoST) and the Maestro AR system. HoST

**Manufacturer Focus Advantages Disadvantages**

Availability

Standalone Availability

tasks (HoST)

Standalone Availability

module [38]

Availability

Standalone Availability

dV-Trainer, da Vinci trainer; dvSS, da Vinci skills simulator; RoSS, robotic surgical simulator; HoST, Hands-On Surgical Simulator; SEP, SimSurgery Educational Platform; 3D, three-dimensional; 2D, two-dimensional; RARP, robotic-assisted

Procedural simulation

29–31]

Extensively validated [20,

Extensively validated [32–34]

Extensively validated [35–37] Augmented reality procedural

Laparoscopic assistant

VR and use with box trainers

Uses actual console

Mechanically different hand

Can only be used when da Vinci robot not in use

Mechanically different hand

Limited availability outside

Mechanically different hand

Mechanically different hand

Less robust validity [39]

Originally designed for

Limited robotic validation [40] Mechanically different hand

Unable to manipulate surgical

No urological procedural

controls

Simulation and Training in Kidney Cancer Surgery http://dx.doi.org/10.5772/intechopen.85683 79

controls Cost

USA

controls

2D vision

controls

2D vision

laparoscopy

controls

field

tasks

Basic skills Standalone

Basic skills Fixed to console

#### *3.1.2. Ex-vivo animal tissue/'wet-lab' models*

Inanimate animal tissue has been used to simulate a range of endourological, laparoscopic and robotic-assisted procedures ex-vivo [10]. These models utilise the actual surgical instruments or console similar to dry-lab models and subsequently have similar advantages with regard to developing familiarity with the surgical platform. Porcine kidneys in particular have been utilised successfully for procedural simulation in renal cancer surgery and offer advantages in terms of higher-fidelity tissue handling and even the ability to be artificially perfused, allowing simulation of vascular control and haemostasis [16, 27]. These advantages need to be weighed against the special facilities required for storage and subsequent increased costs, which can be a limiting factor in some institutions.

#### *3.1.3. Live animal tissue*

Live animal models facilitate the closest simulation to live surgical cases and also provide an opportunity for whole procedural simulation. Whole-procedural simulation has the significant advantage, allowing development in dissection technique, energy control, vascular control and haemostasis techniques. Several groups have even described the creation of artificial tumours in live porcine models, subsequently allowing specific procedural simulation for robotic-assisted partial nephrectomy (RAPN) [15, 16]. Despite these benefits, however, the higher costs, ethical issues and local legislative restrictions can significantly impact the availability. Subsequently, access to live animal simulation is often limited to a few programmes.

#### *3.1.4. Cadaveric tissue*

Human cadaveric material has long been used in surgical training, and it is generally accepted that cadaveric simulation has the highest face validity of all simulation modalities [19, 28]. Simulation using fresh frozen cadavers (FFCs) or thiel-embalmed cadavers (TECs) has shown face, content and construct validity in a range of endourological and laparoscopic procedures [10]. Despite utilisation in various training programmes, validation of the effectiveness of cadaveric training in robotic-assisted procedures remains limited [28], and further research in this area is needed.

#### *3.1.5. Virtual reality (VR) and augmented reality (AR) simulators*

**3.1. Physical simulators (mechanical)**

and predict warm-ischaemia times [26].

*3.1.2. Ex-vivo animal tissue/'wet-lab' models*

*3.1.3. Live animal tissue*

*3.1.4. Cadaveric tissue*

ther research in this area is needed.

costs, which can be a limiting factor in some institutions.

Bench-top models are synthetic models that can vary from simple (i.e. peg-transfer) to more complex tasks (i.e. suturing and knot-tying) in order to acquire surgical skills. These are often incorporated into different surgical platforms via a box-trainer allowing the utilisation of actual surgical instruments and giving the trainee an opportunity to familiarise with the controls and limitations of that platform [12]. Higher-fidelity synthetic models can be utilised for more advanced skills and part-procedural simulation. With the advent of 3D printing, several authors have described high-fidelity partial-nephrectomy models whereby tumour excision and renorrhaphy can be rehearsed [13, 14, 25]. Patient-specific models have even been utilised by expert surgeons to pre-operatively rehearse RAPN in order to determine feasibility of PN

Inanimate animal tissue has been used to simulate a range of endourological, laparoscopic and robotic-assisted procedures ex-vivo [10]. These models utilise the actual surgical instruments or console similar to dry-lab models and subsequently have similar advantages with regard to developing familiarity with the surgical platform. Porcine kidneys in particular have been utilised successfully for procedural simulation in renal cancer surgery and offer advantages in terms of higher-fidelity tissue handling and even the ability to be artificially perfused, allowing simulation of vascular control and haemostasis [16, 27]. These advantages need to be weighed against the special facilities required for storage and subsequent increased

Live animal models facilitate the closest simulation to live surgical cases and also provide an opportunity for whole procedural simulation. Whole-procedural simulation has the significant advantage, allowing development in dissection technique, energy control, vascular control and haemostasis techniques. Several groups have even described the creation of artificial tumours in live porcine models, subsequently allowing specific procedural simulation for robotic-assisted partial nephrectomy (RAPN) [15, 16]. Despite these benefits, however, the higher costs, ethical issues and local legislative restrictions can significantly impact the availability. Subsequently, access to live animal simulation is often limited to a few programmes.

Human cadaveric material has long been used in surgical training, and it is generally accepted that cadaveric simulation has the highest face validity of all simulation modalities [19, 28]. Simulation using fresh frozen cadavers (FFCs) or thiel-embalmed cadavers (TECs) has shown face, content and construct validity in a range of endourological and laparoscopic procedures [10]. Despite utilisation in various training programmes, validation of the effectiveness of cadaveric training in robotic-assisted procedures remains limited [28], and fur-

*3.1.1. Bench-top/'dry-lab' models*

78 Evolving Trends in Kidney Cancer

Robotic surgery in particular lends itself to VR simulation, and as such, there has been a significant development in this modality in recent times. At present, there exist a number of commercially available products as outlined in **Table 2**.

In recent years, the introduction of augmented reality (AR) simulators has provided increasingly realistic and procedure-specific platforms for simulation. The two AR systems in common use are the Hands-On Surgical Training (HoST) and the Maestro AR system. HoST


dV-Trainer, da Vinci trainer; dvSS, da Vinci skills simulator; RoSS, robotic surgical simulator; HoST, Hands-On Surgical Simulator; SEP, SimSurgery Educational Platform; 3D, three-dimensional; 2D, two-dimensional; RARP, robotic-assisted radical prostatectomy; RAPN, robotic-assisted partial nephrectomy.

**Table 2.** Available VR simulation platforms.

(Simulated Surgical Systems, USA) incorporates a real surgical procedure into the virtual reality framework and guides the user through an enhanced version of the operation, with audio-visual illustration, haptic cues and guided movements [24]. The HoST system currently does not offer procedural simulation for nephrectomy or partial nephrectomy. Maestro AR (Mimic Technologies, USA) provides procedure-specific 3D video and interaction via virtual reality robotic instruments. This includes a module on partial nephrectomy that demonstrates face, content, construct and concurrent validity [22].

complication rates similarly decreased. Despite the rabbit's smaller size compared to pigs for example, pneumoperitoneum was able to be established, and conventional instruments were used for all procedures. Reduction in acquisition and handling costs associated with the rabbits allowed the authors to provide a more prolonged period of training demonstrating the

Simulation and Training in Kidney Cancer Surgery http://dx.doi.org/10.5772/intechopen.85683 81

Cruz and colleagues [48] assessed the impact of repeated LRN in the porcine model on overall surgical performance among established surgeons. Six urologists with limited laparoscopic experience were recruited to perform a live porcine LRN weekly for 10 weeks. Surgical performance was judged quantitatively including total operative time and estimated blood loss. Qualitative measures were also assessed using the Global Operative Assessment of Laparoscopic Skills (GOALS) including depth perception, dexterity, efficiency, tissue handling and autonomy. Over the course of the study, blood loss, depth perception and dexterity showed statistically significant improvements. The remaining domains including operative

Despite the obvious benefits of high-fidelity animal models, the costs and associated ethical issues restrict access which is often limited to several day courses. A high-fidelity virtual reality LRN simulation platform has obvious advantages in overcoming some of these barriers. The LAP Mentor (Simbionix, USA) and LapSim (Surgical Sciences, Sweden) are two commercially available laparoscopic simulators, which provide VR laparoscopic training including a full nephrectomy module. While both simulators have been validated in terms of basic laparoscopic skills [49, 50], the nephrectomy modules remain to be formally scientifically assessed. Despite this, these simulators provide full procedure simulation that is reproducible and able to provide feedback on performance metrics such as economy of motion, procedure time and error rates. These metrics have potential utility in assessing progression and setting

With the advent of widespread cross-sectional imaging, there has been a surge in incidental detection of small renal masses. This has subsequently led to increased utilisation of partial nephrectomy (PN) in order to preserve normal renal parenchyma in these otherwise well patients [51]. PN is a technically challenging operation with a significant learning curve and variability unrivalled by almost any other frequently performed kidney procedure [52]. Perhaps, most challenging, however, is the *time-critical* nature of PN. The vast blood supply to the kidney means bleeding is a significant intraoperative risk and efficient excision, and renorrhaphy is therefore crucial. Furthermore, prolonged warm ischaemia is deleterious to healthy renal tissue and can impact post-operative renal function [53, 54]. Finally, each tumour is highly variable in size, location and relation to critical structures, making oncological excision a persistent challenge even for experienced surgeons. For these reasons, training in PN is subsequently fraught with complexity, and mentors must try and negotiate sometimes the discordant goals of training with patient safety. Simulation for PN has rapidly progressed in response to this dilemma, and the availability of PN models is becoming more widespread.

impact of repetition on learning curves and complication rates.

time showed no statistical improvement.

benchmarks for training curriculums.

**4.2. Partial nephrectomy**

*4.1.2. Virtual reality*
