# From visualization to education: the role of 3D-Printed and virtual kidney models in training for renal cancer surgery, a systematic review

Federico Rubat Baleuri, Maxime Pattou, Manon Jaffredo, Gabrielle Lacroix, Camille Courtine, Joffrey Sarrazin, Marleine Tamer, Hugo Larribere, Solène Ricard, Eva Jambon, et al.

► To cite this version:

Federico Rubat Baleuri, Maxime Pattou, Manon Jaffredo, Gabrielle Lacroix, Camille Courtine, et al. From visualization to education: the role of 3D-Printed and virtual kidney models in training for renal cancer surgery, a systematic review. Journal of Robotic Surgery, 2025, 20 (1), pp.52. 10.1007/s11701-025-02961-7. hal-05400972

HAL Id: hal-05400972

https://hal.science/hal-05400972v1

Submitted on 5 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# From Visualization to Education: The Role of 3D-Printed and Virtual Kidney Models in Training for Renal Cancer Surgery, A Systematic Review

## Authors :

Federico RUBAT BALEURI¹,², Maxime PATTOU¹,², Manon JAFFREDO¹,², Gabrielle LACROIX³, Camille COURTINE³, Joffrey SARRAZIN⁴, Marleine TAMER⁵, Hugo LARRIBERE⁴, Solène RICARD¹,², Eva JAMBON²,⁶, Jocelyn SABATIER⁴, Frédéric BOS⁴, Matthieu FAESSEL⁴, Olivier MONDAIN-MONVAL³, Jacques LENG⁵, Jean Christophe BERNHARD¹,²,⁷, Gaelle MARGUE¹,²,⁷

**Declarations of interest:** none

## Affiliations :

1 Urology Department, Bordeaux University Hospital, 33000 Bordeaux, France;

2 I.CaRe Bordeaux—BRIC Inserm U1312, 33200 Bordeaux, France

3 Centre de Recherche Paul Pascal, UMR 5031, Univ. Bordeaux and CNRS, 33600 Pessac, France

4 Department of Additive Fabrication Engineering, TechnoShop Coh@bit, Bordeaux University Technological Institute, Gradignan, France

5 LOF – Laboratory of the Future, University of Bordeaux, 33600 Pessac, France

6 Radiology Department, Bordeaux University Hospital, 33000 Bordeaux, France;

7 French AFU Cancer Committee Guidelines, 75017 Paris, France

federico.rubat-baleuri@chu-bordeaux.fr (F.R.B.); gaelle.margue@chu-bordeaux.fr (G.M.);

maxime.pattou@chu-bordeaux.fr (M.P.); manon.jaffredo@chu-bordeaux.fr (M.J.);

solene.ricard@chu-bordeaux.fr (S.R.); jean-christophe.bernhard@chu-bordeaux.fr (J.C.B.);

gabrielle.lacroix@u-bordeaux.fr (G.L.); camille.courtine@u-bordeaux.fr (C.C.);

joffrey.sarrazin@u-bordeaux.fr (J.S.); marleine.tamer-ext@syensqo.com (M.T.);

hugo.larribere@u-bordeaux.fr (H.L.); eva.jambon@chu-bordeaux.fr (E.J.);

jocelyn.sabatier@u-bordeaux.fr (J.S.); frederic.bos@u-bordeaux.fr (F.B);

matthieu.faessel@u-bordeaux.fr (M.F.); olivier.mondain@crpp.cnrs.fr (O.M.);

jaques.leng-exterieur@syensqo.com (J.L.)
Funding: This work has benefited from a government grant managed by the French national re-
search agency (ANR) under the third investment program for the future (PIA), part of France 2030,
reference ANR-21-RHUS-0015 and for a European grant managed by the Region Nouvelle Aqui-
taine.

ABSTRACT

**Objectives:**

This systematic review on novel 3D technologies aims to discuss the current evidence on the use-
fulness of 3D printing, virtual reality (VR) and augmented reality (AR) simulators in the education
and training of young urologists for kidney cancer surgery, highlighting the modalities employed,
their educational impact, and areas for future development.

**Materials and Methods:**

We performed a comprehensive literature search limited to the last 10 years across PubMed and Embase libraries, identifying studies evaluating the use of 3D technologies as educational tools in urologist training for kidney cancer surgery. The review followed PRISMA guidelines, and two reviewers independently screened eligible studies. We extracted data on study designs and on urologists' education outcomes, through different subcategories.

**Results:**

Seventeen studies were included, mostly small-scale validation or descriptive investigations.
Twelve investigated 3D-printed models and five VR/AR platforms. Simulations focused on laparo-
scopic and robot-assisted partial nephrectomy, often using patient-specific models for rehearsal and
skill development. Training outcomes included improved spatial anatomy understanding, increased
technical performance, greater procedural confidence, and enhanced familiarity with complex sur-
gical steps. However, considerable heterogeneity in methodology and limited sample sizes across
studies underscore the need for standardized evaluation of these educational tools.

**Conclusion:**

3D technologies, including 3D-printed models and VR/AR platforms, show promise in enhancing
surgical training for renal cancer by improving anatomical understanding and procedural skills.
These technologies demonstrate good precision and can help assess trainee surgical skill.
However, evidence remains limited, and further research is needed to validate their effectiveness,
cost-efficiency, and integration into standardized urological training curricula.
**Keywords** Renal cell carcinoma; 3D models; 3D printing; virtual reality; augmented reality; surgical training

ABBREVIATIONS

RCC: renal cell carcinoma

NSS: nephron-sparing surgery

RAPN: robot-assisted partial nephrectomy

3D: three-dimensional

VR: virtual reality

AR: augmented reality

AI: artificial intelligence

LN : laparoscopic nephrectomy
# 1. INTRODUCTION

Renal cell carcinoma (RCC) is a common malignancy, with an estimated 434.840 incident cases worldwide in 2022. In Europe, RCC ranks as the sixth most common cancer among men and the ninth among women [1]. Improvements in cross-sectional imaging techniques have contributed to a rise in the incidental detection of renal masses at earlier stages [2,3]. As a result, nephron-sparing surgery (NSS) has become the standard of care for localized T1 tumors [4] with robot-assisted partial nephrectomy (RAPN) now widely adopted for its precision and minimally invasive nature. The growing complexity of these procedures, especially those involving robotic platforms, poses significant challenges in surgical education. Traditional training paradigm, comprising dry labs, cadaveric dissections, animal models, and supervised live surgeries, are time-intensive, resource-heavy, and increasingly constrained by ethical, logistical, and patient safety considerations [5].

In response to these limitations, emerging three-dimensional (3D) technologies, including 3D printing, virtual reality (VR), and augmented reality (AR) platforms, have been introduced as novel tools in surgical education. These technologies are all based on kidney phantom reconstructions and offer immersive and customizable environments that enhance anatomical understanding, facilitate preoperative planning, and enable simulation of complex procedures and critical intraoperative events [6–8]. The present review focuses exclusively on the educational applications of these 3D technologies in renal surgery training, rather than their clinical or intraoperative impact.

Following the PICo framework, this review aims to synthesize the current evidence on the use and applications of 3D technologies, such as 3D-printed models and VR or AR simulation platforms, in the education of urology trainees for renal cancer surgery. Specifically, this review highlights the modalities employed, their educational impact on surgical skill acquisition and anatomical understanding, and areas for future development.

# 2. MATERIALS AND METHODS

## 2.1. SEARCH STRATEGY

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) 2020 guidelines [9]. The review protocol was registered in PROSPERO database (ID CRD42025627143).

A comprehensive literature search was performed in PubMed and Embase on the December 12, 2024, using a predefined combination of keywords and MeSH terms related to: renal surgery procedures (e.g., partial nephrectomy, radical nephrectomy, nephroureterectomy), 3D technologies (e.g., 3D printing, virtual reality, augmented reality, mixed reality), surgical training and education (e.g., simulation, surgical skills, residency training, medical education).

The full search strategy for each database is detailed in Supplementary File 01.

## 2.2. ELIGIBILITY CRITERIA

Studies were considered eligible for inclusion if they were original research articles published in English between 2014 and 2024, a time frame selected to ensure the relevance of included studies to current surgical education and training practices. Eligible studies investigated the use of 3D technologies (namely 3D printing, virtual reality (VR), augmented reality (AR), or simulation platforms) for training, education, or skill assessment specifically in the context of kidney cancer surgery. Ar-
ticles involving medical students, urology residents, or practicing surgeons were included, regard-
less of study design (e.g., randomized controlled trials, cohort studies, case series, or validation
studies). Only studies with full-text availability were considered for inclusion.

Articles were excluded if they did not present original data (e.g., reviews, editorials, commentaries, conference abstracts, or book chapters), if they did not focus on kidney cancer surgery, lacked a defined educational or training purpose, or if the use of 3D technologies was limited to patient counseling or applied outside the field of urology.

## 2.3. STUDY SELECTION

All retrieved records were imported into Zotero for reference management, and duplicate entries
were removed using the built-in duplicate detection tool, followed by manual verification.

Two independent reviewers screened the titles and abstracts based on the predefined inclu-
sion/exclusion criteria. Discrepancies were resolved through discussion and consultation with a
third reviewer. Full texts of the eligible articles were then reviewed to confirm inclusion. Addition-
ally, the reference lists of all included studies were manually searched for relevant articles not cap-
tured in the initial search.

## 2.4. DATA EXTRACTION

Data from the selected studies were extracted using a standardized Excel spreadsheet. The follow-
ing information was collected:
First author and year of publication,
Study design and sample size,
Type of surgical procedure,
Type of 3D technology used (3D-printing, VR, AR),
Use of control or comparison group,
Main educational or procedural outcome assessed,
Use of patient-specific vs. generic models,
Timing of model presentation (e.g., preoperative),
Reported costs,
Main findings and conclusions.
# 3. RESULTS

## 3.1. STUDY SELECTION

The initial database search yielded a total of 238 articles, 30 from PubMed and 208 from Embase. After the removal of duplicates, 208 unique records remained. Following title and abstract screening, 147 were excluded for not meeting the inclusion criteria. Of the 61 studies that underwent full-text review, an additional 47 were excluded due to irrelevance, lack of outcome data, or unavailability of the full text. Three additional studies were identified through reference screening, resulting in a final total of 17 studies included for qualitative synthesis. The selection process is illustrated in the PRISMA flow (Figure 1).

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only

<figure><img src="image_2.png" /><figcaption>Figure 1. PRISMA flow diagram for the search process</figcaption></figure>
## 3.2. STUDY CHARACTERISTICS

Most of the included studies were small-scale validation or descriptive studies. Considerable heterogeneity was observed across studies in terms of methodology, image processing techniques, simulation platforms, and educational outcomes. The majority of studies (10/17) assessed the role of 3D technologies in surgical simulation and skills training [10–19]. Three studies focused on anatomical comprehension [20–22] while four explored the use of 3D technologies for evaluation of training potential [23–26]. Details about the studies can be found in Table 1.

## 3.3. TYPE OF PROCEDURES SIMULATED

Among the included studies, one focused on the simulation of an intraoperative emergency (hemorrhage control) using a perfused kidney model [10]. Six studies focused on laparoscopic nephrectomy (radical or partial) [11, 12, 16, 23, 24, 27], while ten studies involved robot-assisted partial nephrectomies (RAPN) [10, 13–15, 17–19, 21, 22, 25, 26]. Across these procedural categories, the models were employed for rehearsal, assessment of technical performance, or evaluation of surgical decision-making.

## 3.4. TECHNOLOGIES USED: 3D PRINTING VS VR/AR

A majority of studies (12/17) utilized 3D-printed anatomical models [10, 14–20, 22, 24–26]. These models were based on 3D virtual reconstructions of kidneys derived from CT or MRI images, were often patient-specific, and served as tactile tools for surgical planning or simulation.

When reported, the printing materials varied considerably. Authors described using soft materials such as painted platinum-based silicone, PVA-hydrogel, mixtures of silicone rubber and oil, photo-polymer materials, or agarose gel to reproduce the elasticity of renal tissue. Other studies employed rigid materials such as translucent resin, plaster, or polylactic acid for structural or vascular models. Novel combinations (including Dragonskin 20 silicone mixed with Slacker silicone) were also tested to improve tissue realism.

Information about the printing methodology (e.g., material extrusion, material jetting, or vat photopolymerization) was seldom detailed in the original reports, which represents a limitation of current literature. However, when specified, the studies consistently reported outsourcing part or all of the 3D reconstruction or printing process to external industrial vendors rather than performing it in-house.

The remaining five studies employed VR or AR platforms [11–13, 21, 23], including commercial laparoscopic simulators such as LapPass (Inovus Medical, UK), LapSim (Surgical Science, Sweden), and LapVision Smart (MedVision, Russia/USA). These platforms provided visual and haptic feedback for procedural simulation and anatomical visualization.

Figure 2 illustrates the number of studies published per year and their respective topics.
<figure><img src="image_3.png" /><figcaption>Figure 2. Year of publication and corresponding number of studies. Studies on 3D Printed Models are shown in blue, while those concerning Virtual Reality / Augmented Reality technologies are shown in red.</figcaption></figure>

## 3.5. PERSONALIZATION OF MODELS

Personalization was a key differentiator between technologies. Eleven studies employed patient-specific models, typically in the form of 3D-printed kidneys based on CT or MRI data [14,15,17–22,24–26]. The remaining six studies used generic models that were not anatomically tailored to individual patients [10–13,16,23].

## 3.6. TIMING OF TECHNOLOGY INTEGRATION

In all studies, 3D technologies were implemented during the preoperative phase, either for rehearsal, surgical planning, or didactic teaching. No study reported the use of these technologies in the post-operative setting, such as for performance review or retrospective case analysis.

## 3.7. COST REPORTING

Several studies reported the cost of their 3D models or simulation systems. The cost of 3D-printed models ranged from $3.90 to $1,000 USD per unit, depending on the model complexity, material type, and materials used [10,18,22,24,25,27]. Conversely, commercial virtual reality platforms were significantly more expensive. For example, the LapSim simulator was reported to cost approximately $55,000 USD [12].

## 3.8. PARTICIPANTS AND EDUCATIONAL TARGETS
The average sample size across studies was 28 participants (median 23; IQR 11–42.5), and most studies stratified participants by experience level (medical students, residents, or expert urologists). Technologies designed for anatomical comprehension were primarily directed toward medical students, while simulation-based training tools were mostly evaluated among residents or practicing urologists.

### 3.8.1. SIMULATION

Ten studies evaluated the use of 3D technologies for surgical simulation in kidney cancer surgery. Among these, seven employed 3D-printed models [10,14–19] and three used virtual or augmented reality platforms [11–13]. Simulations included procedural training for laparoscopic and robot-assisted partial nephrectomy, as well as one study simulating intraoperative hemorrhage management using a perfused kidney model [10]. Outcomes commonly assessed included skill acquisition, model realism, and procedural familiarity.

### 3.8.2. ANATOMICAL COMPREHENSION

Three other studies focused on the use of 3D technologies to enhance anatomical comprehension [20,21,26]. These studies targeted primarily medical students or early-stage trainees and involved either patient-specific 3D-printed kidney models or virtual reality platforms. Reported outcomes included improvements in spatial orientation, tumor localization, and anatomical understanding compared to conventional imaging.

### 3.8.3. EVALUATION OF TRAINING POTENTIAL

Four studies evaluated the training potential of 3D technologies: one focused on an augmented reality platform [23], and three on 3D-printed kidneys [24–26]. In these studies, expert surgeons and residents jointly assessed the face and content validity to determine the training potential of these technologies. The questionnaires used for this purpose were qualitative, such as Likert-scale surveys, and focused on the realism of the phantoms and the usefulness of the simulations, as well as on more specific factors such as graphics quality, instrument handling, and tissue resistance.
<table><thead><tr><td>Author</td><td>Year</td><td>Title</td><td>Use</td><td>Procedure Type</td><td>Model Type</td><td>Personnalised</td><td>Cost</td><td>Number of Participants</td></tr></thead><tbody><tr><td>Michael Kheshvadjian et al.</td><td>2014</td><td>Implementation and Validation of a High-fidelity Simulation Model for Surgical Resident Training: Management of Acute Intraoperative Hemorrhage during Robot-assisted Surgery</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>No</td><td>$351.11</td><td>8 : residents</td></tr><tr><td>Margaret Knoedler et al.</td><td>2015</td><td>Individualized Physical 3-dimensional Kidney Tumor Models Constructed From 3-dimensional Printers Result in Improved Trainee Anatomic Understanding</td><td>Anathomy comprehension</td><td>Not specified</td><td>3D Printed</td><td>Yes</td><td>Not specified</td><td>23 : Medical students</td></tr><tr><td>Arun Rai et al.</td><td>2015</td><td>Patient-specific Virtual Simulation—A State of the Art Approach to Teach Renal Tumor Localization</td><td>Anathomy comprehension</td><td>RAPN</td><td>Virtual Reality</td><td>Yes (+ No)</td><td>Not specified</td><td>100 : Medical students</td></tr><tr><td>E. Reilly Scott et al.</td><td>2015</td><td>The use of individualized 3D-printed models on trainee and patient education, and surgical planning for robotic partial nephrectomies</td><td>Anathomy comprehension</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>$30–50 per model</td><td>10 : 6 expert urologists, 4 residents</td></tr><tr><td>Shinji Ohtake et al.</td><td>2015</td><td>Training on a virtual reality laparoscopic simulator improves performance of live laparoscopic surgery</td><td>Simulation</td><td>LN</td><td>Virtual Reality</td><td>No</td><td>Not specified</td><td>12 : residents</td></tr><tr><td>Amjad Alwaal et al.</td><td>2016</td><td>Transfer of skills on LapSim virtual reality laparoscopic simulator into the operating room in urology</td><td>Simulation</td><td>LN</td><td>Virtual Reality</td><td>No</td><td>$55,000 for setup</td><td>12 : residents</td></tr><tr><td>Andrew J. Hung et al.</td><td>2017</td><td>Development and Validation of a Novel Robotic Procedure Specific Simulation Platform: Partial Nephrectomy</td><td>Simulation</td><td>RAPN</td><td>Virtual Reality</td><td>No</td><td>Not specified</td><td>42 : 27 expert urologists, 15 residents</td></tr><tr><td>Steven M. Monda et al.</td><td>2018</td><td>Development and Validity of a Silicone Renal Tumor Model for Robotic Partial Nephrectomy Training</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>$3.90 per model</td><td>24 : 3 expert urologists, 17 residents, 4 medical students</td></tr><tr><td>Friedrich-Carl von Rundstedt et al.</td><td>2019</td><td>Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>Not specified</td><td>Not specified</td></tr><tr><td>Haruka Miyata et al.</td><td>2019</td><td>Validity assessment of the laparoscopic radical nephrectomy module of the LapVision virtual reality simulator</td><td>Evaluation of training potential</td><td>LN</td><td>Virtual Reality</td><td>No</td><td>Not specified</td><td>33 : 21 expert urologists, 1 resident, 11 medical students</td></tr><tr><td>Gonzalo Vitagliano et al.</td><td>2020</td><td>Construction of a 3D Surgical Model for Minimally Invasive Partial Nephrectomy: the Urotrainer VK-1</td><td>Simulation</td><td>LPN</td><td>3D Printed</td><td>No</td><td>Not specified</td><td>Not specified</td></tr></tbody></table>
<table><caption>Table 1: Description of the studies. Abbreviations: RAPN, robot-assisted partial nephrectomy; LN, laparoscopic nephrectomy; PCNL, percutaneous nephrolithotomy; LPN laparoscopic partial nephrectomy; PN partial nephrectomy</caption><tr><td>Yi Zhang et al.</td><td>2021</td><td>Evaluation of three-dimensional printing for laparoscopic partial nephrectomy of renal tumors: a preliminary report</td><td>Evaluation of training potential</td><td>LPN</td><td>3D Printed</td><td>Yes</td><td>$150 per model</td><td>4 : expert urologists</td></tr><tr><td>Ahmed Ghazi et al.</td><td>2021</td><td>Multi-institutional validation of a perfused robot-assisted partial nephrectomy procedural simulation platform utilizing clinically relevant objective metrics of simulators (CROMS)</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>$485 per model</td><td>43 : 16 expert urologists, 27 residents</td></tr><tr><td>Michael M. Maddox et al.</td><td>2022</td><td>3D-printed soft-tissue physical models of renal malignancies for individualized surgical simulation: a feasibility study</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>Not specified</td><td>Not specified</td></tr><tr><td>Hakmin Lee et al.</td><td>2023</td><td>Personalized 3D kidney model produced by rapid prototyping method and its usefulness in clinical applications</td><td>Evaluation of training potential</td><td>Not specified</td><td>3D Printed</td><td>Yes</td><td>$650 per model</td><td>15 : 2 expert urologists, 1 resident, 12 medical students</td></tr><tr><td>Ahmed Ghazi et al.</td><td>2023</td><td>The development &amp; implementation of a 3D printing perfused hydrogel Robotic Assisted Partial Nephrectomy Surgical Training Platform: Advancing from generic to patient specific simulation-based translational research Robotic Assisted Partial Nephrectomy Surgical Training Platform: Advancing from generic to patient specific simulation-based translational research</td><td>Simulation</td><td>RAPN</td><td>3D Printed</td><td>Yes</td><td>Not specified</td><td>43 : 16 experts urologists, 27 residents</td></tr><tr><td>Jonathan Silberstein et al.</td><td>2024</td><td>Physical Models of Renal Malignancies Using Standard Cross-sectional Imaging and 3-Dimensional Printers: A Pilot Study</td><td>Evaluation of training potential</td><td>RAPN / Open PN</td><td>3D Printed</td><td>Yes</td><td>Not specified</td><td>Not specified</td></tr></table>
4. DISCUSSION

This systematic review synthesizes current evidence on the application of 3D technologies (3D printing, virtual reality (VR), and augmented reality (AR)) in the education and training of urologists for renal surgery. Across the 17 included studies, 3D technologies were primarily used for surgical simulation, and to a lesser extent also for anatomical comprehension, validating anatomical accuracy of models, and skill assessment tool, reflecting their broad utility in modern surgical education. Importantly, the scope of this review is restricted to the educational role of these technologies. It does not assess their clinical utility, intraoperative decision-making, or perioperative outcomes. By maintaining this focus, the analysis aims to provide a clear understanding of how 3D-based tools contribute to surgical learning and skill acquisition among trainees, independent of their potential impact on patient care or expert surgical performance.

In the domain of surgical simulation, both physical and virtual models were employed to facilitate procedure rehearsal, technical skills acquisition, and emergency scenario training. Among the studies focusing on simulation, seven utilized 3D-printed models [10,14–19], while three implemented VR/AR platforms [11–13]. For instance, Vitagliano et al. developed a detailed kidney phantom with multiple tumors and cysts designed to progressively challenge trainees during simulated partial nephrectomy [16]. Simulation was not limited to routine procedures. Kheshvadjian et al. introduced a perfused kidney model to mimic intraoperative hemorrhage, offering rare training opportunities for critical events typically unteachable in clinical settings [10]. In VR-based studies, Ohtake et al. demonstrated improved efficiency scores after virtual training on porcine models, while Hung et al. validated a modular VR platform for RAPN, emphasizing its adaptability for stepwise skills development [11,13]. The idea behind the simulation, according to the authors, is that the use of these 3D technologies may result in a flattening of the early learning curve, especially in the training of resident surgeons.

Before reaching this conclusion, four studies focused on evaluating the training potential of 3D technologies, often analyzing original projects developed within their own centers, which therefore had not yet been used or validated. The evaluation was primarily qualitative, and both expert surgeons and trainees consistently validated the usefulness of these technologies during the learning process of RAPN. In particular, some authors have highlighted the educational potential for trainees of tactile interaction with a 3D model, within a surgical framework based on NSS, in which trainees come into contact with the tumor only after its removal.

Regarding anatomical comprehension, three studies targeted medical students or early trainees using 3D-printed or VR-based reconstructions to facilitate the understanding of renal anatomy and tumor localization. These tools consistently enhanced visuospatial interpretation skills compared to conventional imaging. For example, in a randomized trial involving 100 students, Rai et al. showed that access to VR models significantly improved tumor localization accuracy compared to CT alone [21].

Although 3D-printed kidney models and VR platforms offer clear educational benefits, their costs remain highly variable and may limit widespread implementation. Reported expenses range from less than $5 [14] per printed model to over $50,000 [12] for commercial simulators. Future advances in automated segmentation and low-cost biomimetic materials may help lower production costs and facilitate integration of these tools into standardized urological training programs.

Another consideration is the degree of personalization. While printed models were frequently patient-specific, VR and AR platforms often relied on generic anatomy. This discrepancy may impact the fidelity and transferability of training outcomes. The authors who investigated the anatomical
accuracy of their models subsequently validated them, reporting only discrepancies that were not
clinically significant [28,29]. This finding supports the use of personalized 3D-printed models when
the goal is to simulate and plan complex cases, rather than relying on generalized VR/AR systems.

Looking ahead, several future directions are worth emphasizing. The integration of artificial intelli-
gence (AI) could dramatically streamline and democratize the use of 3D technologies. AI algo-
rithms capable of automatic organ segmentation and tumor modeling from imaging data could ena-
ble rapid, low-cost generation of patient-specific models, opening the door to adaptive, individual-
ized training environments. These tools could also predict surgical difficulty or simulate complica-
tions based on anatomical complexity. In this way, the planning of complex cases on customized
models and the training of young urologists would progress along hand with the new technologies,
while consuming fewer financial and time resources.

On the other hand, there is still no standard regarding the molding processes or the materials used for 3D-printed models. Research plays therefore a crucial role in exploring biomimetic properties, as these directly affect the realism of the models and, consequently, the pedagogical effectiveness for trainees. However, most of these investigations are still conducted confidentially.

Beyond traditional simulation settings, 3D reconstruction and AR-based visualization tools also hold promise for remote education, particularly in tele-proctoring and tele-mentoring programs. These technologies can support shared surgical planning and interactive guidance between mentors and trainees located in different institutions, extending the reach of expert feedback and promoting standardized training across centers. Although not formally assessed in the studies included in this review, this represents a promising future direction for 3D-enhanced surgical education [30–32].

To our knowledge, this is the first systematic review to provide a dedicated synthesis of studies evaluating 3D technologies specifically for educational purposes in renal surgery training, complementing previous broader reviews that have addressed surgical education or 3D applications across other urological fields. Prior reviews on the topic have either addressed surgical education more broadly, focused on specific tools (e.g., VR or 3D printing), or included other uses of the 3D technologies rather than specifically kidney surgery or trainees educational outcomes. Notably, no head-to-head comparisons between 3D-printed models and VR/AR platforms were identified for renal surgery trainee education. Outside this context, comparative studies exist in the patient-education domain. For example, Wake et al. [33] compared technologies for patient understanding and decisional support. While informative, these findings target patient counseling rather than trainee performance and were therefore outside our inclusion criteria.

**5. CONCLUSION**

Three-dimensional technologies have shown clear educational benefits in renal surgery training. Across the included studies, these tools improved trainees’ anatomical comprehension, spatial awareness, and procedural confidence, and in some cases enhanced technical performance during simulated partial nephrectomy.

However, the available evidence remains limited by small, single-center studies, heterogeneous meth-
odologies, and reliance on subjective assessments. While 3D reconstruction has effectively helped
trainees visualize complex renal anatomy and practice key surgical steps, it has not yet demonstra-
ted consistent skill transfer or cost-effectiveness in real operating settings.
Overall, 3D technologies represent promising adjuncts to urological education, but their true pedagogical value will depend on rigorous validation, standardized evaluation metrics, and sustainable integration into structured training curricula.

## BIBLIOGRAPHY

[1] Bukavina L, Bensalah K, Bray F, Carlo M, Challacombe B, Karam JA, et al. Epidemiology of Renal Cell Carcinoma: 2022 Update. European Urology 2022;82:529-42.
https://doi.org/10.1016/j.eururo.2022.08.019.

[2] Tahbaz R, Schmid M, Merseburger AS. Prevention of kidney cancer incidence and recurrence: lifestyle, medication and nutrition. Curr Opin Urol 2018;28:62-79.
https://doi.org/10.1097/MOU.0000000000000454.

[3] Thorstenson A, Bergman M, Scherman-Plogell A-H, Hosseinnia S, Ljungberg B, Adolfsson J, et al. Tumour characteristics and surgical treatment of renal cell carcinoma in Sweden 2005-2010: a population-based study from the national Swedish kidney cancer register. Scand J Urol 2014;48:231-8.
https://doi.org/10.3109/21681805.2013.864698.

[4] Ljungberg B, Albiges L, Abu-Ghanem Y, Bedke J, Capitanio U, Dabestani S, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. European Urology 2022;82:399-410.
https://doi.org/10.1016/j.eururo.2022.03.006.

[5] Herrell SD, Smith JA. Robotic-assisted laparoscopic prostatectomy: what is the learning curve? Urology 2005;66:105-7.
https://doi.org/10.1016/j.urology.2005.06.084.

[6] Leung G, Pickett AT, Bartellas M, Milin A, Bromwich M, Shorr R, et al. Systematic review and meta-analysis of 3D-printing in otolaryngology education. Int J Pediatr Otorhinolaryngol 2022;155:111083.
https://doi.org/10.1016/j.ijporl.2022.111083.

[7] Maglara E, Angelis S, Solia E, Apostolopoulos AP, Tsakotos G, Vlasis K, et al. Three-Dimensional (3D) Printing in Orthopedics Education. J Long Term Eff Med Implants 2020;30:255-8.
https://doi.org/10.1615/JLongTermEffMedImplants.2020036911.

[8] Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T, Kumamaru KK, et al. Medical 3D Printing for the Radiologist. Radiographics 2015;35:1965-88.
https://doi.org/10.1148/rg.2015140320.

[9] Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71.
https://doi.org/10.1136/bmj.n71.

[10] Kheshvadjian M, Nazmifar M, Rawal R, Davood J, Castaneda P, Dadashian E, et al. Implementation and Validation of a High-fidelity Simulation Model for Surgical Resident Training: Management of Acute Intraoperative Hemorrhage during Robot-assisted Surgery. Urology 2024;191:185-92.
https://doi.org/10.1016/j.urology.2024.06.055.

[11] Ohtake S, Makiyama K, Yamashita D, Tatenuma T, Yao M. Training on a virtual reality laparoscopic simulator improves performance of live laparoscopic surgery. Asian J Endosc Surg 2022;15:313-9.
https://doi.org/10.1111/ases.13005.

[12] Alwaal A, Al-Qaoud TM, Haddad RL, Alzahrani TM, Delisle J, Anidjar M. Transfer of skills on LapSim virtual reality laparoscopic simulator into the operating room in urology. Urol Ann 2015;7:172-6.
https://doi.org/10.4103/0974-7796.150475.

[13] Hung AJ, Shah SH, Dalag L, Shin D, Gill IS. Development and Validation of a Novel Robotic Procedure Specific Simulation Platform: Partial Nephrectomy. J Urol 2015;194:520-6.
https://doi.org/10.1016/j.juro.2015.02.2949.

[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. Urology 2018;114:114-20.
https://doi.org/10.1016/j.urology.2018.01.030.
[15] von Rundstedt F-C, Scovell JM, Agrawal S, Zaneveld J, Link RE. Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy. BJU Int 2017;119:598-604.
https://doi.org/10.1111/bju.13712.

[16] Vitagliano G, Mey L, Rico L, Birkner S, Ringa M, Biancucci M. Construction of a 3D Surgical Model for Minimally Invasive Partial Nephrectomy: the Urotrainer VK-1. Curr Urol Rep 2021;22. https://doi.org/10.1007/s11934-021-01060-y.

[17] Ghazi A, Shepard L, Schuler N, Saba P, Joseph J. The development & implementation of a 3D printing perfused hydrogel Robotic Assisted Partial Nephrectomy Surgical Training Platform: Advancing from generic to patient specific simulation-based translational research. UroloVideo J 2023;17. https://doi.org/10.1016/j.urolvj.2022.100205.

[18] Ghazi A, Melnyk R, Hung AJ, Collins J, Ertefaie A, Saba P, et al. Multi-institutional validation of a perfused robot-assisted partial nephrectomy procedural simulation platform utilizing clinically relevant objective metrics of simulators (CROMS). BJU International 2021;127:645-53. https://doi.org/10.1111/bju.15246.

[19] 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 feasibility study. J Robotic Surg 2018;12:27-33. https://doi.org/10.1007/s11701-017-0680-6.

[20] Knoedler M, Feibus AH, Lange A, Maddox MM, Ledet E, Thomas R, et al. Individualized physical 3-dimensional kidney tumor models constructed from 3-dimensional printers result in improved trainee anatomic understanding. Urology 2015;85:1257-61.
https://doi.org/10.1016/j.urology.2015.02.053.

[21] Rai A, Scovell JM, Xu A, Balasubramanian A, Siller R, Kohn T, et al. Patient-specific Virtual Simulation—A State of the Art Approach to Teach Renal Tumor Localization. Urology 2018;120:42-8. https://doi.org/10.1016/j.urology.2018.04.043.

[22] Scott ER, Singh A, Quinn AM, Morano S, Karp A, Boyd K, et al. The use of individualized 3D-printed models on trainee and patient education, and surgical planning for robotic partial nephrectomies. J Rob Surg 2023;17:465-72. https://doi.org/10.1007/s11701-022-01441-6.

[23] Miyata H, Abe T, Hotta K, Higuchi M, Osawa T, Matsumoto R, et al. Validity assessment of the laparoscopic radical nephrectomy module of the LapVision virtual reality simulator. Surg Open Sci 2020;2:51-6. https://doi.org/10.1016/j.sopen.2019.08.003.

[24] Zhang Y, Ge H, Li N, Yu C, Guo H, Jin S, et al. Evaluation of three-dimensional printing for laparoscopic partial nephrectomy of renal tumors: a preliminary report. World J Urol 2016;34:533-7. https://doi.org/10.1007/s00345-015-1530-7.

[25] Lee H, Nguyen NH, Hwang SI, Lee HJ, Hong SK, Byun S-S. Personalized 3D kidney model produced by rapid prototyping method and its usefulness in clinical applications. Int Braz J Urol 2018;44:952-7. https://doi.org/10.1590/s1677-5538.ibju.2018.0162.

[26] Silberstein JL, Maddox MM, Dorsey P, Feibus A, Thomas R, Lee BR. Physical Models of Renal Malignancies Using Standard Cross-sectional Imaging and 3-Dimensional Printers: A Pilot Study. Urology 2014;84:268-73. https://doi.org/10.1016/j.urology.2014.03.042.

[27] Glybochko PV, Rapoport LM, Alyaev YG, Sirota ES, Bezrukov EA, Fiev DN, et al. Multiple application of three-dimensional soft kidney models with localized kidney cancer: A pilot study. Urologia 2018;85:99-105. https://doi.org/10.1177/0391560317749405.

[28] Melnyk R, Oppenheimer D, Ghazi AE. How specific are patient-specific simulations? Analyzing the accuracy of 3D-printing and modeling to create patient-specific rehearsals for complex urological procedures. World J Urol 2022;40:621-6. https://doi.org/10.1007/s00345-021-03797-0.

[29] Michiels C, Jambon E, Sarrazin J, Latxague C, De Hauteclocque AB, Capon G, et al. Anatomical accuracy of 3D printed patient-specific kidney models used for robot-assisted-partial nephrectomy pre-operative planning (UROCCR study n° 39: 3D-print). J Urol 2019;201:e1166-7. https://doi.org/10.1097/01.JU.0000557412.40302.ae.
[30] Checcucci E, Amparore D, Volpi G, De Cillis ST, Alessio P, Piramide F, et al. ROBOTIC PARTIAL NEPHRECTOMY WITH METAVERSE SURGICAL PLANNING. J Urol 2024;211:e198. https://doi.org/10.1097/01.JU.0001009444.59519.d3.09.

[31] Worlikar H, Coleman S, Kelly J, O'Connor S, Murray A, McVeigh T, et al. Mixed Reality Platforms in Telehealth Delivery: Scoping Review. JMIR Biomed Eng 2023;8:e42709. https://doi.org/10.2196/42709.

[32] Ayoub CH, El-Asmar JM, Abdulfattah S, El-Hajj A. Telemedicine and Telementoring in Urology: A Glimpse of the Past and a Leap Into the Future. Front Surg 2022;9:811749. https://doi.org/10.3389/fsurg.2022.811749.

[33] Wake N, Rosenkrantz AB, Huang R, Park KU, Wysock JS, Taneja SS, et al. Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education. 3D Print Med 2019;5:4. https://doi.org/10.1186/s41205-019-0041-3.