Robotic partial nephrectomy in 3D virtual reconstructions era: is the paradigm changed?

Visual extended reality tools in image-guided surgery in urology: a systematic review

PURPOSE

The aim of this systematic review is to assess the clinical implications of employing various Extended Reality (XR) tools for image guidance in urological surgery.

METHODS

In June 2023, a systematic electronic literature search was conducted using the Medline database (via PubMed), Embase (via Ovid), Scopus, and Web of Science. The search strategy was designed based on the PICO (Patients, Intervention, Comparison, Outcome) criteria. Study protocol was registered on PROSPERO (registry number CRD42023449025). We incorporated retrospective and prospective comparative studies, along with single-arm studies, which provided information on the use of XR, Mixed Reality (MR), Augmented Reality (AR), and Virtual Reality (VR) in urological surgical procedures. Studies that were not written in English, non-original investigations, and those involving experimental research on animals or cadavers were excluded from our analysis. The quality assessment of comparative and cohort studies was conducted utilizing the Newcastle-Ottawa scale, whilst for randomized controlled trials (RCTs), the Jadad scale was adopted. The level of evidence for each study was determined based on the guidelines provided by the Oxford Centre for Evidence-Based Medicine.

RESULTS

The initial electronic search yielded 1,803 papers after removing duplicates. Among these, 58 publications underwent a comprehensive review, leading to the inclusion of 40 studies that met the specified criteria for analysis. 11, 20 and 9 studies tested XR on prostate cancer, kidney cancer and miscellaneous, including bladder cancer and lithiasis surgeries, respectively. Focusing on the different technologies 20, 15 and 5 explored the potential of VR, AR and MR. The majority of the included studies (i.e., 22) were prospective non-randomized, whilst 7 and 11 were RCT and retrospective studies respectively. The included studies that revealed how these new tools can be useful both in preoperative and intraoperative setting for a tailored surgical approach.

CONCLUSIONS

AR, VR and MR techniques have emerged as highly effective new tools for image-guided surgery, especially for urologic oncology. Nevertheless, the complete clinical advantages of these innovations are still in the process of evaluation.

3D virtual model for robot-assisted partial nephrectomy in highly-complex cases (PADUA ⩾ 10)

PURPOSE

To compare two cohorts of patients submitted to robot-assisted partial nephrectomy (RAPN) for highly-complex renal masses (PADUA ⩾ 10) with versus without the use of 3DVMs.

MATERIALS AND METHODS

We screened a prospective consecutive cohort of 152 patients submitted to RAPN with 3DVM and 1264 patients submitted to RAPN without 3DVM between 2019 and 2022. Only PADUA ⩾ 10 cases were considered eligible for analysis. Propensity score matching (PSM) analysis was applied. Primary endpoint was to evaluate whereas RAPNs with 3DVM were superior in terms of functional outcomes at 12-month. Secondary outcomes were to compare perioperative and oncological outcomes. Multivariable logistic regression analyses (MVA) tested the associations of clinically significant eGFR drop and 3DVMs. Subgroups analysis was performed for PAUDA-risk categories.

RESULTS

Thirty seven patients for each group were analyzed after PSM. RAPN with 3DVM presented a higher rate of selective/no clamping procedure (32.5% vs 16.2%, p = 0.03) and a higher enucleation rate (43.2% vs 29.8%, p = 0.04). Twelve-month functional preservation performed better within 3DVM group in terms of creatinine serum level (median 1.2 [IQR 1.1-1.4] vs 1.6 [IQR 1.1-1.8], p = 0.03) and eGFR (median 64.6 [IQR 56.2-74.1] vs 52.3 [IQR 49.2-74.1], p = 0.03). MVA confirmed 3DVM as a protective factor for clinically significant eGFR drop in this subgroup of patients.

CONCLUSIONS

RAPN performed with the use of 3DVM assistance for PADUA ⩾ 10 cases resulted in lower incidence of global ischemia and higher rate of enucleations. The positive impact of such technology was found at 12-month follow-up.

Virtual Reality Head-Mounted Display (HMD) and Preoperative Patient-Specific Simulation: Impact on Decision-Making in Pediatric Urology: Preliminary Data

AIM OF THE STUDY

To assess how virtual reality (VR) patient-specific simulations can support decision-making processes and improve care in pediatric urology, ultimately improving patient outcomes.

PATIENTS AND METHODS

Children diagnosed with urological conditions necessitating complex procedures were retrospectively reviewed and enrolled in the study. Patient-specific VR simulations were developed with medical imaging specialists and VR technology experts. Routine CT images were utilized to create a VR environment using advanced software platforms. The accuracy and fidelity of the VR simulations was validated through a multi-step process. This involved comparing the virtual anatomical models to the original medical imaging data and conducting feedback sessions with pediatric urology experts to assess VR simulations' realism and clinical relevance.

RESULTS

A total of six pediatric patients were reviewed. The median age of the participants was 5.5 years (IQR: 3.5-8.5 years), with an equal distribution of males and females across both groups. A minimally invasive laparoscopic approach was performed for adrenal lesions (n = 3), Wilms' tumor (n = 1), bilateral nephroblastomatosis (n = 1), and abdominal trauma in complex vascular and renal malformation (ptotic and hypoplastic kidney) (n = 1). Key benefits included enhanced visualization of the segmental arteries and the deep vascularization of the kidney and adrenal glands in all cases. The high depth perception and precision in the orientation of the arteries and veins to the parenchyma changed the intraoperative decision-making process in five patients. Preoperative VR patient-specific simulation did not offer accuracy in studying the pelvic and calyceal anatomy.

CONCLUSIONS

VR patient-specific simulations represent an empowering tool in pediatric urology. By leveraging the immersive capabilities of VR technology, preoperative planning and intraoperative navigation can greatly impact surgical decision-making. As we continue to advance in medical simulation, VR holds promise in educational programs to include even surgical treatment of more complex urogenital malformations.

Three-dimensional virtual model for robot-assisted partial nephrectomy: a propensity-score matching analysis with a contemporary control group

PURPOSE

To compare two cohorts of patients submitted to robot-assisted partial nephrectomy (RAPN) with vs without the use of three-dimensional virtual models (3DVMs).

METHODS

We screened a prospective consecutive cohort of 152 patients submitted to RAPN with 3DVM and 1264 patients submitted to RAPN without 3DVM between 2019 and 2022. Propensity score matching analysis (PSMA) was applied. Primary endpoint was to evaluate whereas RAPNs with 3DVM were superior in terms of functional outcomes at 12-month. Secondary endopoints were to compare perioperative and oncological outcomes. Multivariable logistic regression analyses (MVA) tested the associations of clinically significant eGFR drop and 3DVMs. Subgroups analysis was performed for PAUDA-risk categories.

RESULTS

100 patients for each group were analyzed after PSMA. RAPN with 3DVM presented a higher rate of selective/no clamping procedure (32% vs 16%, p = 0.03) and a higher enucleation rate (40% vs 29%, p = 0.04). As concern to primary endopoint, 12-month functional preservation performed better within 3DVM group in terms of creatinine serum level (median 1.2 [IQR 1.1-1.4] vs 1.6 [IQR 1.1-1.8], p = 0.03) and eGFR (median 64.6 [IQR 56.2-74.1] vs 52.3 [IQR 49.2-74.1], p = 0.03). However, this result was confirmed only in the PADUA ≥ 10 renal masses. Regarding secondary endpoints, no significative difference emerged between the two cohorts. MVA confirmed 3DVM as a protective factor for clinically significant eGFR drop only in high-risk (PADUA ≥ 10) masses.

CONCLUSIONS

RAPN performed with the use of 3DVM assistance resulted in lower incidence of global ischemia and higher rate of enucleations. The positive impact of such technology was found at 12-month only in high-risk renal masses.

Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience

The aim of "Precision Surgery" is to reduce the impact of surgeries on patients' global health. In this context, over the last years, the use of three-dimensional virtual models (3DVMs) of organs has allowed for intraoperative guidance, showing hidden anatomical targets, thus limiting healthy-tissue dissections and subsequent damage during an operation. In order to provide an automatic 3DVM overlapping in the surgical field, we developed and tested a new software, called "ikidney", based on convolutional neural networks (CNNs). From January 2022 to April 2023, patients affected by organ-confined renal masses amenable to RAPN were enrolled. A bioengineer, a software developer, and a surgeon collaborated to create hyper-accurate 3D models for automatic 3D AR-guided RAPN, using CNNs. For each patient, demographic and clinical data were collected. A total of 13 patients were included in the present study. The average anchoring time was 11 (6-13) s. Unintended 3D-model automatic co-registration temporary failures happened in a static setting in one patient, while this happened in one patient in a dynamic setting. There was one failure; in this single case, an ultrasound drop-in probe was used to detect the neoplasm, and the surgery was performed under ultrasound guidance instead of AR guidance. No major intraoperative nor postoperative complications (i.e., Clavien Dindo > 2) were recorded. The employment of AI has unveiled several new scenarios in clinical practice, thanks to its ability to perform specific tasks autonomously. We employed CNNs for an automatic 3DVM overlapping during RAPN, thus improving the accuracy of the superimposition process.

Early introduction of simulation in the medical curriculum: the MedInTo perspective

Despite the increasing body of evidence supporting the use of simulation in medicine, a question remains: when should we introduce it into the medical school's curriculum? We present the experience and future perspectives of the MD program in Medicine and Surgery of University of Turin-MedInTo. Since its launch, MedInTo has been dedicated to integrating innovative teaching approaches at the early stages into the medical curriculum. Herewith, we describe a case-based approach for our activities, which includes the utilization of simulation for emergency medical care training for students and the integration of virtual and augmented reality technology. Dedicated surgical training activities using virtual-augmented reality and life-like simulator for students are also described.

Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy

OBJECTIVES

The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention.

INTRODUCTION

Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually.

METHODS

In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected.

RESULTS

Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3-11) seconds while in the case of the second technology 11 (6-13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered.

CONCLUSION

The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence.

3D Model Artificial Intelligence-Guided Automatic Augmented Reality Images during Robotic Partial Nephrectomy

More than ever, precision surgery is making its way into modern surgery for functional organ preservation. This is possible mainly due to the increasing number of technologies available, including 3D models, virtual reality, augmented reality, and artificial intelligence. Intraoperative surgical navigation represents an interesting application of these technologies, allowing to understand in detail the surgical anatomy, planning a patient-tailored approach. Automatic superimposition comes into this context to optimally perform surgery as accurately as possible. Through a dedicated software (the first version) called iKidney, it is possible to superimpose the images using 3D models and live endoscopic images during partial nephrectomy, targeting the renal mass only. The patient is 31 years old with a 28 mm totally endophytic right-sided renal mass, with a PADUA score of 9. Thanks to the automatic superimposition and selective clamping, an enucleoresection of the renal mass alone was performed with no major postoperative complication (i.e., Clavien-Dindo < 2). iKidney-guided partial nephrectomy is safe, feasible, and yields excellent results in terms of organ preservation and functional outcomes. Further validation studies are needed to improve the prototype software, particularly to improve the rotational axes and avoid human help. Furthermore, it is important to reduce the costs associated with these technologies to increase its use in smaller hospitals.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

Robotic versus Laparoscopic Partial Nephrectomy in the New Era: Systematic Review

(1) Background: In recent years there have been advances in imaging techniques, in addition to progress in the surgery of renal tumors directed towards minimally invasive techniques. Thus, nephron-sparing surgery has become the gold standard for the treatment of T1 renal masses. The aim of this study is to investigate the benefits of robotic partial nephrectomy in comparison with laparoscopic nephrectomy. (2) Methods: We performed a systematic review according to the PRISMA criteria during September 2022. We included clinical trials, and cohort and case-control studies published between 2000 and 2022. This comprised studies performed in adult patients with T1 renal cancer and studies comparing robotic with open and laparoscopic partial nephrectomy. A risk of bias assessment was performed according to the Newcastle—Ottawa scale. (3) Results: We observed lower hot ischemia times in the robotic surgery groups, although at the cost of an increase in total operative time, without appreciating the differences in terms of serious surgical complications (Clavien III–V). (4) Conclusions: Robotic partial nephrectomy is a safe procedure, with a shorter learning curve than laparoscopic surgery and with all the benefits of minimally invasive surgery.

Systematic Review of Comparative Studies of 3D Models for Preoperative Planning in Minimally Invasive Partial Nephrectomy

Background: The employment of 3-dimensional (3D) virtual models of the organs and tumors, obtained from conventional 2-dimensional (2D) imaging (i.e. computed tomography scan and magnetic resonance imaging) have already demonstrated an outstanding potential in urology, especially in renal surgery. Objectives: The aim of this systematic review is to provide an updated focus on the results obtained from the preoperative employment of 3D virtual imaging reconstructions in nephron sparing oncological surgery. Methods: A systematic literature search was conducted in April 2022 using Medline (via PubMed), Embase (via Ovid), Scopus, and Web of Science. The search strategy used PICO criteria and article selection was conducted in accordance with the PRISMA guidelines. The risk of bias and the quality of the articles included were assessed. A dedicated data extraction form was used to collect the data of interest. Results: The initial electronic search identified 471 papers, of which 13 ultimately met the inclusion criteria and were included in the review. 11 studies reported outcomes of virtual models, 2 studies focused on printed 3D models. In these studies, the application of 3D models for preoperative planning has been reported to increase the selective clamping rate and reducing the opening of collecting system, blood loss and loss of renal function. Conclusions: 3D virtual models seem to provide some surgical benefits for preoperative planning especially for complex renal masses. In the next future the continuous evolution of this technology may further increase its field of application and its potential clinical benefit.