Augmented Reality Implementation in Minimally Invasive Surgery for Future Application in Pulmonary Surgery: A Systematic Review.

OBJECTIVE: This systematic review investigates of Augmented Reality (AR) systems used in minimally invasive surgery of deformable organs, focusing on initial registration, dynamic tracking, and visualization. The objective is to acquire a comprehensive understanding of the current knowledge, applications, and challenges associated with current AR-techniques, aiming to leverage these insights for developing a dedicated AR pulmonary Video or Robotic Assisted Thoracic Surgery (VATS/RATS) workflow. METHODS: A systematic search was conducted within Embase, Medline (Ovid) and Web of Science on April 16, 2024, following the Preferred Reporting items for Systematic Reviews and Meta-Analyses (PRISMA). The search focused on intraoperative AR applications and intraoperative navigational purposes for deformable organs. Quality assessment was performed and studies were categorized according to initial registration and dynamic tracking methods. RESULTS: 33 articles were included, of which one involved pulmonary surgery. Studies used both manual and (semi-) automatic registration methods, established through anatomical landmark-based, fiducial-based, or surface-based techniques. Diverse outcome measures were considered, including surgical outcomes and registration accuracy. The majority of studies that reached an registration accuracy below 5 mm applied surface-based registration. CONCLUSIONS: AR can potentially aid surgeons with real-time navigation and decision making during anatomically complex minimally invasive procedures. Future research for pulmonary applications should focus on exploring surface-based registration methods, considering their non-invasive, marker-less nature, and promising accuracy. Additionally, vascular-labeling-based methods are worth exploring, given the importance and relative stability of broncho-vascular anatomy in pulmonary VATS/RATS. Assessing clinical feasibility of these approaches is crucial, particularly concerning registration accuracy and potential impact on surgical outcomes.

Metaverse in surgery - origins and future potential.

The metaverse refers to a collective virtual space that combines physical and digital realities to create immersive, interactive environments. This space is powered by technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI) and blockchain. In healthcare, the metaverse can offer many applications. Specifically in surgery, potential uses of the metaverse include the possibility of conducting immersive surgical training in a VR or AR setting, and enhancing surgical planning through the adoption of three-dimensional virtual models and simulated procedures. At the intraoperative level, AR-guided surgery can assist the surgeon in real time to increase surgical precision in tumour identification and selective management of vessels. In post-operative care, potential uses of the metaverse include recovery monitoring and patient education. In urology, AR and VR have been widely explored in the past decade, mainly for surgical navigation in prostate and kidney cancer surgery, whereas only anecdotal metaverse experiences have been reported to date, specifically in partial nephrectomy. In the future, further integration of AI will improve the metaverse experience, potentially increasing the possibility of carrying out surgical navigation, data collection and virtual trials within the metaverse. However, challenges concerning data security and regulatory compliance must be addressed before the metaverse can be used to improve patient care.

Generative artificial intelligence in surgery.

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

First-in-human real-time AI-assisted instrument deocclusion during augmented reality robotic surgery.

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to the authors' best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal, and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics, and acceptance of AR in minimally invasive surgery.

Privacy-proof Live Surgery Streaming: Development and Validation of a Low-cost, Real-time Robotic Surgery Anonymization Algorithm.

OBJECTIVE: Develop a pioneer surgical anonymization algorithm for reliable and accurate real-time removal of out-of-body images validated across various robotic platforms. BACKGROUND: The use of surgical video data has become a common practice in enhancing research and training. Video sharing requires complete anonymization, which, in the case of endoscopic surgery, entails the removal of all nonsurgical video frames where the endoscope can record the patient or operating room staff. To date, no openly available algorithmic solution for surgical anonymization offers reliable real-time anonymization for video streaming, which is also robotic-platform and procedure-independent. METHODS: A data set of 63 surgical videos of 6 procedures performed on four robotic systems was annotated for out-of-body sequences. The resulting 496.828 images were used to develop a deep learning algorithm that automatically detected out-of-body frames. Our solution was subsequently benchmarked against existing anonymization methods. In addition, we offer a postprocessing step to enhance the performance and test a low-cost setup for real-time anonymization during live surgery streaming. RESULTS: Framewise anonymization yielded a receiver operating characteristic area under the curve score of 99.46% on unseen procedures, increasing to 99.89% after postprocessing. Our Robotic Anonymization Network outperforms previous state-of-the-art algorithms, even on unseen procedural types, despite the fact that alternative solutions are explicitly trained using these procedures. CONCLUSIONS: Our deep learning model, Robotic Anonymization Network, offers reliable, accurate, and safe real-time anonymization during complex and lengthy surgical procedures regardless of the robotic platform. The model can be used in real time for surgical live streaming and is openly available.

Robot-assisted Radical Prostatectomy Performed with Different Robotic Platforms: First Comparative Evidence Between Da Vinci and HUGO Robot-assisted Surgery Robots.

BACKGROUND: In the field of robotic surgery, there is a lack of comparative evidence on surgical and functional outcomes of different robotic platforms. OBJECTIVE: To assess the outcomes of patients receiving robot-assisted radical prostatectomy (RARP) at a high-volume robotic center with daVinci and HUGO robot-assisted surgery (RAS) surgical systems. DESIGN, SETTING, AND PARTICIPANTS: We analyzed the data of 542 patients undergoing RARP ± extended pelvic lymph node dissection at OLV hospital (Aalst, Belgium) between 2021 and 2023. All procedures were performed by six surgeons using daVinci or HUGO RAS robots; the use of one platform rather than the other did not follow any specific preference and/or indication. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariable analyses investigated the association between robotic system (daVinci vs HUGO RAS) and surgical outcomes after adjustment for patient- and tumor-related factors. Urinary continence recovery was defined as the use of no/one safety pad. RESULTS AND LIMITATIONS: A total of 378 (70%) and 164 (30%) patients underwent RARP with daVinci and HUGO RAS surgical systems, respectively. Despite a higher rate of palpable disease in the HUGO RAS group (34% vs 25%), baseline characteristics did not differ between the groups (all p > 0.05). After adjusting for confounders, we did not find evidence of a difference between the groups with respect to operative time (estimate: 16.71; 95% confidence interval [CI]: -6.35, 39.78; p = 0.12), estimated blood loss (estimate: 3.12; 95% CI: -67.03, 73.27; p = 0.9), and postoperative Clavien-Dindo ≥2 complications (odds ratio [OR]: 1.66; 95% CI: 0.34, 8.15; p = 0.5). On final pathology, 55 (15%) and 20 (12%) men in, respectively, the daVinci and the HUGO RAS group had positive surgical margins (PSMs; p = 0.5). On multivariable analyses, we did not find evidence of an association between a robotic system and PSMs (OR: 1.08; 95% CI: 0.56, 2.07; p = 0.8). Similarly, the odds of recovering continence did not differ between daVinci and HUGO RAS cases after both 1 mo (OR: 0.78; 95% CI: 0.45, 1.38; p = 0.4) and 3 mo (OR: 1.17; 95% CI: 0.49, 2.79; p = 0.7). CONCLUSIONS: Among patients receiving RARP with daVinci or HUGO RAS surgical platforms, we did not find differences in surgical and functional outcomes between the robots. This may be a result of a standardized surgical technique that allowed surgeons to transfer their skills between robotic systems. Awaiting future investigations with longer follow-up, these results have important implications for patients, surgeons, and health care policymakers. PATIENT SUMMARY: We compared surgical and functional outcomes of patients receiving robot-assisted radical prostatectomy with daVinci versus HUGO robot-assisted surgery (RAS) robots. The two platforms were able to achieve similar outcomes, suggesting that the introduction of HUGO RAS is safe and allows for optimal outcomes after radical prostatectomy.

Robot-assisted adrenalectomy: Step-by-step technique and surgical outcomes at a high-volume robotic center.

Objective: In the last years, robotic surgery was introduced in several different settings with good perioperative results. However, its role in the management of adrenal masses is still debated. In order to provide a contribution to this field, we described our step-by-step technique for robotic adrenalectomy (RA) and related modifications according to the type of adrenal mass treated. Methods: We retrospectively analyzed 27 consecutive patients who underwent RA at Onze-Lieve-Vrouw hospital (Aalst, Belgium) between January 2009 and October 2022. Demographic, intra- and post-operative, and pathological data were retrieved from our prospectively maintained institutional database. Continuous variables are summarized as median and interquartile range (IQR). Categorical variables are reported as frequencies (percentages). Results: Twenty-seven patients underwent RA were included in the study. Median age, body mass index, and Charlson's comorbidity index were 61 (IQR: 49-71) years, 26 (IQR: 24-29) kg/m2, and 2 (IQR: 0-3), respectively, and 16 (59.3%) patients were male. Median tumor size at computed tomography scan was 6.0 (IQR: 3.5-8.0) cm. Median operative time and blood loss were 105 (IQR: 82-120) min and 175 (IQR: 94-250) mL, respectively. No intraoperative complications were recorded. Overall postoperative complications rate was 11.1%, with a postoperative transfusion rate of 3.7%. A total of 10 (37.0%) patients harbored malignant adrenal masses. Among them, 3 (11.1%) had adrenocortical carcinoma, 6 (22.2%) secondary metastasis, and 1 (3.7%) malignant pheochromocytoma on final pathological exam. Only 1 (10.0%) patient had positive surgical margins. Conclusion: We described our step-by-step technique for RA, which can be safely performed even in case of high challenging settings as malignant tumors, pheochromocytoma, and large masses. The standardization of perioperative protocol should be encouraged to maximize the outcomes of this complex surgical procedure.

Transitioning from Da Vinci Si to Xi: assessing surgical outcomes at a high-volume robotic center.

PURPOSE: In the emerging field of robotics, only few studies investigated the transition between different robotic platforms in terms of surgical outcomes. We aimed at assessing surgical outcomes of patients receiving robot-assisted radical prostatectomy (RARP) and robot-assisted partial nephrectomy (RAPN) at a high-volume robotic center during the transition from Si to Xi Da Vinci surgical systems. METHODS: We analyzed data of 1884 patients undergoing RARP (n = 1437, 76%) and RAPN (n = 447, 24%) at OLV hospital (Aalst, Belgium) between 2011 and 2021. For both procedures, we assessed operative time, estimated blood loss, length of stay, and positive surgical margins. For RARP, we investigated length of catheterization and PSA persistence after surgery, whereas warm ischemia time, clampless surgery, and acute kidney injury (AKI) were assessed for RAPN. Multivariable analyses (MVA) investigated the association between robotic platform (Si vs. Xi) and surgical outcomes after adjustment for patient- and tumor-related factors. RESULTS: A total of 975 (68%) and 462 (32%) patients underwent RARP performed with the Si vs. Xi surgical system, respectively. Baseline characteristics did not differ between the groups. On MVA, we did not find evidence of a difference between the groups with respect to operative time (estimate: 1.07) or estimated blood loss (estimate: 32.39; both p > 0.05). Median (interquartile range [IQR]) length of stay was 6 (3, 6) and 4 (3, 5) days in the Si vs. Xi group, respectively (p < 0.0001). On MVA, men treated with the Xi vs. Si robot had lower odds of PSM (Odds ratio [OR]: 0.58; p = 0.014). A total of 184 (41%) and 263 (59%) patients received RAPN with the Si and Xi robotic system, respectively. Baseline characteristics, including demographics, functional data, and tumor-related features did not differ between the groups. On MVA, operative time was longer in the Xi vs. Si group (estimate: 30.54; p = 0.006). Patients treated with the Xi vs. Si system had higher probability of undergoing a clampless procedure (OR: 2.56; p = 0.001), whereas the risk of AKI did not differ between the groups (OR: 1.25; p = 0.4). On MVA, patients operated with the Xi robot had shorter length of stay as compared to the Si group (estimate: - 0.86; p = 0.003), whereas we did not find evidence of an association between robotic system and PSM (OR: 1.55; p = 0.3). CONCLUSION: We found that the Xi robot allowed for improvements in peri-operative outcomes as compared to the Si platform, with lower rate of positive margins for RARP and higher rate of off-clamp procedures for RAPN. Hospital stay was also shorter for patients operated with the Xi vs. Si robot, especially after robot-assisted partial nephrectomy. Awaiting future investigations-in particular, cost analyses-these results have important implications for patients, surgeons, and healthcare policymakers.

Can the Abdominal Aortic Atherosclerotic Plaque Index Predict Functional Outcomes after Robot-Assisted Partial Nephrectomy?

This study aims to evaluate the abdominal aortic atherosclerotic plaque index (API)'s predictive role in patients with pre-operatively or post-operatively developed chronic kidney disease (CKD) treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC). One hundred and eighty-three patients (134 with no pre- and post-operative CKD (no CKD) and 49 with persistent or post-operative CKD development (post-op CKD)) who underwent RAPN between January 2019 and January 2022 were deemed eligible for the analysis. The API was calculated using dedicated software by assessing the ratio between the CT scan atherosclerotic plaque volume and the abdominal aortic volume. The ROC regression model demonstrated the influence of API on CKD development, with an increasing effect according to its value (coefficient 0.13; 95% CI 0.04-0.23; p = 0.006). The Model 1 multivariable analysis of the predictors of post-op CKD found that the following are independently associated with post-op CKD: Charlson Comorbidity Index (OR 1.31; p = 0.01), last follow-up (FU) Δ%eGFR (OR 0.95; p < 0.01), and API ≥ 10 (OR 25.4; p = 0.01). Model 2 showed API ≥ 10 as the only factor associated with CKD development (OR 25.2; p = 0.04). The median follow-up was 22 months. Our results demonstrate API to be a strong predictor of post-operative CKD, allowing the surgeon to tailor the best treatment for each patient, especially in those who might be at higher risk of CKD.

Surgical Phase Duration in Robot-Assisted Partial Nephrectomy: A Surgical Data Science Exploration for Clinical Relevance.

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback, and teaching. The phase duration itself and its correlation with clinical parameters at diagnosis have not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assessed 100 robot-assisted partial nephrectomy videos for phase durations based on previously defined proficiency metrics. We developed an annotation framework and subsequently compared our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explored clinical correlations between phase durations and parameters derived from diagnosis and treatment. (3) Results: An objective and uniform phase assessment requires precise definitions derived from an iterative revision process. A comparison to a commercial solution shows large differences in definitions across phases. BMI and the duration of renal tumor identification are positively correlated, as are tumor complexity and both tumor excision and renorrhaphy duration. (4) Conclusions: The surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate whether the retrieved correlations are also clinically meaningful. This requires an increase in dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided that the phase annotation details are disclosed.

Robot-assisted Partial Nephrectomy Using Intra-arterial Renal Hypothermia for Highly Complex Endophytic or Hilar Tumors: Case Series and Description of Surgical Technique.

Background: In partial nephrectomy for highly complex tumors with expected long ischemia time, renal hypothermia can be used to minimize ischemic parenchymal damage. Objective: To describe our case series, surgical technique, and early outcomes for robot-assisted partial nephrectomy (RAPN) using intra-arterial cold perfusion through arteriotomy. Design setting and participants: A retrospective analysis was conducted of ten patients with renal tumors (PADUA score 9-13) undergoing RAPN between March 2020 and March 2023 with intra-arterial cooling because of expected arterial clamping times longer than 25 min. Surgical procedure: Multiport transperitoneal RAPN with full renal mobilization and arterial, venous, and ureteral clamping was performed. After arteriotomy and venotomy, 4°C heparinized saline is administered intravascular through a Fogarty catheter to maintain renal hypothermia while performing RAPN. Measurements: Demographic data, renal function, console and ischemia times, surgical margin status, hospital stay, estimated blood loss, and complications were analyzed. Results and limitations: The median warm and cold ischemia times were 4 min (interquartile range [IQR] 3-7 min) and 60 min (IQR 33-75 min), respectively. The median rewarming ischemia time was 10.5 min (IQR 6.5-23.75 min). The median pre- and postoperative estimated glomerular filtration rate values at least 1 mo after surgery were 90 ml/min (IQR 78.35-90 ml/min) and 86.9 ml/min (IQR 62.08-90 ml/min), respectively. Limitations include small cohort size and short median follow-up (13 [IQR 9.1-32.4] mo). Conclusions: We demonstrate the feasibility and first case series for RAPN using intra-arterial renal hypothermia through arteriotomy. This approach broadens the scope for minimal invasive nephron-sparing surgery in highly complex renal masses. Patient summary: We demonstrate a minimally invasive surgical technique that reduces kidney infarction during complex kidney tumor removal where surrounding healthy kidney tissue is spared. The technique entails arterial cold fluid irrigation, which temporarily decreases renal metabolism and allows more kidneys to be salvaged.

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.

Different approaches for bladder neck dissection during robot-assisted radical prostatectomy: the Aalst technique.

INTRODUCTION: Bladder neck dissection is one of the most delicate surgical steps of robotic-assisted radical prostatectomy (RARP) [1, 2], and it may affect surgical margins rate and functional outcomes [3, 4]. Given the relationship between outcomes and surgical experience [5-7], it is crucial to implement a step-by-step approach for each surgical step of the procedure, especially in the most challenging part of the intervention. In this video compilation, we described the techniques for bladder neck dissection utilized at OLV Hospital (Aalst, Belgium). SURGICAL TECHNIQUE: We illustrated five different techniques for bladder neck dissection during RARP. The anterior technique tackles the bladder neck from above until the urethral catheter is visualized, and then the dissection is completed posteriorly. The lateral and postero-lateral approaches involve the identification of a weakness point at the prostate-vesical junction and aim to develop the posterior plane - virtually until the seminal vesicles - prior to the opening of the urethra anteriorly. Finally, we described our techniques for bladder neck dissection in more challenging cases such as in patients with bulky middle lobes and prior surgery for benign prostatic hyperplasia. All approaches follow anatomic landmarks to minimize positive surgical margins and aim to preserve the bladder neck in order to promote optimal functional recovery. All procedures were performed with DaVinci robotic platforms using a 3-instruments configuration (scissors, fenestrated bipolar, and needle driver). As standard protocol at our Institution, urinary catheter was removed on postoperative day two [8]. CONCLUSIONS: Five different approaches for bladder neck dissection during RARP were described in this video compilation. We believe that the technical details provided here might be of help for clinicians who are starting their practice with this surgical intervention.

Improving Augmented Reality Through Deep Learning: Real-time Instrument Delineation in Robotic Renal Surgery.

Several barriers prevent the integration and adoption of augmented reality (AR) in robotic renal surgery despite the increased availability of virtual three-dimensional (3D) models. Apart from correct model alignment and deformation, not all instruments are clearly visible in AR. Superimposition of a 3D model on top of the surgical stream, including the instruments, can result in a potentially hazardous surgical situation. We demonstrate real-time instrument detection during AR-guided robot-assisted partial nephrectomy and show the generalization of our algorithm to AR-guided robot-assisted kidney transplantation. We developed an algorithm using deep learning networks to detect all nonorganic items. This algorithm learned to extract this information for 65 927 manually labeled instruments on 15 100 frames. Our setup, which runs on a standalone laptop, was deployed in three different hospitals and used by four different surgeons. Instrument detection is a simple and feasible way to enhance the safety of AR-guided surgery. Future investigations should strive to optimize efficient video processing to minimize the 0.5-s delay currently experienced. General AR applications also need further optimization, including detection and tracking of organ deformation, for full clinical implementation.

A Novel Three-dimensional Planning Tool for Selective Clamping During Partial Nephrectomy: Validation of a Perfusion Zone Algorithm.

BACKGROUND: Selective clamping during robot-assisted partial nephrectomy (RAPN) requires extensive knowledge on patient-specific renal vasculature, obtained through imaging. OBJECTIVE: To validate an in-house developed perfusion zone algorithm that provides patient-specific three-dimensional (3D) renal perfusion information. DESIGN, SETTING, AND PARTICIPANTS: Between October 2020 and June 2022, 25 patients undergoing RAPN at Ghent University Hospital were included. Three-dimensional models, based on preoperative computed tomography (CT) scans, showed the clamped artery's ischemic zone, as calculated by the algorithm. SURGICAL PROCEDURE: All patients underwent selective clamping during RAPN. Indocyanine green (ICG) was administered to visualize the true ischemic zone perioperatively. Surgery was recorded for a postoperative analysis. MEASUREMENTS: The true ischemic zone of the clamped artery was compared with the ischemic zone predicted by the algorithm through two metrics: (1) total ischemic zone overlap and (2) tumor ischemic zone overlap. Six urologists assessed metric 1; metric 2 was assessed objectively by the authors. RESULTS AND LIMITATIONS: In 92% of the cases, the algorithm was sufficiently accurate to plan a selective clamping strategy. Metric 1 showed an average score of 4.28 out of 5. Metric 2 showed an average score of 4.14 out of 5. A first limitation is that ICG can be evaluated only at the kidney surface. A second limitation is that mainly patients with impaired renal function are expected to benefit from this technology, but contrast-enhanced CT is required at present. CONCLUSIONS: The proposed new tool demonstrated high accuracy when planning selective clamping for RAPN. A follow-up prospective study is needed to determine the tool's clinical added value. PATIENT SUMMARY: In partial nephrectomy, the surgeon has no information on which specific arterial branches perfuse the kidney tumor. We developed a surgeon support system that visualizes the perfusion zones of all arteries on a three-dimensional model and indicates the correct arteries to clamp. In this study, we validate this tool.

Techniques and outcomes of robot-assisted partial nephrectomy for the treatment of multiple ipsilateral renal masses.

BACKGROUND: Patients with multiple ipsilateral renal masses have an augmented risk of metachronous contralateral lesions and are likely to undergo repeated surgeries. We report our experience with the technologies currently available and the surgical techniques to preserve healthy parenchyma while guaranteeing oncological radicality during robot-assisted partial nephrectomy (RAPN). METHODS: The data were collected at three tertiary-care centers, where 61 patients with multiple ipsilateral renal masses were treated with RAPN between 2012 and 2021. RAPN was performed with da Vinci Si or Xi surgical system using TilePro (Life360; San Francisco, CA, USA), indocyanine green fluorescence and intraoperative ultrasound. Three-dimensional reconstructions were built in some cases preoperatively. Different techniques were employed for hilum management. The primary endpoint is to report intra- and postoperative complications. Secondary endpoints were the estimated blood loss (EBL), warm ischemia time (WIT) and positive surgical margins (PSM) rate. RESULTS: Median preoperative size of the largest mass was 37.5 mm (24-51) with a median PADUA and R.E.N.A.L. score of 8 (7-9) and 7 (6-9). One hundred forty-two tumors were excised, with a mean number of 2.32. The median WIT was 17 (12-24) minutes, and the median EBL was 200 (100-400) mL. Intraoperative ultrasound was employed in 40 (67.8%) patients. The rate of early unclamping, selective clamping and zero-ischemia were respectively 13 (21.3%), 6 (9.8%) and 13 (21.3%). ICG fluorescence was employed in 21 (34.42%) patients and three-dimensional reconstructions were built in 7 (11.47%) patients. Three (4.8%) intraoperative complications occurred, all classified as grade-1 according to EAUiaiC. Postoperative complications were reported in 14 (22.9%) cases with 2 Clavien-Dindo grade >2 complications. Four (6.56%) patients had PSM. Mean period of follow-up was 21 months. CONCLUSIONS: In experienced hands, with the employment of the currently available technologies and surgical techniques, RAPN can guarantee optimal outcomes in patients with multiple ipsilateral renal masses.