New imaging technologies for robotic kidney cancer surgery

Biophotonics-Intraoperative Guidance During Partial Nephrectomy: A Systematic Review and Meta-analysis

CONTEXT

Partial nephrectomy (PN) with intraoperative guidance by biophotonics has the potential to improve surgical outcomes due to higher precision. However, its value remains unclear since high-level evidence is lacking.

OBJECTIVE

To provide a comprehensive analysis of biophotonic techniques used for intraoperative real-time assistance during PN.

EVIDENCE ACQUISITION

We performed a comprehensive database search based on the PICO criteria, including studies published before October 2022. Two independent reviewers screened the titles and abstracts followed by full-text screening of eligible studies. For a quantitative analysis, a meta-analysis was conducted.

EVIDENCE SYNTHESIS

In total, 35 studies were identified for the qualitative analysis, including 27 studies on near-infrared fluorescence (NIRF) imaging using indocyanine green, four studies on hyperspectral imaging, two studies on folate-targeted molecular imaging, and one study each on optical coherence tomography and 5-aminolevulinic acid. The meta-analysis investigated seven studies on selective arterial clamping using NIRF. There was a significantly shorter warm ischemia time in the NIRF-PN group (mean difference [MD]: -2.9; 95% confidence interval [CI]: -5.6, -0.1; p = 0.04). No differences were noted regarding transfusions (odds ratio [OR]: 0.5; 95% CI: 0.2, 1.7; p = 0.27), positive surgical margins (OR: 0.7; 95% CI: 0.2, 2.0; p = 0.46), or major complications (OR: 0.4; 95% CI: 0.1, 1.2; p = 0.08). In the NIRF-PN group, functional results were favorable at short-term follow-up (MD of glomerular filtration rate decline: 7.6; 95% CI: 4.6, 10.5; p < 0.01), but leveled off at long-term follow-up (MD: 7.0; 95% CI: -2.8, 16.9; p = 0.16). Remarkably, these findings were not confirmed by the included randomized controlled trial.

CONCLUSIONS

Biophotonics comprises a heterogeneous group of imaging modalities that serve intraoperative decision-making and guidance. Implementation into clinical practice and cost effectiveness are the limitations that should be addressed by future research.

PATIENT SUMMARY

We reviewed the application of biophotonics during partial removal of the kidney in patients with kidney cancer. Our results suggest that these techniques support the surgeon in successfully performing the challenging steps of the procedure.

A systematic review of robot-assisted partial nephrectomy outcomes for advanced indications: Large tumors (cT2-T3), solitary kidney, completely endophytic, hilar, recurrent, and multiple renal tumors

Objective

Robot-assisted partial nephrectomy (RAPN) has become widely used for treatment of renal cell carcinoma and it is expanding in the field of complex renal masses. The aim of this systematic review was to analyze outcomes of RAPN for completely endophytic renal masses, large tumors (cT2-T3), renal cell carcinoma in solitary kidney, recurrent tumors, completely endophytic and hilar masses, and simultaneous and multiple tumors.

Methods

A comprehensive search in the PubMed, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials databases was performed in December 2022 for English language papers. The primary endpoint was to evaluate the role of RAPN in the setting of each category of complex renal masses considered. The secondary endpoint was to evaluate the surgical and functional outcomes.

Results

After screening 1250 records, 43 full-text manuscripts were selected, comprising over 8500 patients. Twelve and thirteen studies reported data for endophytic and hilar renal masses, respectively. Five and three studies reported outcomes for cT2-T3 and solitary kidney patients, respectively. Four studies focused on redo-RAPN for recurrent tumors. Two studies investigated simultaneous bilateral renal masses and five reports focused on multiple tumor excision in ipsilateral kidney.

Conclusion

Over the past decade, evidence supporting the use of RAPN for the most challenging nephron-sparing surgery indications has continuously grown. Although limitations remain including study design and lack of detailed long-term functional and oncological outcomes, the adoption of RAPN for the included advanced indications is associated with favorable surgical outcomes with good preservation of renal function without compromising the oncological result. Certainly, a higher likelihood of complication might be expected when facing extremely challenging cases. However, none of these indications should be considered per se an exclusion criterion for performing RAPN. Ultimately, a risk-adapted approach should be employed.

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.

Three-Dimensional Customized Imaging Reconstruction for Urological Surgery: Diffusion and Role in Real-Life Practice from an International Survey

Despite the arising interest in three-dimensional (3D) reconstruction models from 2D imaging, their diffusion and perception among urologists have been scarcely explored. The aim of the study is to report the results of an international survey investigating the use of such tools among urologists of different backgrounds and origins. Beyond demographics, the survey explored the degree to which 3D models are perceived to improve surgical outcomes, the procedures mostly making use of them, the settings in which those tools are mostly applied, the surgical steps benefiting from 3D reconstructions and future perspectives of improvement. One hundred responders fully completed the survey. All levels of expertise were allowed; more than half (53%) were first surgeons, and 59% had already completed their training. Their main application was partial nephrectomy (85%), followed by radical nephrectomy and radical prostatectomy. Three-dimensional models are mostly used for preoperative planning (75%), intraoperative consultation and tailoring. More than half recognized that 3D models may highly improve surgical outcomes. Despite their recognized usefulness, 77% of responders use 3D models in less than 25% of their major operations due to costs or the extra time taken to perform the reconstruction. Technical improvements and a higher availability of the 3D models will further increase their role in surgical and clinical daily practice.

Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications

Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D^®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D^® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D^® models' reconstruction.

Health Information Technology Usability Evaluation Scale (Health-ITUES) and User-Experience Questionnaire (UEQ) for 3D Intraoperative Cognitive Navigation (ICON3DTM) System for Urological Procedures

Backgound and objectives: In recent years, the adoption of 3D models for surgical planning and intraoperative guidance has gained a wide diffusion. The aim of this study was to evaluate the surgeons' perception and usability of ICON3D^TM platform for robotic and laparoscopic urological surgical procedures. Materials and Methods: During the 10th edition of the Techno-Urology Meeting, surgeons and attendees had the opportunity to test the new ICON3D^TM platform. The capability of the user to manipulate the model with hands/mouse, the software usability, the quality of the 3D model's reproduction, and the quality of its use during the surgery were evaluated with the Health Information Technology Usability Evaluation Scale (Health-ITUES) and the User-Experience Questionnaire (UEQ). Results: Fifty-three participants responded to the questionnaires. Based on the answers to the Health-ITUES questionnaire, ICON3D^TM resulted to have a positive additional value in presurgical/surgical planning with 43.4% and 39.6% of responders that rated 4 (agree) and 5 (strongly agree), respectively. Regarding the UEQ questionnaire, both mouse and infrared hand-tracking system resulted to be easy to use for 99% of the responders, while the software resulted to be easy to use for 93.4% of the responders. Conclusions: In conclusion, ICON3D^TM has been widely appreciated by urologists thanks to its various applications, from preoperative planning to its support for intraoperative decision-making in both robot-assisted and laparoscopic settings.

Artificial Intelligence in Surgical Learning

(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.

Superior Mesenteric Artery Injury during Robotic Radical Nephrectomy: Scenarios and Management Strategies

Injury to the superior mesenteric artery (SMA) is a rare, underreported, and potentially devastating complication. This study aims to propose a systematic workup to describe how to prevent and manage SMA injury in a standardized stepwise manner. Three different instances of intraoperative injury to the SMA are described in an accompanying video. All three occurred when the SMA was misidentified as the left renal artery during left robotic radical nephrectomy. In the first case, the SMA was mistakenly identified as the renal artery, but after further dissection, the real renal artery was identified and SMA injury was prevented. In the second case, the SMA was clipped and the real left renal artery was subsequently identified, requiring clip removal. In the third case, the SMA was clipped and completely transected, requiring prompt repair by vascular surgery with a successful outcome. This study aims to propose a systematic workup to describe how to prevent and manage SMA injury in a standardized stepwise manner. The proper anatomic recognition of the SMA may prevent its injury. Intraoperative SMA injury should be promptly identified and repaired to avoid its devastating consequences.