Since the inception of the first robotic-assisted urologic surgery in 2001, the utilization in urologic oncology has significantly increased, offering enhanced precision and a minimally invasive approach. However, mastering the intricacies of robotic surgery requires surgeons to navigate a steep learning curve. Reports suggest a range of 10 to 250 cases for proficiency in robotic-assisted radical prostatectomy and up to 150 cases for robotic-assisted partial nephrectomy.¹ This large variation is attributed to the complexity of the surgery, patient factors, and the metrics used for assessment.

Efforts to mitigate the steep learning curve have been multifaceted. Structured training programs are imperative in urologic residency programs. These programs typically incorporate comprehensive didactic education, hands-on simulation, virtual reality–based simulators, and mentored clinical experiences.^2,3 Simulator training provides surgeons the opportunity to practice maneuvers, instrument control, and decision-making in a controlled setting prior to transitioning to live surgeries. Furthermore, mentorship from experienced robotic surgeons provides constructive guidance and insight. For more advanced trainees, the European Association of Urology Robotic Urology Section⁴ recommends a structured curriculum designed to facilitate proficiency in robot-assisted radical cystectomy with intracorporeal urinary diversion. This comprehensive 11-step curriculum, supervised by a mentor expert, has demonstrated remarkable efficacy in guiding trainees through the learning process. For surgeons in practice who are seeking to expand their skill set, there are also a number of existing courses both in-person and online to review techniques and approaches to complex robotic surgery. These tools not only enhance fundamental robotic skills and refine technique but also improve intraoperative clinical performance.³

Managing the learning curve effectively involves gradually introducing trainees to increasingly complex steps within a surgery. Commencing with simpler steps and cases, followed by repetition and positive reinforcement, enables trainees to acclimate themselves with the system’s capabilities and limitations, establishing a solid foundation. Furthermore, active teaching in the operating room and subsequent review of operative video with feedback from attendings are fundamental tools in refining robotic technique for trainees. As proficiency develops, surgeons can incrementally advance to more intricate robotic urologic surgeries. This methodical progression minimizes the risk of complications, while ensuring gradual refinement of skills and optimizing patient outcomes.

When advancing robotic surgery techniques, several factors must be considered. Not only is it important for surgeons to obtain technical skills of robotic surgery and gain proficiency, but it is also equally as important to ensure a patient-centered approach and prioritize patient safety. For example, during robotic-assisted radical prostatectomy, patients are positioned in steep Trendelenburg with upper extremities tucked on either side and often in lithotomy position. Given that operative times are longer during the learning stages, we highly recommend (if using the da Vinci Xi platform) adopting a supine position to avoid lithotomy due to the associated risk of lower extremity nerve damage or compartment syndrome, as well as limiting the time in Trendelenburg as much as possible. Attention to the nuances of careful patient positioning and vigilant monitoring throughout the duration of the procedure are important. During the surgeon’s learning curve (or for lengthy complex procedures), we recommend implementing a practice of briefly pausing the operation every 3 hours to perform a brief examination of pressure points, limbs, and other aspects of positioning that may be impacted. This will help to minimize potentially avoidable injuries that can be associated with prolonged cases.

Figure 1. Preoperative CT scan of a 7-cm endophytic interpolar mass of the left kidney in the transverse (A) and coronal (B) planes.

Technically challenging robotic surgeries pose inherent oncologic risks, including the potential for tumor seeding and incomplete resection. To mitigate these risks, it is imperative to adhere to oncologic principles utilizing gentle tissue handling and manipulation techniques to avoid dissemination of tumor cells during the surgery. Moreover, to decrease the risk of incomplete resection, meticulous preoperative planning and imaging play a pivotal role in identifying precise tumor margins and key anatomical structures aiding in complete resections. Intraoperatively, employing imaging modalities such as ultrasound or fluorescence-guided surgery assists in real-time tumor visualization, and most recently, 3D-augmented reality with intraoperative navigation⁵ can enhance resection while minimizing damage to surrounding healthy tissue. These advanced imaging tools are especially helpful during complex partial nephrectomies.

Figure 1 (preoperative) and Figure 2 (postoperative) illustrate an example of a complex partial nephrectomy that was performed at our institution using the da Vinci Xi. Multiple retrospective analyses have shown that robotic nephron-sparing techniques in cases of large renal masses can safely be performed with acceptable outcomes, either from oncologic or functional point of view.⁶ Nevertheless, there should be a risk-benefit assessment regarding the balance between the technical feasibility and patient’s individualized competing risk of morbidity and cancer-related events for decision-making. The surgeon’s experience and surgical learning curve play an important role in achieving trifecta in renal surgery: reducing postoperative complications, nonprolonged warm ischemia time, and absence of positive surgical margins.⁷

Figure 2. Postoperative CT scan following resection of the left renal mass, showing the preserved left kidney and absorbing hematoma around it.

Another example of a complex procedure is salvage robotic radical prostatectomy, which represents a potentially curative option for radio-recurrent prostate cancer but is associated with elevated morbidity. Bonet et al⁸ described the surgical learning curve in a single surgeon tertiary-referral center, with progressive reduction in operative time and rate of anastomotic leaks but no significant changes in biochemical failure rates or other postoperative complications. Salvage prostatectomy represents a challenging scenario due to radiation-induced scarring and desmoplastic reaction that causes altered tissue planes. Extensive experience in primary prostatectomy (at least 100 or more) is critical if undertaking a salvage procedure as familiarity with the anatomy and a high level of comfort with robotic pelvic surgery can ensure reasonably good outcomes.

In conclusion, the learning curve for robotic-assisted complex urologic tumor procedures is substantial but can be effectively addressed through structured training programs, simulation-based learning, and advancements in technology. Given the steep learning curve, strong consideration should be given to referring such complex cases to high-volume centers with vast experience in robotic assisted oncologic surgeries.