From open radical hysterectomy to robot-assisted laparoscopic radical hysterectomy for early stage cervical cancer: aspects of a single institution learning curve
- First Online:
- Cite this article as:
- Schreuder, H.W.R., Zweemer, R.P., van Baal, W.M. et al. Gynecol Surg (2010) 7: 253. doi:10.1007/s10397-010-0572-5
We analysed the introduction of the robot-assisted laparoscopic radical hysterectomy in patients with early-stage cervical cancer with respect to patient benefits and surgeon-related aspects of a surgical learning curve. A retrospective review of the first 14 robot-assisted laparoscopic radical hysterectomies and the last 14 open radical hysterectomies in a similar clinical setting with the same surgical team was conducted. Patients were candidates for a laparoscopic sentinel node procedure, pelvic lymph node dissection and open radical hysterectomy (RH) before August 2006 and were candidates for a laparoscopic sentinel node procedure, pelvic lymph node dissection and robot-assisted laparoscopic radical hysterectomy (RALRH) after August 2006. Overall, blood loss in the open cases was significantly more compared with the robot cases. Median hospital stay after RALRH was 5 days less than after RH. The median theatre time in the learning period for the robot procedure was reduced from 9 h to less that 4 h and compared well to the 3 h and 45 min for an open procedure. Three complications occurred in the open group and one in the robot group. RALRH is feasible and of benefit to the patient with early stage cervical cancer by a reduction of blood loss and reduced hospital stay. Introduction of this new technique requires a learning curve of less than 15 cases that will reduce the operating time to a level comparable to open surgery.
KeywordsRobotic surgery da Vinci Radical hysterectomy Learning curve
For the surgical treatment of stage Ib1 cervical cancer, open radical hysterectomy with pelvic lymph node dissection has been the gold standard for over 100 years and has undergone little modification since it was first described by Wertheim  and later by Meigs . Techniques for laparoscopic radical hysterectomy and lymph node dissection were developed in the early 1990s [3, 4] and considerable experience has been gained since with over 1,000 cases reported in literature .
After initial experience with robotic systems that are no longer available, the introduction of the da Vinci Surgical Robotic system (Intuitive Surgery, Mountain View, Ca, USA) and subsequent FDA approval for gynaecological use in 2005 , has made the laparoscopic approach to complex radical gynaecologic operations more feasible. The robotic system has an advantage over the traditional laparoscopic approach regarding improved articulation of instruments, stereoscopic vision, tremor reduction and motion downscaling . These features suggest that a shorter learning curve may be achieved compared to conventional laparoscopy . Robot-assisted laparoscopic surgery is used in gynaecology for benign hysterectomy, myomectomy, tubal reanastomoses, radical surgery, lymph node dissections and sacrocolpopexias [9, 10]. Robot-assisted laparoscopic radical hysterectomy (RALRH) for cervical cancer has been recently introduced. A total of 313 cases in 11 reports have now been published [11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. These early reports describe operation time, blood loss, hospital stay, lymph node count and complications, but the aspect of the learning curve is not well described. One report describes the aspect of the learning curve more in detail . In the present study, we compare 14 open radical hysterectomies (RH) with 14 RALRHs including sentinel node detection. We analysed patient-related aspects (blood loss, operating time, radicality of surgery (lymph node count), hospital stay and follow-up) and surgeon-related aspects (operating time) of the learning curve of the surgical team in the transition from open to robot-assisted laparoscopic radical hysterectomy.
Material and methods
A total of 14 cases underwent an open RH between July 2004 and July 2006, and subsequently 14 patients were operated with the use of the da Vinci robot between August 2006 and January 2008 at the VU University Medical Centre in the Netherlands. All the operations were performed by the same surgical team. In one case from the Robot group, a sentinel lymph node showed metastatic disease, and the radical hysterectomy was abandoned. The patient received chemo-radiation therapy. This case was excluded from further analysis. One case in the Robot group consisted of a stage IIb endometrial cancer. All other cases where FIGO stage IBI cervical cancer.
In the Netherlands, we introduced the laparoscopic pelvic lymph node dissection (LPLND) with sentinel node (SN) detection for the surgical treatment of FIGO Ia2–IIa cervical cancers in 2000 . When the SN contains a metastasis, the procedure is abandoned, and the patient subsequently receives chemo-radiotherapy. The aim of this approach is to reduce the number of patients undergoing radical hysterectomy followed by chemo-radiation as this leads to substantially more morbidity than either treatment alone, without obvious better survival [23, 24].
Patient characteristics, histology and stage
(One endometrial cancer stage IIB, one stage Ib2 after neo-adjuvant chemo)
(One stage Ib2)
Comparison of the theatre time, lymph nodes removed, blood loss, hospital stay, complications and recurrences for radical hysterectomy patients
Robot group (n = 13)
Open group (n = 14)
Theatre time (min)
LN removed (n)
Blood loss (ml)
Hospital stay (days)
In a median follow-up of 42 months (range, 31–54), for the open group, one pelvic sidewall recurrence occurred 7 months after the radical hysterectomy. The robot group has a mean follow-up of 26 months (range, 17–32) in which two recurrences occurred. One pelvic sidewall recurrence and a port metastasis occurred 12 months after primary surgery. This was treated with excision of the port metastasis and chemo-radiation on the pelvis. The second recurrence occurred 17 months after primary surgery and was located in the rectum, a posterior exenteration was performed. All but one patient received single modality treatment. In one patient from the open group, post-operative radiotherapy was required due to a narrow vaginal margin.
Our team gradually introduced the use of the robot and had experience in laparoscopic oncological surgery and open radical surgery for many years. When a more rapid transition to robotic surgery is chosen, the learning curve may be longer. In our study, we purposely measured real theatre time as it is our experience that a significant amount of time may be lost in preparing and positioning of the patient as well as positioning and introducing the robotic system. This is also stated by Seamon et al. who looked in detail at theatre time, skin-to-skin time and console times for the robotic hysterectomy with lymphadenectomy for endometrial cancer. In their study console, time is approximately half of the total theatre time . Measuring pure console time, as is done in many other studies on robotic surgery, does not reflect the effort of the whole team's learning curve and may therefore not represent a true comparison with open surgery. In our study, the theatre time may be longer than in other centres. This is mainly due to the SN technique where the anatomical spaces are all opened before starting the lymph node in search of the SN. This procedure increases theatre time with approximately 1 h .
A dedicated surgical and anaesthesiological team tremendously increases efficiency and reduces the time spent in this phase of the procedure. Anaesthesiological aspects of robotic surgery should be well appreciated before embarking on robotic surgery [29, 30]. Interestingly, anaesthesiological complications did not occur despite prolonged surgical times with the patient in extreme Trendelenburg position.
There is still a lack of literature about the learning curve for robot-assisted gynaecological procedures. In urology, the aspect of the learning curve for radical prostatectomies is well described, and compared to conventional laparoscopy, the learning curve for robotic surgery is significantly shorter [31, 32]. Recently, the learning curve of the robot-assisted sacrocolpopexy was described, a reduction of operative time of 25% after ten cases was found, again suggesting a relatively short learning curve . Two other studies have specifically looked at learning curves in benign gynaecology. Lenihan et al. performed 113 robot-assisted procedures (mainly hysterectomies, but also myomectomy, sacrocolpopexy and oophorectomy). With the use of a dedicated team, they were able to set up the robot for surgery in 45 min after 20 cases and in 35 min within 50 cases. Robot console times and total operative times were consolidated after approximately 50 cases at about 50 min for console time and 90 min for total operative time . Forty cases of benign gynaecologic procedures by a single surgeon are described by Pitter et al. After 20 cases, a statistical improvement in operative time is reached . Both studies do not differentiate between the procedures performed.
In conclusion, robotic-assisted surgery is rapidly growing and has a high potential. Our data suggest a relatively short learning curve with quick improvement of patient and surgeon-related parameters. This is a great advantage in the implementation in daily practice.
Conflict of interest
No payment or support in kind for any aspect of the submitted work (including but not limited to grants, data monitoring board, study design, manuscript preparation, statistical analysis, etc) was received
RHM Verheijen is a proctor for radical robot-assisted surgery, sponsored by Intuitive Surgery. For all other authors there is no conflict of interest.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.