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The robotic learning curve for a newly appointed colorectal surgeon

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Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5 h, while total operative time dropped from 6 h to 5.5 h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.
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Journal of Robotic Surgery (2023) 17:73–78
https://doi.org/10.1007/s11701-022-01400-1
ORIGINAL ARTICLE
The robotic learning curve foranewly appointed colorectal surgeon
SabahUddinSaqib1 · MuhammadZeeshanRaza2· CharlesEvans3· AdeelAhmadBajwa3
Received: 3 February 2022 / Accepted: 11 March 2022 / Published online: 24 March 2022
© Crown 2022
Abstract
Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of
the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies.
However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formal-
ized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and
effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics
related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in
the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a
recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console
and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related
outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve.
Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were
divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of
the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3
to 2.5h, while total operative time dropped from 6h to 5.5h as the surgeon became more experienced; however, this was
not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study
period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after
30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for
robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are
low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new
colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.
Keywords Robotic surgery· Da Vinci· Learning curve· Colorectal cancer
Abbreviations
CRC Colorectal cancer
CRM Circumferential resection margin
LOS Length of stay
rELAPE Robotic extra levator abdominoperineal
excision
rAR Robotic anterior resection
rLAR Robotic lower anterior resection
TME Total mesorectal excision
Introduction
Robotic surgery has expanded the potential of minimally
invasive surgery through articulated instrumentation giving
the freedom of movement to replicate open surgery. These
* Sabah Uddin Saqib
Sabah.saqib@uhcw.nhs.uk
Muhammad Zeeshan Raza
zraza1991@gmail.com
Charles Evans
Charles.Evans@uhcw.nhs.uk
Adeel Ahmad Bajwa
AdeelAhmad.Bajwa@uhcw.nhs.uk
1 Clinical Fellow Colorectal Surgery, University Hospital
Coventry, Coventry, UK
2 Robotic Research Fellow inRobotic Colorectal Surgery,
University Hospital Coventry, Coventry, UK
3 Consultant Colorectal Surgeon, University Hospital
Coventry, Coventry, UK
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Robotic surgery in the pelvis provides 3D visualization with increased depth perception. The tips of the instruments have seven degrees of freedom, and wrist action is controlled by the surgeon's hand control at the console to allow better ergonomics even in the narrow anatomy of the pelvis [5]. Endowrist instruments in robotic surgery unlike laparoscopic surgery provide enhanced articulation beyond the limits of human wrist movements and eliminate human hand tremors, thus facilitating superior operative dexterity with augmented precision for surgical dissection while preserving stable tissue retraction [6]. ...
... All cases were operated by a combination of colorectal, gynecological, urological, and plastic surgeons. The robot docking and undocking was recorded for the colorectal, urological, and gynecological surgeons, median time 6 h [4][5][6][7][8]; the reconstruction time taken by the plastic surgeons was not recorded separately (8 out of 13 patients). Our study results showed patients were comfortable to tolerate oral intake immediately after surgery, and the median day for end colostomy (all 13 patients) to work was 3 [2][3][4][5] days. ...
... The robot docking and undocking was recorded for the colorectal, urological, and gynecological surgeons, median time 6 h [4][5][6][7][8]; the reconstruction time taken by the plastic surgeons was not recorded separately (8 out of 13 patients). Our study results showed patients were comfortable to tolerate oral intake immediately after surgery, and the median day for end colostomy (all 13 patients) to work was 3 [2][3][4][5] days. The median length of hospital stay was 15 days which was mainly because nine of our patients suffered grade II or grade III complications on the Clavien-Dindo classification system. ...
Article
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Background Exenteration surgery for multi-visceral pelvic malignancy is a complex life-changing operation with high perioperative morbidity and mortality. Traditional open surgery has long been the standard approach for pelvic exenteration for achieving Ro resection which is the main aim of surgery. In the current era of minimally invasive surgery, robotic-assisted pelvic exenteration has provided a promising alternative, offering potential advantages in terms of improved oncological outcomes and enhanced postoperative recovery. This study aims to explore the feasibility of a robotic platform for locally advanced multi-visceral pelvic malignancy. Methods A retrospective review from the prospectively maintained robotic colorectal surgery database at University Hospital Coventry and Warwickshire (UHCW) Trust was performed. Demographic details and clinical and surgical details were documented from the case records. Data was analysed using SPSS version 22. Results Thirteen female patients diagnosed with primary or recurrent pelvic malignancy who underwent robotic pelvic exenteration at UHCW between February 2019 and April 2023 at UHCW were included. The mean age of our patients was 60.4 (± 10.1) years. Complete Ro resection was achieved in all 13 (100%) cases on final histopathology. The median length of hospital stay was 15 days after this extensive surgery. Grade 3 morbidity on Clavien–Dindo classification was observed in four (30.7%) patients, while zero percent 30-day mortality was experienced in this study. At a median follow-up of 21 (3–53) months, we observed tumor recurrence in three (23.7%) patients, while death in four (30.7%) patients. Only few studies have highlighted outcomes of robotic pelvic exenteration, and our results were quite comparable to them. Conclusion Robotic-assisted pelvic exenteration for primary or recurrent pelvic malignancy is feasible with improved oncological and acceptable postoperative outcomes.
... With increased utilization of the robotic platform, several studies have been published evaluating learning curves for colorectal surgeons [4][5][6][7][8][9][10][11][12][13]. The learning curves are established using the cumulative sum (CUSUM) analysis described by Bokhari et al. [6]. ...
... We consider the sharp drop after case 79 as the cutoff between the learning and proficiency phases. The number of cases required to get to proficiency/expert level in the available literature ranges widely, from 10's to > 100 for some surgeons [4][5][6][7][8][9][10][11][12][13]. ...
... We have adopted a similar approach for highly selected patients which could potentially explain this decrease in pathologic complete response rate. In our patient population, lymph node harvest was very similar to the ROLARR and Kim trials in both of our groups, median (IQR) 18 (13,260) and 19 (14,26) (learning, proficiency, respectively) vs 23.2 and 18 [17,18]. ...
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The surgical management of rectal cancer is shifting toward more widespread use of robotics across a spectrum of medical centers. There is evidence that the oncologic outcomes are equivalent to laparoscopic resections, and the post-operative outcomes may be improved. This study aims to evaluate the learning curve of robotic rectal cancer resections at a community-based teaching institution and evaluate clinical and oncologic outcomes. A retrospective review of consecutive robotic rectal cancer resections by a single surgeon was performed for a five-year period. The cumulative sum (CUSUM) for total operative time was calculated and plotted to establish a learning curve. The oncologic and post-operative outcomes for each phase were analyzed and compared. The CUSUM learning curve yielded two phases, the learning phase (cases 1–79) and the proficiency phase (cases 80–130). The median operative time was significantly lower in the proficiency phase. The type of neoadjuvant therapy used between the two groups was statistically different, with chemoradiation being the primary regimen in the learning phase and total neoadjuvant therapy being more common in the proficiency phase. Otherwise, oncologic and overall post-operative outcomes were not significantly different between the groups. Robotic rectal resections can be done in a community-based hospital system by trained surgeons with outcomes that are favorable and similar to larger institutions.
... They could show comparable shortterm survival and oncologic outcomes when comparing robotic to non-robotic approach [18]. Saqib et al. reviewed a single, newly appointed colorectal surgeons and significantly reduced operation time (2.5 h) and no intra-and postoperative complications as result of the learning curve [19]. They point out that a learning curve plateau can be achieved through combination of training and proctoring [19]. ...
... Saqib et al. reviewed a single, newly appointed colorectal surgeons and significantly reduced operation time (2.5 h) and no intra-and postoperative complications as result of the learning curve [19]. They point out that a learning curve plateau can be achieved through combination of training and proctoring [19]. Aradaib et al. showed with the first 55 consecutive conducted colorectal resections performed by four surgeons with benign and malign indications that implementation of a robotic colorectal training program is safe [13]. ...
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The “Robotic Curriculum for young Surgeons” (RoCS) was launched 03/2020 to address the increasing importance of robotics in surgical training. It aims to provide residents with foundational robotic skills by involving them early in their training. This study evaluated the impact of RoCS’ integration into clinical routine on patient outcomes. Two cohorts were compared regarding the implementation of RoCS: Cohort 1 (before RoCS) included all robot-assisted procedures between 2017 and 03/2020 (n = 174 adults) retrospectively; Cohort 2 (after RoCS) included all adults (n = 177) who underwent robotic procedures between 03/2020 and 2021 prospectively. Statistical analysis covered demographics, perioperative parameters, and follow-up data, including mortality and morbidity. Subgroup analysis for both cohorts was organ-related (upper gastrointestinal tract (UGI), colorectal (CR), hepatopancreaticobiliary system (HPB)). Sixteen procedures were excluded due to heterogeneity. In-hospital, 30-, 90-day morbidity and mortality showed no significant differences between both cohorts, including organ-related subgroups. For UGI, no significant intraoperative parameter changes were observed. Surgery duration decreased significantly in CR and HPB procedures (p = 0.018 and p < 0.001). Estimated blood loss significantly decreased for CR operations (p = 0.001). The conversion rate decreased for HPB operations (p = 0.005). Length of hospitalization decreased for CR (p = 0.015) and HPB (p = 0.006) procedures. Oncologic quality, measured by histopathologic R0-resections, showed no significant changes. RoCS can be safely integrated into clinical practice without compromising patient safety or oncologic quality. It serves as an effective training pathway to guide robotic novices through their first steps in robotic surgery, offering promising potential for skill acquisition and career advancement.
... In ovarian cancer (OC), MIS may only be the chosen approach in the definition of disease extension (the Fagotti score) (Marchetti et al., 2021), early-stage cases, selected cases of interval debulking surgeries (LANCE trial) (Nitecki et al., 2020) or specific recurrences (Fanfani et al., 2016b). In cervical cancer (CC), in 2018 the LACC trial (Ramirez et al., 2018) excluded the possibility of laparoscopy in patients with a tumour size >2cm suggesting that laparoscopic radical hysterectomy may have lower disease-free survival and overall survival rates as compared to open abdominal radical hysterectomy (Saqib et al., 2023). However, two prospective randomised clinical trials are underway to assess the outcomes and survival of CC patients undergoing robotic radical hysterectomy (RACC and ROCC trial) (Falconer et al., 2019;Bixel et al., 2022). ...
... The technology offers a better visual field, allowing for more precise surgery and improving outcomes in conditions such as endometriosis and cancer (Green et al., 2023). The learning curve for robotic surgery is shorter as compared to conventional laparoscopic surgery, leading to fewer open surgery conversions (Saqib et al., 2023). Blood loss and transfusions are further reduced, and there is less pain and discomfort, thanks to the dexterity of the robotic tool tips, minimising excessive leverage and force at incision sites. ...
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Background: More than two decades ago, the advent of robotic laparoscopic surgery marked a significant milestone, featuring the introduction of the AESOP robotic endoscope control system and the ZEUS robotic surgery system. The latter, equipped with distinct arms for the laparoscope and surgical instruments, was designed to accommodate remote connections, enabling the practice of remote telesurgery as early as 2001. Subsequent technological progress has given rise to a range of options in today’s market, encompassing multi-port and single-port systems, both rigid and flexible, across various price points, with further growth anticipated. Objective: This article serves as an indispensable guide for gynaecological surgeons with an interest in embracing robotic surgery. Materials and Methods: Drawing insights from the experience of the Strasbourg training centre for minimally invasive surgery (IRCAD), this article offers a comprehensive overview of existing robotic platforms in the market, as well as those in development. Results: Robotic surgical systems not only streamline established operative methods but also broaden the scope of procedures, including intra- and transluminal surgeries. As integral components of the digital surgery ecosystem, these robotic systems actively contribute to the increasing integration and adoption of advanced technologies, such as artificial intelligence-based data analysis and support systems. Conclusion: Robotic surgery is increasingly being adopted in clinical practice. With the growing number of systems available on the marketplace, the primary challenge lies in identifying the optimal platform for each specific procedure and patient. The seamless integration of robotic systems with artificial intelligence, image-guided surgery, and telesurgery presents undeniable advantages, enhancing the precision and effectiveness of surgical interventions. What is new? This article provides a guide to the robotic platforms available on the market and those in development for gynaecologists interested in robotic surgeryIntroduction
... A fitted LOESS curve is plotted to the absolute trends adjustment of CUSUM calculation, which may serve to benchmark future studies. There is a dearth of guidance for the adoption of robotic upper gastro-intestinal surgery [12], with only nascent efforts being developed in other specialties such as colorectal surgery and urology [20,21]. ...
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Background The adoption of new surgical technologies is inevitably accompanied by a learning curve. With the increasing adoption of robotic techniques in benign foregut surgery, it is imperative to define optimal learning pathways, to ensure a clinically safe introduction of such a technique. The aim of this study was to assess the learning curve for robotic hiatal hernia repair with a pre-defined adoption process and proctoring. Methods The learning curve was assessed in four surgeons in a high-volume tertiary referral centre, performing over a 100 hiatal hernia repairs annually. The robotic adoption process included simulation-based training and a multi-day wet lab-based course, followed by robotic operations proctored by robotic upper GI experts. CUSUM analysis was performed to assess changes in operating time in sequential cases. Results Each surgeon (A, B, C and D) performed between 22 and 32 cases, including a total of 109 patients. Overall, 40 cases were identified as ‘complex’ (36.7%), including 16 revisional cases (16/109, 14.7%). With CUSUM analysis inflection points for operating time were seen after 7 (surgeon B) to 15 cases (surgeon B). Conclusion The learning curve for robotic laparoscopic fundoplication may be as little as 7–15 cases in the setting of a clearly organized learning pathway with proctoring. By integrating these organized learning pathways learning curves may be shortened, ensuring patient safety, preventing detrimental outcomes due to longer learning curves, and accelerating adoption and integration of novel surgical techniques.
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Aim: This study aimed to present our experience with robotic colorectal surgery since its establishment at our institution in 2009. By examining the outcomes of over 500 patients, our experience provides a basis for assessing the introduction of a robotic platform in a colorectal practice. Specific measures investigated include intraoperative data and postoperative outcomes for all operations using the robotic platform. In addition, for our most commonly performed operations we wished to analyse the learning curve to improve operative proficiency. This is the largest single-surgeon robotic database analysed to date. Method: A prospectively maintained database of patients who underwent robotic colorectal surgery by a single surgeon at the George Washington University Hospital was retrospectively reviewed. Demographic data and perioperative outcomes were assessed. Additionally, an operating time learning curve analysis was performed. Results: Inclusion criteria identified 502 patients who underwent robotic colorectal surgery between October 2009 and December 2018. The most common indications for surgery were diverticulitis (22.9%), colon adenocarcinoma (22.1%) and rectal adenocarcinoma (19.5%). The most common operations were anterior/low anterior resection (33.9%), right hemicolectomy/ileocaecectomy (24.9%) and left hemicolectomy/sigmoidectomy (21.9%). The rate of conversion to open surgery was 4.8%. The most common postoperative complications were wound infection (5.0%), anastomotic leakage (4.0%) and abscess formation (2.8%). The operating time learning curve plateaued at 55-65 cases for anterior and low anterior resection and 35-45 cases for left hemicolectomy and sigmoidectomy. A clear learning curve was not seen in right hemicolectomy. Conclusion: Robotic-assisted surgery can be performed in a diverse colorectal practice with low rates of conversion and postoperative complications. Plateau performance was achieved after 65 anterior/low anterior resections and 45 left and sigmoid colectomies.
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Purpose: To understand the role of case complexity in the learning curve for robotic colorectal surgery. Materials and methods: Sixty-two patients who underwent robot-assisted colorectal surgery were retrospectively reviewed. Each case was assigned a category of complexity ranging from I to IV. Overall, groups and categories of segmental colectomy, rectopexy, and proctectomy for cancer were analyzed according to case volume. Forty-eight patients who underwent similar laparoscopic cases during the same period were also reviewed for comparison. Results: Level I complexity cases were identified in 30% of the first 15 cases compared to 3% after the first 15 cases (P < .01). Level IV complexity cases were identified in 10% of the first 15 cases and 34% after 15 cases (P = .03). Mean operative time for the overall group was 426 minutes (range 178-766, standard deviation [SD] = 152) in the first 15 cases and 373 minutes (range 190-593, SD = 109) after more than 15 cases (P = NS). Mean operative time for rectal cancer procedures decreased from 518 minutes (range 425-752, SD = 88) to 410 minutes (range 220-593, SD = 98) after 15 cases (P = .02). Mean operative time for rectopexy decreased from 361 minutes (range 276-520, SD = 85) to 258 minutes (range 215-318, SD = 34) after 15 cases (P = .03). Overall complications were reduced after 15 cases (6.3%) compared with the first 15 cases (27%) (P = .04). When comparing laparoscopic and open cases, laparoscopic cases were associated with a significant shorter operative time (P = < .00001) as well as overall cost (P = < .00001). Conclusion: Complex robotic colorectal surgery can be performed early in the experience, with reduced operative time. Overall complications are reduced after 15 robotic cases. This study shows that improvement in robotic surgery operating time and surgical outcomes occur along with application of the technology to more difficult cases, not as a function of choosing less complex cases.
Article
Objective: Work-related musculoskeletal disorders (WMSDs) are prevalent among surgeons and may result in practice modification. We aimed to perform a comprehensive review of the English-language literature regarding ergonomic risk, prevalence of WMSDs, and unique ergonomic considerations by route of surgery. Methods: Multiple searches were performed of PubMed and University library resources to access English-language publications related to surgeon ergonomics. Combinations of keywords were used for each mode of surgery, including the following: "ergonomics," "guidelines," "injury," "operating room," "safety," "surgeon," and "work-related musculoskeletal disorders." Each citation was read in detail, and references were reviewed. Results: Surgeon WMSDs are prevalent, with rates ranging from 66% to 94% for open surgery, 73% to 100% for conventional laparoscopy, 54% to 87% for vaginal surgery, and 23% to 80% for robotic-assisted surgery. Risk factors for injury in open surgery include use of loupes, headlamps, and microscopes. Unique risks in laparoscopic surgery include table and monitor position, long-shafted instruments, and poor instrument handle design. In vaginal surgery, improper table height and twisted trunk position create injury risk. Although robotic surgery offers some advantages, it remains associated with trunk, wrist, and finger strain. Surgeon WMSDs often result in disability but are under-reported to institutions. Additionally, existing research tools face limitations in the operating room environment. Conclusions: Work-related musculoskeletal disorders are prevalent among surgeons but have received little attention owing to under-reporting of injury and logistical constraints of studying surgical ergonomics. Future research must aim to develop objective surgical ergonomics instruments and guidelines and to correlate ergonomics assessments with pain and tissue-level damage in surgeons with WMSDs. Ergonomics training should be developed to protect surgeons from preventable, potentially career-altering injuries.
Article
Background With the increasing availability of the surgical robotic system, the young generation colorectal surgeons may learn robotic‐assisted rectal surgery upfront. There are currently very limited studies evaluating the learning curve of novice rectal surgeons. Objective This study aimed to evaluate the learning curve of a surgeon who had limited experience in open and laparoscopic rectal surgery. Methods Thirty‐nine consecutive robotic‐assisted total mesorectal excisions were performed from March 2013 to October 2014. All cases were performed by a single surgeon whose prior experience in open or laparoscopic low rectal cancer resections was <5 cases. The learning curve was analyzed using the cumulative sum method. Results Thirty‐four low anterior resections, four abdomino‐perineal resections, and one Hartmann’s operation were performed. The mean total operating time was 397.2 ± 184.3 min. There was no conversion. The major complication rate was 10.3 %. When total operating time was analyzed with the CUSUM method, three phases could be identified. They are the initial eight cases, middle 17 cases, and the final 14 cases. The first phase consisted of more proximal tumors (86.3 ± 20.7 vs. 58.0 ± 34.9 mm from anal verge, p = 0.04) and was associated with a shorter total operating time (243.5 ± 38.0 vs. 540.9 ± 133.4 min, p = 0.000) and less estimated blood loss (81.3 ± 25.9 vs. 168.8 ± 99.5 ml, p = 0.02) compared to the second phase. When the third phase is compared with the first and second phase, it has shorter total operating time (310.6 ± 164.5 vs. 44 5.7 ± 179.8 min, p = 0.03). Complications rate were 12.5, 17.6, and 0 % for phase one, two, and three respectively. Conclusions In this study, the learning curve for a novice rectal surgeon was 25 cases. This is comparable to those who have already mastered the technique with laparoscopic or open approach. Surgical robotic system may have a role in shortening the learning curve for low rectal resection.
Article
Background: Few data are available to assess the learning curve for robotic-assisted surgery for rectal cancer. The aim of the present study was to evaluate the learning curve for robotic-assisted surgery for rectal cancer by a surgeon at a single institute. Methods: From December 2011 to August 2013, a total of 80 consecutive patients who underwent robotic-assisted surgery for rectal cancer performed by the same surgeon were included in this study. The learning curve was analyzed using the cumulative sum method. This method was used for all 80 cases, taking into account operative time. Results: Operative procedures included anterior resections in 6 patients, low anterior resections in 46 patients, intersphincteric resections in 22 patients, and abdominoperineal resections in 6 patients. Lateral lymph node dissection was performed in 28 patients. Median operative time was 280 min (range 135-683 min), and median blood loss was 17 mL (range 0-690 mL). No postoperative complications of Clavien-Dindo classification Grade III or IV were encountered. We arranged operative times and calculated cumulative sum values, allowing differentiation of three phases: phase I, Cases 1-25; phase II, Cases 26-50; and phase III, Cases 51-80. Conclusions: Our data suggested three phases of the learning curve in robotic-assisted surgery for rectal cancer. The first 25 cases formed the learning phase.