Interpretation
Recently, several studies showed that surgical proficiency and surgical
volume seem to play a substantial role in the oncologic outcomes of
patients with early-stage cervical cancer treated with a surgical robot,
with reported learning curves varying from 19 to 77
cases.9-12 Analysing our own cohort retrospectively,
we demonstrated a learning curve of at least 61 procedures during which
the survival of patients was inferior to the experienced phase
thereafter.8 These nationwide and institutional
studies all started their inclusions from the first robot-assisted
procedures, soon after the robot was adopted for gynaecological
purposes. In the early days of robot-assisted surgery, a structured
learning curriculum, as we know it today, did not
exist.30 Now that several structured learning
curricula for robot-assisted surgery within gynaecological oncology
exist31, 32, it is critical to assess its performance
in terms of patient outcomes.
Studies in prostate, pancreas and oesophageal cancer already showed that
a structured learning curriculum allows for safe introduction of less
experienced robotic surgeons without compromising patient outcomes.
Their results were mostly based on surgical outcomes such as operation
time, blood loss and specimen radicality.33-35Regarding oncological outcomes, several studies performed after the LACC
trial suggest it would be safe to continue with minimally invasive
surgery when performed by experienced (high-volume)
surgeons.36, 37 However, these studies do not specify
how the surgeons were trained, other than one study mentioning the
surgeons were fellowship-trained.
Importantly, learning curve effects do not merely apply to
robot-assisted surgery. In cervical cancer specifically, research showed
that surgeons in the early phase of laparoscopic and open radical
hysterectomy are also subjected to a learning curve.12Results from studies across the full spectrum of medical specialities
and procedures suggest that the learning curve concept applies to
adopting many complex surgical procedures, if not
all.2, 38-40 Such a learning curve should not be
perceived as negative but surgeons need to understand how to limit its
effect on patient outcomes while effectively acquiring skills.
In group 3 a lower number of lymph nodes were harvested at pelvic lymph
node dissection compared to the other two groups, which could not be
explained. No clear consensus exists regarding the relationship between
surgical experience and number of lymph nodes
harvested.41, 42 The number of lymph nodes removed was
not associated with the recurrence rate (non-significant in univariate
logistic regression).
Robot-assisted surgery is evolving fast and training programs have a
hard time keeping up.43 In previous years, several
curricula for robot-assisted gynaecological surgery have been developed,
including the SERGS curriculum, which is based on the (so far only)
validated curriculum of the European Association of Urology (EAU)
Robotic Urologic Section (ERUS).31, 44 The SERGS
curriculum follows a validated format30 including
didactic training, dry lab, virtual reality simulation, cadaver
training, and a stepwise approach of patient training with a minimum of
10 proctored cases.24 Although volume based criteria
are not completely abandoned yet, focus is shifted towards competency
based criteria. Still, as our results also show, room for further
improvement exists since curricula are a crucial step in the
standardisation of training and certification of robotic surgeons.
Besides curricula development, strategies to assess maintenance of
robotic proficiency and to teach the trainers are also
needed.45
Quantitative robot-generated performance metrics, so far a rather unused
potential in robot learning curricula, could play a crucial role in
improving robotic training.46 Incorporating these
metrics into a curriculum could be an inexpensive and effective way to
quantify skill acquisition. Research on learning curves of conventional
laparoscopy trainees already demonstrated that use of force, motion and
time metrics could indicate progression of skills over
time.47-49 Future areas of research should include
validating existing curricula, determining how to translate the acquired
skills into patient outcomes (prospectively), and find strategies to
assess maintenance of surgical proficiency.
Conclusion
Based on these single-institution results, introducing a novice robotic
surgeon who was trained in accordance with a structured curriculum did
not significantly compromise surgical or oncological outcomes of
early-stage cervical cancer patients treated with robot-assisted
surgery. Further research should explore ways to objectively quantify
skill acquisition during training and, thereby, minimize the impact of
learning curves on patient outcomes.
Acknowledgements
The authors are grateful to R.H.M. Verheijen (emeritus professor
Gynaecological Oncology, UMC Utrecht) for his contribution to this study
and to the manuscript.
Disclosure of Interests
RPZ is a proctor for robot-assisted surgery in gynaecological oncology
on behalf of Intuitive Surgical. All other authors declare they have no
conflicts of interests related to the presented research.
Contribution to
Authorship
IGTB : Conceptualization, Methodology, Formal analysis,
Investigation, Writing – Original Draft, Visualisation. JPH :
Conceptualization, Methodology, Validation, Writing – Review &
Editing. HWRS : Validation, Writing – Review & Editing.IMJ : Validation, Writing – Review & Editing. CGG :
Conceptualization, Methodology, Validation, Writing – Review &
Editing. RPZ : Conceptualization, Methodology, Validation,
Writing – Review & Editing, Supervision.
Details of Ethics
Approval
This study was based on the departmental complication and treatment
outcome register for robot-assisted surgery, which is maintained as a
part of standard clinical care and primarily aims to improve that care.
The institutional review board approved this study and waived individual
consent requirements for the use of these pseudonymised retrospective
data in accordance to Dutch law. The Medical Research Involving Human
Subjects Act (WMO) did not apply to our study and therefore official
approval by the Medical Research Ethics Committee was not required under
the WMO (MREC number 21-250, date April 7th 2021).
Funding
None.
References
1. Gofton WT, Papp SR, Gofton T, Beaulé PE. Understanding and Taking
Control of Surgical Learning Curves. Instr Course Lect. 2016;65:623-31.
2. Hopper AN, Jamison MH, Lewis WG. Learning curves in surgical
practice. Postgraduate Medical Journal. 2007;83(986):777-9.
3. Schreuder HW, Zweemer RP, van Baal WM, van de Lande J, Dijkstra JC,
Verheijen RH. From open radical hysterectomy to robot-assisted
laparoscopic radical hysterectomy for early stage cervical cancer:
aspects of a single institution learning curve. Gynecological surgery.
2010 Sep;7(3):253-8.
4. Yim GW, Kim SW, Nam EJ, Kim S, Kim YT. Learning curve analysis of
robot-assisted radical hysterectomy for cervical cancer: initial
experience at a single institution. J Gynecol Oncol. 2013
Oct;24(4):303-12.
5. Heo YJ, Kim S, Min KJ, Lee S, Hong JH, Lee JK, et al. The comparison
of surgical outcomes and learning curves of radical hysterectomy by
laparoscopy and robotic system for cervical cancer: an experience of a
single surgeon. Obstetrics & gynecology science. 2018 Jul;61(4):468-76.
6. Seamon LG, Fowler JM, Richardson DL, Carlson MJ, Valmadre S, Phillips
GS, et al. A detailed analysis of the learning curve: robotic
hysterectomy and pelvic-aortic lymphadenectomy for endometrial cancer.
Gynecol Oncol. 2009 Aug;114(2):162-7.
7. Rocconi RP, Meredith C, Finan MA. Evaluation of the learning curve of
total robotic hysterectomy with or without lymphadenectomy for a
gynecologic oncology service. J Robot Surg. 2011 Sep;5(3):189-93.
8. Baeten IGT, Hoogendam JP, Schreuder H, Jurgenliemk-Schulz IM,
Verheijen RHM, Zweemer RP, et al. The influence of learning curve of
robot-assisted laparoscopy on oncological outcomes in early-stage
cervical cancer: an observational cohort study. BJOG : an international
journal of obstetrics and gynaecology. 2021 Feb;128(3):563-71.
9. Ekdahl L, Wallin E, Alfonzo E, Reynisson P, Lönnerfors C, Dahm-Kähler
P, et al. Increased Institutional Surgical Experience in Robot-Assisted
Radical Hysterectomy for Early Stage Cervical Cancer Reduces Recurrence
Rate: Results from a Nationwide Study. J Clin Med. 2020;9(11):3715.
10. Eoh KJ, Lee JY, Nam EJ, Kim S, Kim SW, Kim YT. The institutional
learning curve is associated with survival outcomes of robotic radical
hysterectomy for early-stage cervical cancer-a retrospective study. BMC
Cancer. 2020 Feb 24;20(1):152.
11. Paek J, Lim PC. The early surgical period in robotic radical
hysterectomy is related to the recurrence after surgery in stage IB
cervical cancer. Int J Med Sci. 2021;18(12):2697-704.
12. Kim S, Min KJ, Lee S, Hong JH, Song JY, Lee JK, et al. Learning
curve could affect oncologic outcome of minimally invasive radical
hysterectomy for cervical cancer. Asian J Surg. 2021 Jan;44(1):174-80.
13. Ramirez PT, Frumovitz M, Pareja R, Lopez A, Vieira M, Ribeiro R, et
al. Minimally Invasive versus Abdominal Radical Hysterectomy for
Cervical Cancer. N Engl J Med. 2018 Nov 15;379(20):1895-904.
14. Melamed A, Margul DJ, Chen L, Keating NL, Del Carmen MG, Yang J, et
al. Survival after Minimally Invasive Radical Hysterectomy for
Early-Stage Cervical Cancer. N Engl J Med. 2018 Nov 15;379(20):1905-14.
15. Falconer H. Evaluating robotic surgical courses: structured training
matters. J Gynecol Oncol. 2021 Mar;32(2):e39.
16. Mikhail E, Salemi JL, Hart S, Imudia AN. Comparing Single and Dual
Console Systems in the Robotic Surgical Training of Graduating OB/GYN
Residents in the United States. Minim Invasive Surg. 2016;2016:5190152.
17. Bertolo R, Garisto J, Dagenais J, Sagalovich D, Kaouk J. Single
Session of Robotic Human Cadaver Training: The Immediate Impact on
Urology Residents in a Teaching Hospital. Journal of Laparoendoscopic &
Advanced Surgical Techniques. 2018;28(10):1157-62.
18. Sheth SS, Fader AN, Tergas AI, Kushnir CL, Green IC. Virtual reality
robotic surgical simulation: an analysis of gynecology trainees. J Surg
Educ. 2014 Jan-Feb;71(1):125-32.
19. Morgan MSC, Shakir NA, Garcia-Gil M, Ozayar A, Gahan JC, Friedlander
JI, et al. Single- versus dual-console robot-assisted radical
prostatectomy: impact on intraoperative and postoperative outcomes in a
teaching institution. World Journal of Urology. 2015
2015/06/01;33(6):781-6.
20. Schreuder HW, Persson JE, Wolswijk RG, Ihse I, Schijven MP,
Verheijen RH. Validation of a novel virtual reality simulator for
robotic surgery. ScientificWorldJournal. 2014;2014:507076.
21. Rusch P, Ind T, Kimmig R, Maggioni A, Ponce J, Zanagnolo V, et al.
Recommendations for a standardised educational program in robot assisted
gynaecological surgery: Consensus from the Society of European Robotic
Gynaecological Surgery (SERGS). Facts Views Vis Obgyn. 2019
Mar;11(1):29-41.
22. Azadi S, Green IC, Arnold A, Truong M, Potts J, Martino MA. Robotic
Surgery: The Impact of Simulation and Other Innovative Platforms on
Performance and Training. Journal of minimally invasive gynecology. 2021
2021/03/01/;28(3):490-5.
23. Boitano TKL, Smith HJ, Cohen JG, Rossi EC, Kim KH. Implementation
and evaluation of a novel subspecialty society fellows robotic surgical
course: the SGO minimally invasive academy surgical curriculum. J
Gynecol Oncol. 2021 3/;32(2).
24. (SERGS) SoERGS. SERGS Curriculum for robotassisted gynaecological
surgery. 2017.
25. Pecorelli S. Revised FIGO staging for carcinoma of the vulva,
cervix, and endometrium. International journal of gynaecology and
obstetrics: the official organ of the International Federation of
Gynaecology and Obstetrics. 2009 May;105(2):103-4.
26. Bhatla N, Berek JS, Cuello Fredes M, Denny LA, Grenman S,
Karunaratne K, et al. Revised FIGO staging for carcinoma of the cervix
uteri. International journal of gynaecology and obstetrics: the official
organ of the International Federation of Gynaecology and Obstetrics.
2019 Apr;145(1):129-35.
27. National Cancer Institute. Common Terminology Criteria for Adverse
Events (CTCAE). 2018.
https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm#ctc_50.
Published November 2017. Accessed December 15, 2021.
28. Sedlis A, Bundy BN, Rotman MZ, Lentz SS, Muderspach LI, Zaino RJ. A
Randomized Trial of Pelvic Radiation Therapy versus No Further Therapy
in Selected Patients with Stage IB Carcinoma of the Cervix after Radical
Hysterectomy and Pelvic Lymphadenectomy: A Gynecologic Oncology Group
Study. Gynecologic Oncology. 1999 1999/05/01/;73(2):177-83.
29. Steiner SH, Woodall WH. Debate: what is the best method to monitor
surgical performance? BMC Surg. 2016 Apr 5;16:15.
30. Schreuder HW, Wolswijk R, Zweemer RP, Schijven MP, Verheijen RH.
Training and learning robotic surgery, time for a more structured
approach: a systematic review. BJOG : an international journal of
obstetrics and gynaecology. 2012 Jan;119(2):137-49.
31. Rusch P, Kimmig R, Lecuru F, Persson J, Ponce J, Degueldre M, et al.
The Society of European Robotic Gynaecological Surgery (SERGS) Pilot
Curriculum for robot assisted gynecological surgery. Archives of
Gynecology and Obstetrics. 2018 2018/02/01;297(2):415-20.
32. Ismail A, Wood M, Ind T, Gul N, Moss E. The development of a robotic
gynaecological surgery training curriculum and results of a delphi
study. BMC Medical Education. 2020 2020/03/04;20(1):66.
33. van der Sluis PC, Ruurda JP, van der Horst S, Goense L, van
Hillegersberg R. Learning Curve for Robot-Assisted Minimally Invasive
Thoracoscopic Esophagectomy: Results From 312 Cases. The Annals of
Thoracic Surgery. 2018 2018/07/01/;106(1):264-71.
34. Rice MK, Hodges JC, Bellon J, Borrebach J, Al Abbas AI, Hamad A, et
al. Association of Mentorship and a Formal Robotic Proficiency Skills
Curriculum With Subsequent Generations’ Learning Curve and Safety for
Robotic Pancreaticoduodenectomy. JAMA Surgery. 2020;155(7):607-15.
35. Ryan JPC, Lynch O, Broe MP, Swan N, Moran D, McGuire B, et al.
Robotic-assisted radical prostatectomy—impact of a mentorship program
on oncological outcomes during the learning curve. Irish Journal of
Medical Science (1971 -). 2022 2022/02/01;191(1):479-84.
36. Alfonzo E, Wallin E, Ekdahl L, Staf C, Radestad AF, Reynisson P, et
al. No survival difference between robotic and open radical hysterectomy
for women with early-stage cervical cancer: results from a nationwide
population-based cohort study. Eur J Cancer. 2019 Jul;116:169-77.
37. Leitao MM, Zhou QC, Brandt B, Iasonos A, Sioulas V, Lavigne Mager K,
et al. The MEMORY Study: MulticentEr study of Minimally invasive surgery
versus Open Radical hYsterectomy in the management of early-stage
cervical cancer: Survival outcomes. Gynecologic Oncology. 2022
2022/09/01/;166(3):417-24.
38. Brajcich BC, Stulberg JJ, Palis BE, Chung JW, Huang R, Nelson H, et
al. Association Between Surgical Technical Skill and Long-term Survival
for Colon Cancer. JAMA Oncology. 2021;7(1):127-9.
39. van der Poel MJ, Besselink MG, Cipriani F, Armstrong T, Takhar AS,
van Dieren S, et al. Outcome and Learning Curve in 159 Consecutive
Patients Undergoing Total Laparoscopic Hemihepatectomy. JAMA Surg. 2016
Oct 1;151(10):923-8.
40. Kazi M, Sukumar V, Bankar S, Kapadia R, Desouza A, Saklani A.
Learning curves for minimally invasive total mesorectal excision beyond
the competency phase - a risk-adjusted cumulative sum analysis of 1000
rectal resections. Colorectal Dis. 2022 Dec;24(12):1516-25.
41. Togami S, Kawamura T, Fukuda M, Yanazume S, Kamio M, Kobayashi H.
Learning curve and surgical outcomes for laparoscopic surgery, including
pelvic lymphadenectomy, for early stage endometrial cancer. Japanese
Journal of Clinical Oncology. 2019;49(6):521-4.
42. Lim PC, Kang E, Park DH. Learning Curve and Surgical Outcome for
Robotic-Assisted Hysterectomy with Lymphadenectomy: Case-Matched
Controlled Comparison with Laparoscopy and Laparotomy for Treatment of
Endometrial Cancer. Journal of minimally invasive gynecology. 2010
2010/11/01/;17(6):739-48.
43. Imai T, Amersi F, Tillou A, Chau V, Soukiasian H, Lin M. A
Multi-Institutional Needs Assessment in the Development of a Robotic
Surgery Curriculum: Perceptions From Resident and Faculty Surgeons.
Journal of Surgical Education. 2023 2023/01/01/;80(1):93-101.
44. Volpe A, Ahmed K, Dasgupta P, Ficarra V, Novara G, van der Poel H,
et al. Pilot Validation Study of the European Association of Urology
Robotic Training Curriculum. European Urology. 2015
2015/08/01/;68(2):292-9.
45. Collins JW, Levy J, Stefanidis D, Gallagher A, Coleman M, Cecil T,
et al. Utilising the Delphi Process to Develop a Proficiency-based
Progression Train-the-trainer Course for Robotic Surgery Training. Eur
Urol. 2019 May;75(5):775-85.
46. Quinn KM, Chen X, Runge LT, Pieper H, Renton D, Meara M, et al. The
robot doesn’t lie: real-life validation of robotic performance metrics.
Surg Endosc. 2022 Oct 20.
47. Hardon SF, van Gastel LA, Horeman T, Daams F. Assessment of
technical skills based on learning curve analyses in laparoscopic
surgery training. Surgery. 2021 Sep;170(3):831-40.
48. Overtoom EM, Horeman T, Jansen F-W, Dankelman J, Schreuder HWR.
Haptic Feedback, Force Feedback, and Force-Sensing in Simulation
Training for Laparoscopy: A Systematic Overview. Journal of Surgical
Education. 2019 2019/01/01/;76(1):242-61.
49. Horeman T, Dankelman J, Jansen FW, Van Den Dobbelsteen JJ.
Assessment of laparoscopic skills based on force and motion parameters.
IEEE Transactions on Biomedical Engineering. 2014;61(3):805-13.