Surgical Endoscopy

, Volume 33, Issue 12, pp 3880–3888 | Cite as

Defining the learning curve of robotic thoracic surgery: what does it take?

  • Alexandra D. Power
  • Desmond M. D’Souza
  • Susan D. Moffatt-Bruce
  • Robert E. Merritt
  • Peter J. KneuertzEmail author
Review Article



Controversy exists as to what constitutes a learning curve to achieve competency, and how the initial learning period of robotic thoracic surgery should be approached.


We conducted a systematic review of the literature published prior to December 2018 using PubMed/MEDLINE for studies of surgeons adopting the robotic approach for anatomic lung resection or thymectomy. Changes in operating room time and outcomes based on number of cases performed, type of procedure, and existing proficiency with video-assisted thoracoscopic surgery (VATS) were examined.


Twelve observational studies were analyzed, including nine studies on robotic lung resection and three studies on thymectomy. All studies showed a reduction in operative time with an increasing number of cases performed. A steep learning curve was described for thymectomy, with a decrease in operating room time in the first 15 cases and a plateau after 15–20 cases. For anatomic lung resection, the number of cases to achieve a plateau in operative time ranged between 15–20 cases and 40–60 cases. All but two studies had at least some VATS experience. Six studies reported on experience of over one hundred cases and showed continued gradual improvements in operating room time.


The learning curve for robotic thoracic surgery appears to be rapid with most studies indicating the steepest improvement in operating time occurring in the initial 15–20 cases for thymectomy and 20–40 cases for anatomic lung resection. Existing data can guide a standardized robotic curriculum for rapid adaptation, and aid credentialing and quality monitoring for robotic thoracic surgery programs.


Robotic surgery Learning curve Lobectomy Segmentectomy Thymectomy 



This research was not solicited and did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards


Dr. Merritt is a speaker for intuitive Surgical. Dr. D’Souza is a Proctor for Intuitive Surgical. Drs. Power, Moffatt-Bruce, and Kneuertz have no conflicts of interest or financial ties to disclose.


  1. 1.
    Long H et al (2018) Thoracoscopic versus thoracotomy for lung cancer: short-term outcomes of a randomized trial. Ann Thorac Surg 105(2):386–392CrossRefGoogle Scholar
  2. 2.
    Bendixen M et al (2019) Cost-utility analysis of minimally invasive surgery for lung cancer: a randomized controlled trial. Eur J Cardiothorac Surg. CrossRefPubMedGoogle Scholar
  3. 3.
    Demmy T et al (2018) Oncologic equivalence of minimally invasive lobectomy: the scientific and practical arguments. Ann Thorac Surg 106(2):609–617CrossRefGoogle Scholar
  4. 4.
    Baldonado J et al (2018) Credentialing for robotic lobectomy: what is the learning curve? A retrospective analysis of 272 cases by a single surgeon. J Robot Surg. CrossRefPubMedGoogle Scholar
  5. 5.
    Rajaram R et al (2017) Nationwide assessment of robotic lobectomy for non-small cell lung cancer. Ann Thorac Surg 103(4):1092–1100CrossRefGoogle Scholar
  6. 6.
    Wei B, Eldaif S, Cerfolio R (2016) Robotic lung resection for non-small cell lung cancer. Surg Oncol Clin N Am 25(3):515–531CrossRefGoogle Scholar
  7. 7.
    Veronesi G (2015) Robotic lobectomy and segmentectomy for lung cancer: results and operating technique. J Thorac Dis 7:122–130Google Scholar
  8. 8.
    Hernandez J et al (2012) Robotic lobectomy: flattening the learning curve. J Robot Surg 6(1):41–45CrossRefGoogle Scholar
  9. 9.
    Gallagher SP et al (2018) Learning curve of robotic lobectomy for early stage non-small cell lung cancer by a thoracic surgeon adept in open lobectomy. Innovations 13(5):321–327CrossRefGoogle Scholar
  10. 10.
    Stewart L et al (2015) Preferred reporting items for systematic review and meta-analyses of individual participant data. JAMA 313:1657–1665CrossRefGoogle Scholar
  11. 11.
    Downs S, Black N (1998) The feasibility of creating a checklist for the assessment of the methodological quality both of randomized and non-randomized studies of health care interventions. J Epidemiol Community Health 52:377–384CrossRefGoogle Scholar
  12. 12.
    Fahim C et al (2017) Robotic-assisted thoracoscopic surgery for lung resection: the first Canadian series. Can J Surg 60(4):260–265CrossRefGoogle Scholar
  13. 13.
    Huang P et al (2014) Experience with the “da Vinci” robotic system for early-stage thymomas: report of 23 cases. Thorac Cancer 5(4):325–329CrossRefGoogle Scholar
  14. 14.
    Ro C et al (2006) Three-year experience with totally endoscopic robotic thymectomy. Innovations 1(3):111–114CrossRefGoogle Scholar
  15. 15.
    Kamel M et al (2017) Robotic thymectomy: learning curve and associated perioperative outcomes. J Laparoendosc Adv Surg Tech 27(7):685–690CrossRefGoogle Scholar
  16. 16.
    Cheufou D et al (2011) Starting a robotic program in general thoracic surgery: how, why, and lessons learned. Ann Thorac Surg 91(6):1729–1737CrossRefGoogle Scholar
  17. 17.
    Veronesi G et al (2011) Experience with robotic lobectomy for lung cancer. Innovations 6(6):355–360CrossRefGoogle Scholar
  18. 18.
    Meyer M et al (2012) The learning curve of robotic lobectomy. Int J Med Robot Comput Assisted Surg 8:448–452CrossRefGoogle Scholar
  19. 19.
    Toker A et al (2016) Robotic anatomic lung resections: the initial learning experience and description of learning in 102 cases. Surg Endosc 30:676–683CrossRefGoogle Scholar
  20. 20.
    Zhang Y et al (2018) Robotic anatomical segmentectomy: an analysis of the learning curve. Ann Thorac Surg 107(5):1515–1522CrossRefGoogle Scholar
  21. 21.
    Gonsenhauser I et al (2012) Developing a multidisciplinary robotic surgery quality assessment program. J Healthc Qual 34(3):43–53CrossRefGoogle Scholar
  22. 22.
    Li X, Wang J, Ferguson M (2014) Competence versus mastery: the time course for developing proficiency in video-assisted thoracoscopic lobectomy. J Thorac Cardiovasc Surg 147(4):1150–1154CrossRefGoogle Scholar
  23. 23.
    Zhao H et al (2010) Video-assisted thoracoscopic surgery lobectomy for lung cancer: the learning curve. World J Surg 34(10):2368–2372CrossRefGoogle Scholar
  24. 24.
    Mazzella A et al (2016) Video-assisted thoracoscopic lobectomy: which the learning curve of an experienced consultant? J Thorac Dis 8(9):2444–2453CrossRefGoogle Scholar
  25. 25.
    Cerfolio R, Bryant A (2013) How to teach robotic pulmonary resection. Semin Thorac Cardiovasc Surg 25(1):76–82CrossRefGoogle Scholar
  26. 26.
    Estes S et al (2017) Best practices for robotic surgery programs. JSLS 21(2):e2016.00102CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Thoracic Surgery, Department of SurgeryThe Ohio State University Wexner Medical CenterColumbusUSA

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