Learning Curve of Robotic Lobectomy for Early-Stage Non-Small Cell Lung Cancer by a Thoracic Surgeon Adept in Open Lobectomy

Abstract

Objective

The aim of the study was to characterize the clinical outcomes and learning curve during the adoption of a robotic platform for lobectomy for early-stage non-small cell lung cancer by a thoracic surgeon experienced in open thoracotomy.

Methods

Retrospective review of 157 consecutive patients (57 open thoracotomies, 100 robotic lobectomies) treated with lobectomy for clinical stage I or II non-small cell lung cancer between 2007 and 2014. Clinical outcomes were compared between the open thoracotomy group and five consecutive groups of 20 robotic lobectomies. We used the following six metrics to evaluate learning curve: operative time, conversion to open, estimated blood loss, hospitalization duration, overall morbidity, and pathologic nodal upstaging.

Results

The robotic and open thoracotomy groups had equivalent preoperative characteristics, except for a higher proportion of clinical stage IA patients in the robotic cohort. The robotic group, as a whole, had lower intraoperative blood loss, less overall morbidity, shorter chest tube duration, and shorter length of hospital stay as compared with the open thoracotomy group. Operative time demonstrated a bimodal learning curve. Conversion rate diminished from 22.5% in the first two robotic groups to 6.7% in the latter three groups. The rate of pathologic nodal upstaging was statistically equivalent to the open thoracotomy group.

Conclusions

Adoption of a robotic platform for lobectomy for early-stage non-small cell lung cancer by an experienced open thoracic surgeon is safe and feasible, with fewer complications, less blood loss, and equivalent nodal sampling rate even during the learning curve. The conversion to open rate significantly dropped after the first 40 robotic lobectomies, and operative time for robotic lobectomy approached open thoracotomy after 60 cases, after a bimodal curve.