Robot-Assisted versus Laparoscopic-Assisted Gastrectomy among Gastric Cancer Patients: A Retrospective Short-Term Analysis from a Single Institution in China

Abstract

Background: The da Vinci robotic system was considered an effectively alternative treatment option for early gastric cancer patients in recent years. The aim of our study was to evaluate the safety and feasibility of robot-assisted gastrectomy in our center.

Methods: This study included 33 patients who underwent robot-assisted gastrectomy (RAG) and 88 patients who underwent laparoscopic-assisted gastrectomy (LAG) between January 2016 and April 2018. Clinicopathological characteristics, surgical parameters, postoperative recovery, and the learning curves of RAG were evaluated.

Results: Baseline characteristics between two groups were well balanced. The operation time of RAG was longer than that of LAG (333.1 ± 61.4 min vs. 290.6 ± 39.0 min, p = 0.001), and the estimated blood loss was 62.4 ± 41.2 ml in the RAG group and 77.7 ± 32.3 ml in the LAG group (p = 0.005), respectively. The mean number of examined lymph nodes in RAG was less than that in LAG (30.3 ± 10.2 vs. 37.4 ± 13.7, p = 0.008). However, RAG had an advantage in the dissection of No. 9 lymph nodes (3.4 ± 2.1 vs. 2.5 ± 1.6, p = 0.039). The incidence of postoperative complications was similar in both groups (p = 0.735). There were no significant differences in terms of postoperative recovery between the two groups. The learning curve of RAG showed that the CUSUM value decreased from the 8th case, which suggested a rapid learning curve among experienced surgeons on LAG operations.

Conclusions: RAG was safe and feasible for gastric cancer patients, with superiority in the dissection of No. 9 lymph nodes.

Figure 1

Flow chart of this study.

Figure 2

Operation time of RAG and LAG patients in the order of date of operation.

Figure 3

Learning curve for RAG in terms of the cumulative sum of operation time. Dashed line showed the best fitted curve; the equation was y = 0.0573x³–3.509x² + 54x–1.357 (R² = 0.7307), where x represented the case number. The cut-off value was the 10th case.

Figure 4

Learning curve for RAG in terms of multidimensional cumulative sum analysis. Dashed line showed the best fitted curve; the equation was y = 0.001216x³–0.0686x² + 0.8825x + 2.23 (R² = 0.8146), where x represented the case number. The cut-off value was the 8th case.