ABSTRACT
The authors wish to make the following corrections to this paper [...].
ABSTRACT
Surgery for locally recurrent rectal cancer (LRRC) presents several challenges, which is why the percentage of inadequate resections of these tumors is high. In this exploratory study, we evaluate the use of image-guided surgical navigation during resection of LRRC. Patients who were scheduled to undergo surgical resection of LRRC who were deemed by the multidisciplinary team to be at a high risk of inadequate tumor resection were selected to undergo surgical navigation. The risk of inadequate surgery was further determined by the proximity of the tumor to critical anatomical structures. Workflow characteristics of the surgical navigation procedure were evaluated, while the surgical outcome was determined by the status of the resection margin. In total, 20 patients were analyzed. For all procedures, surgical navigation was completed successfully and demonstrated to be accurate, while no complications related to the surgical navigation were discerned. Radical resection was achieved in 14 cases (70%). In five cases (25%), a tumor-positive resection margin (R1) was anticipated during surgery, as extensive radical resection was determined to be compromised. These patients all received intraoperative brachytherapy. In one case (5%), an unexpected R1 resection was performed. Surgical navigation during resection of LRRC is thus safe and feasible and enables accurate surgical guidance.
ABSTRACT
Robot-assisted surgery is assumed to be time consuming partially due to extra time needed in preparing the robot. The objective of this study was to give realistic times in Da Vinci Xi draping and docking and to analyse the learning curve in the transition from the Si to the Xi in an experienced team. This prospective study was held in a hospital with a high volume of robot-assisted surgery in general surgery, urology and gynaecology. Times from the moment patients entered the operating room until the surgeon took place behind console were precisely recorded during the first 6 weeks after the implementation of the Xi. In total, 65 procedures were performed and documented. The learning curve for the process of draping and docking the robot was reached after 21 and 18 cases, respectively. Mean times after completion of the learning curve were 5 min for draping and 7 min for docking and were statistically different from mean times before completion of the learning curve (p values < 0.01). In dedicated teams netto extra time needed for preparing the Xi can even be reduced to just the time needed for docking. Thus, setting up the robot should have limited impact on overall time spent in the operation room.