Title

Feasibility and Perioperative Outcomes of Robotic-Assisted Surgery for the Management of Recurrence Disease in Gynecologic Oncology: A Multi-Institutional Study

Publication/Presentation Date

7-2013

Abstract

Objectives: Minimally invasive surgery for recurrent ovarian cancer is generally not performed. The aim of this study was to assess the feasibility and surgical outcomes of robotic-assisted surgery in the management of recurrent ovarian cancer. Methods: Eligible patients included those with confirmed recurrent ovarian cancer amenable to surgical resection and in which a complete resection was thought to be feasible with the use of the robotic platform. Patients with evidence of carcinomatosis were not considered for a robotic approach. Clinical and pathologic data were abstracted from the medical records. Appropriate statistical tests were performed using SPSS statistical software program (SPSS 20.0 Inc., Chicago, IL). Results: A total of 48 patients were identified. Thirty-six (75%) patients had a recurrent mass or masses isolated to one anatomic region (pelvis or abdomen). Conversion to laparotomy was necessary in 4 (8.3%) cases. In cases not requiring conversion to laparotomy, the median operative time, EBL, and length of stay were 179.5minutes, 50cc, and 1day, respectively. An optimal debulking was achieved in 36 (82%) cases. Complications occurred in 6 (13.6%) cases. The median operative time, EBL, length of stay, and complications were all statistically significantly lower in the cases not converted to laparotomy compared to those that were (pConclusions:This study suggests that select patients with recurrent ovarian cancer in the absence of carcinomatosis may be candidates for secondary surgical cytoreduction via a robotic approach. Surgical and postoperative outcomes appear to be favorable compared to reports of laparotomy in recurrent ovarian cancer. (Copyright © 2014. Published by Elsevier Inc.)

Volume

130

Issue

1

Disciplines

Diseases | Medical Specialties | Medicine and Health Sciences | Obstetrics and Gynecology | Oncology | Physical Sciences and Mathematics | Statistics and Probability | Surgery

PubMedID

24844594

Department(s)

Department of Obstetrics and Gynecology, Department of Obstetrics and Gynecology Faculty

Document Type

Article