Augmented Reality Navigation for Stereoscopic Laparoscopic Anatomical Hepatectomy of Primary Liver Cancer: Preliminary Experience.

Abstract

BACKGROUND:

Accurate determination of intrahepatic anatomy remains challenging for laparoscopic anatomical hepatectomy (LAH). Laparoscopic augmented reality navigation (LARN) is expected to facilitate LAH of primary liver cancer (PLC) by identifying the exact location of tumors and vessels. The study was to evaluate the safety and effectiveness of our independently developed LARN system in LAH of PLC.

METHODS:

From May 2018 to July 2020, the study included 85 PLC patients who underwent three-dimensional (3D) LAH. According to whether LARN was performed during the operation, the patients were divided into the intraoperative navigation (IN) group and the non-intraoperative navigation (NIN) group. We compared the preoperative data, perioperative results and postoperative complications between the two groups, and introduced our preliminary experience of this novel technology in LAH.

RESULTS:

There were 44 and 41 PLC patients in the IN group and the NIN group, respectively. No significant differences were found in preoperative characteristics and any of the resection-related complications between the two groups (All P > 0.05). Compared with the NIN group, the IN group had significantly less operative bleeding (P = 0.002), lower delta Hb% (P = 0.039), lower blood transfusion rate (P < 0.001), and reduced postoperative hospital stay (P = 0.003). For the IN group, the successful fusion of simulated surgical planning and operative scene helped to determine the extent of resection.

CONCLUSIONS:

The LARN contributed to the identification of important anatomical structures during LAH of PLC. It reduced vascular injury and accelerated postoperative recovery, showing a potential application prospects in liver surgery.

• Authors: Weiqi Zhang, Wen Zhu, Jian Yang, Nan Xiang, Ning Zeng, Haoyu Hu, Fucang Jia, Chihua Fang
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 663236 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 33842378 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Zhang, Zhu, Yang, Xiang, Zeng, Hu, Jia and Fang.