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Real-time navigation for laparoscopic hepatectomy using image fusion of preoperative 3D surgical plan and intraoperative indocyanine green fluorescence imaging

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Abstract

Background

Understanding the internal anatomy of the liver remains a major challenge in anatomical liver resection. Although virtual hepatectomy and indocyanine green (ICG) fluorescence imaging techniques have been widely used in hepatobiliary surgery, limitations in their application for real-time navigation persist.

Objective

The aim of the present study was to evaluate the feasibility and clinical utility of the novel laparoscopic hepatectomy navigation system (LHNS), which fuses preoperative three-dimensional (3D) models with ICG fluorescence imaging to achieve real-time surgical navigation.

Methods

We conducted a retrospective review of clinical outcome for 64 patients who underwent laparoscopic hepatectomy from January 2018 to December 2018, including 30 patients who underwent the procedure using the LHNS (LHNS group) and 34 patients who underwent the procedure without LHNS guidance (Non-LHNS group).

Results

There was no significant difference in preoperative characteristics between the two groups. The LHNS group had a significantly less blood loss (285.0 ± 163.0 mL vs. 391.1 ± 242.0 mL; P = 0.047), less intraoperative blood transfusion rate (13.3% vs. 38.2%; P = 0.045), and shorter postoperative hospital stay (7.8 ± 2.1 days vs. 10.6 ± 3.8 days; P < 0.001) than the Non-LHNS group. There was no statistical difference in operative time and the overall complication rate between the two groups. The liver transection line was clearly delineated by the LHNS in 27 patients; however, the projection of boundary was unclear in 2 cases, and in 1 case, the boundary was not clearly displayed by ICG fluorescence imaging.

Conclusions

We developed the LHNS to address limitations of current intraoperative imaging systems. The LHNS is hopefully to become a promising real-time navigation system for laparoscopic hepatectomy.

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Acknowledgments

The authors are grateful for the researchers of the Shenzhen Institute of Advanced Technology of the Chinese Academy of Science, and Professor Liu Lianxin of the First Affiliated Hospital of University of Science and Technology of China for their help in preclinical experiments of LHNS. We also want to thank Wen Sai, the research assistant of Department of Hepatobiliary Surgery in Zhujiang Hospital affiliated to Southern Medical University for her contribution to translation and proofreading of this paper.

Funding

This work was supported by the grants from the National Key R&D Program, China (No. 2016YFC0106500), the NSFC-GD Union Foundation, China (No. U1401254), the Major Instrument Project of National Natural Science Fund, China (No. 81627805), and National High Technology Research and Development Program of China (863 program, China) (Nos. 2006AA02Z346 and 2012AA021105).

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Author notes

  1. Peng Zhang, Huoling Luo and Wen Zhu contributed equally to this work.

Authors and Affiliations

  1. The First Department of Hepatobiliary Surgery, Guangdong Provincial Clinical and Engineering Center of Digital Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China

    Peng Zhang, Wen Zhu, Jian Yang, Ning Zeng, Yingfang Fan, Sai Wen, Nan Xiang & Chihua Fang

  2. Research Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Huoling Luo & Fucang Jia

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  1. Peng Zhang
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Correspondence to Nan Xiang, Fucang Jia or Chihua Fang.

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Peng Zhang, Huoling Luo, Wen Zhu, Jian Yang, Ning Zeng, Yingfang Fan, Sai Wen, Nan Xiang, Fucang Jia, and Chihua Fang have no conflicts of interest or financial ties to disclose.

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Zhang, P., Luo, H., Zhu, W. et al. Real-time navigation for laparoscopic hepatectomy using image fusion of preoperative 3D surgical plan and intraoperative indocyanine green fluorescence imaging. Surg Endosc 34, 3449–3459 (2020). https://doi.org/10.1007/s00464-019-07121-1

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