Literature DB >> 28577725

Postoperative liver volume was accurately predicted by a medical image three dimensional visualization system in hepatectomy for liver cancer.

Wei Cai1, Yingfang Fan2, Haoyu Hu2, Nan Xiang2, Chihua Fang3, Fucang Jia4.   

Abstract

BACKGROUND: Liver cancer is the second most common cause of cancer death worldwide. The hepatectomy is the most effective and the only potentially curative treatment for patients with resectable neoplasm. Precisely preoperative assessment of remnant liver volume is essential in preventing postoperative liver failure. The aim of our study is to report our experience of using a medical image three dimensional (3D) visualization system (MI-3DVS), which was developed by our team, in assisting hepatectomy for patients with liver cancer.
METHODS: Between January 2010 and June 2016, 69 patients with liver cancer underwent hepatic resection based on the MI-3DVS were enrolled in this study. All patients underwent CT scan 5 days before the surgery and within 5 days after resection. CT images were reconstructed with the MI-3DVS to assist to perform hepatectomy. Simple linear regression, intra-class correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate the relationship and agreement between actual excisional liver volume (AELV) and predicted excisional liver volume (PELV).
RESULTS: Among 69 patients in this study, 62(89.85%) of them were diagnosed with hepatocellular carcinoma by histopathologic examination, and 41(59.42%) underwent major hepatectomy. The average AELV was 330.13 cm3 and the average PELV was 287.67 cm3. The simple regression equation is AELV = 1.016 × PELV+30.39(r = 0.966; p < 0.0003). PELV (ICC = 0.964) achieved an excellent agreement with AELV with statistical significance (p < 0.001). 65 of 69 dots are in the range of 95% confidence interval in Bland-Altman analyses.
CONCLUSIONS: The MI-3DVS has advantages of simple usage and convenient hold. It is accurate in assessment of postoperative liver volume and improve safety in liver resection.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Liver resection; Liver volume; Surgical planning

Mesh:

Year:  2017        PMID: 28577725     DOI: 10.1016/j.suronc.2017.03.006

Source DB:  PubMed          Journal:  Surg Oncol        ISSN: 0960-7404            Impact factor:   3.279


  10 in total

1.  The anatomical configuration of the splenic artery influences suprapancreatic lymph node dissection in laparoscopic gastrectomy: analysis using a 3D volume rendering program.

Authors:  Chunchao Zhu; Seong-Ho Kong; Tae-Han Kim; Shin-Hoo Park; Rene Ronson G Ang; Michele Diana; Luc Soler; Yun-Suhk Suh; Hyuk-Joon Lee; Jacques Marescaux; Hui Cao; Han-Kwang Yang
Journal:  Surg Endosc       Date:  2018-05-03       Impact factor: 4.584

2.  Diagnostic accuracy of 3D imaging combined with intra-operative ultrasound in the prediction of post-hepatectomy liver failure.

Authors:  Tianchong Wu; Wenhao Huang; Baochun He; Yuehua Guo; Gongzhe Peng; Mingyue Li; Shiyun Bao
Journal:  J Gastrointest Oncol       Date:  2022-06

3.  Influence of Three-Dimensional Visual Reconstruction Technology Combined with Virtual Surgical Planning of CTA Images on Precise Resection of Liver Cancer in Hepatobiliary Surgery.

Authors:  Yuanyu Zhao; Ting Chen; Hui Wang; Qiang Xue; Wenyuan Guo; Guoshan Ding; Junfeng Dong; Junsong Ji
Journal:  Comput Math Methods Med       Date:  2022-07-07       Impact factor: 2.809

4.  Using virtual 3D-models in surgical planning: workflow of an immersive virtual reality application in liver surgery.

Authors:  Christian Boedecker; Florentine Huettl; Patrick Saalfeld; Markus Paschold; Werner Kneist; Janine Baumgart; Bernhard Preim; Christian Hansen; Hauke Lang; Tobias Huber
Journal:  Langenbecks Arch Surg       Date:  2021-03-12       Impact factor: 3.445

5.  Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases.

Authors:  Chihua Fang; Jihyun An; Antonio Bruno; Xiujun Cai; Jia Fan; Jiro Fujimoto; Rita Golfieri; Xishan Hao; Hongchi Jiang; Long R Jiao; Anand V Kulkarni; Hauke Lang; Cosmas Rinaldi A Lesmana; Qiang Li; Lianxin Liu; Yingbin Liu; Wanyee Lau; Qiping Lu; Kwan Man; Hitoshi Maruyama; Cristina Mosconi; Necati Örmeci; Michael Pavlides; Guilherme Rezende; Joo Hyun Sohn; Sombat Treeprasertsuk; Valérie Vilgrain; Hao Wen; Sai Wen; Xianyao Quan; Rafael Ximenes; Yinmo Yang; Bixiang Zhang; Weiqi Zhang; Peng Zhang; Shaoxiang Zhang; Xiaolong Qi
Journal:  Hepatol Int       Date:  2020-07-07       Impact factor: 6.047

Review 6.  Role of artificial intelligence in hepatobiliary and pancreatic surgery.

Authors:  Hassaan Bari; Sharan Wadhwani; Bobby V M Dasari
Journal:  World J Gastrointest Surg       Date:  2021-01-27

Review 7.  Chinese guidelines on the management of liver cirrhosis (abbreviated version).

Authors:  Xiao-Yuan Xu; Hui-Guo Ding; Wen-Gang Li; Jing-Hang Xu; Ying Han; Ji-Dong Jia; Lai Wei; Zhong-Ping Duan; En-Qiang Ling-Hu; Hui Zhuang
Journal:  World J Gastroenterol       Date:  2020-12-07       Impact factor: 5.742

8.  Three-dimensional versus two-dimensional video-assisted hepatectomy for liver disease: a meta-analysis of clinical data.

Authors:  Shumao Zhang; Zhanwen Huang; Liang Cai; Wei Zhang; Haoyuan Ding; Li Zhang; Yue Chen
Journal:  Wideochir Inne Tech Maloinwazyjne       Date:  2020-11-05       Impact factor: 1.195

9.  Feasibility Study of Intelligent Three-Dimensional Accurate Liver Reconstruction Technology Based on MRI Data.

Authors:  Shaodong Cao; Huan Li; Suyu Dong; Zhenxuan Gao
Journal:  Front Med (Lausanne)       Date:  2022-03-17

10.  Application of 3D Visualization Technology in Complex Abdominal Wall Defects.

Authors:  Zhicheng Song; Wenpei Dong; Dongchao Yang; Jianjun Yang; Jugang Wu; Yiping Wang; Yan Gu
Journal:  Int J Gen Med       Date:  2021-06-10
  10 in total

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