Evidence map›Paper›PMID 38090522›Full record

ArticleTranslational lung cancer research2023

Robotic-assisted navigation system for preoperative lung nodule localization: a pilot study.

Jun Liu, Yu Jiang, Rui He, Fei Cui, Yuechun Lin, Ke Xu, Weipeng Cai, Zhexue Hao, Yuan Zeng, Xiangqian Chen and 12 more

Abstract read
In one paragraph

Article in Translational lung cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Trial
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  7. [Expert Consensus on Precision Management of Pulmonary Nodules (2026 Version)].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026
    Article
  8. Article
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  10. Article
  11. Review
  12. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Jun Liu *Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Yu Jiang *Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Rui He *School of Mechatronical Engineering, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing, China.
Fei Cui *Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Yuechun LinDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Ke XuDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Weipeng CaiDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Zhexue HaoDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Yuan ZengDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Xiangqian ChenTrue Health Medical Technology Co. Ltd., Hengqin, China.
Hao ZhangTrue Health Medical Technology Co. Ltd., Hengqin, China.
Jipeng ShiTrue Health Medical Technology Co. Ltd., Hengqin, China.
Houiam CheongTrue Health Medical Technology Co. Ltd., Hengqin, China.
Mengxing DongTrue Health Medical Technology Co. Ltd., Hengqin, China.
Kaicheng UCornell University, Ithaca, NY, USA.
Xiaoyan HuangDepartments of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shunjun JiangDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Jun HuangDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Wei WangDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Hengrui LiangDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Xingguang DuanSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing, China.
Jianxing HeDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Preoperative percutaneous computed tomography (CT)-guided localization of pulmonary nodules plays a pivotal role in the diagnosis and treatment of early-stage lung cancer. However, conventional manual localization techniques have inherent limitations in achieving a high degree of accuracy. Consequently, a novel robotic-assisted navigation system was developed to attain precise localization of small lung nodules. This study aims to investigate the accuracy and safety of this system in clinical applications. Methods: Patients with peripheral solitary pulmonary nodules measuring less than 20 mm were enrolled. The robotic-assisted navigation system generated a three-dimensional (3D) model based on the patient's CT images, determining the optimal puncture path. The robotic arm then accurately located the nodule and, following percutaneous puncture, indocyanine green (ICG) was injected. The primary outcome measure was the accuracy of pulmonary nodule localization, while secondary outcomes included the complication rate, procedural duration, and total radiation exposure. Results: A total of 33 nodules were successfully localized using the robotic-assisted navigation system and resected through video-assisted thoracoscopic surgery (VATS). The first-pass success rate was 100%, with a median deviation of 6.1 mm [interquartile range (IQR), 2.5-7.2 mm] between the localizer and the nodule. The median localization time was 25.0 minutes, and the single and cumulative exam dose-length products (DLP) were 534.0 and 1491.0 mGy·cm, respectively. Notably, no observable complications were reported during the procedures. Conclusions: The innovative robotic-assisted navigation system demonstrated satisfactory accuracy and holds promise for improving the percutaneous localization of lung nodules. This method represents a safe and viable alternative to traditional CT-guided manual localization techniques.

Indexed as

lung nodulenavigation systempreoperative localizationRobotic-assistedvideo-assisted thoracoscopic surgery (VATS)

Identifiers

PMID38090522
PMCPMC10713270

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.