Evidence map›Paper›PMID 38106331›Full record

ArticleQuantitative imaging in medicine and surgery2023

Robot-assisted navigation for percutaneous localization of peripheral pulmonary nodule: an

Xingguang Duan, Rui He, Yu Jiang, Fei Cui, Hao Wen, Xiangqian Chen, Zhexue Hao, Yuan Zeng, Hui Liu, Jipeng Shi and 8 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
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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

18 authors.

Xingguang Duan *School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Rui He *School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 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.
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.
Hao WenSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Xiangqian ChenTrue Health Medical Technology Co. Ltd., Hengqin, 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.
Hui LiuDepartment of Anesthesia, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 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.
Shunjun JiangDepartments of Pharmacology, the First Affiliated Hospital of Guangzhou Medical University, 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 Liang *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.
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.
Jianxing He *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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robot-assisted surgery (RAS) systems have been developed but rarely applied to lung nodule localization. This study aimed to assess the feasibility and safety of using a robot-assisted navigation system in percutaneous lung nodule localization. Methods: A computed tomography (CT)-guided robot-assisted navigation system was used to localize the simulated peripheral nodule in the swine lung through fluorescent agent injection. After the localization, fluorescent thoracoscopic wedge resection was performed. The deviation between the target point and the needle tip was measured using a professional 3-dimensional (3D) distance measurement software. The primary outcome was the localization accuracy (deviation) of the localization. The secondary outcomes were the localization-related complication rate, the localization duration, and the success rate. Results: A total of 4 pigs were enrolled, and 20 peripheral lung nodules were created and localized successfully. All nodules underwent subsequent wedge resection for verification. The mean deviation by measuring the 3D distance was 3.81 mm [standard deviation (SD): 1.29 mm, 95% confidence interval (CI): 2.936-4.536 mm]. The technical success rate for localization was 100%, and the mean localization time was 14.69 minutes (SD: 4.67 minutes). The complication rate was 5% (1/20), with 1 pneumothorax after localization, and no mortality occurred. Conclusions: This pilot animal study demonstrated the promising potential of the robot-assisted navigation technique in peripheral lung nodule localization, with high accuracy and feasibility. Further clinical trials are needed to validate its safety compared to traditional manual localization.

Indexed as

localizationLung nodulesrobot-assisted navigation

Identifiers

PMID38106331
PMCPMC10721995

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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.