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Poster Display session

217P - Robotic-assisted navigation system for preoperative percutaneous lung nodule localization: A pilot study

Date

31 Mar 2023

Session

Poster Display session

Presenters

Yuechun Lin

Citation

Journal of Hepatology (2023) 18 (4S): S154-S159.
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Authors

Y. Lin

Author affiliations

  • Guangzhou/CN

Resources

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Abstract 217P

Background

Computed tomography (CT)-guided percutaneous preoperative localization of pulmonary nodules is a key step in diagnosing and treating early-stage lung cancer, but traditional manual localization still lacks accuracy. Therefore, the novel robotic-assisted navigation system was developed for the precise localization of small lung nodules. This study aimed to investigate its accuracy and safety in clinical applications.

Methods

Patients with peripheral solitary pulmonary nodules (<2 cm) were enrolled. The robotic-assisted navigation system reconstructed a 3-dimensional (3D) model and calculated the desired path based on the CT image of the patient. The robotic arm then located the lung nodule according to the planned path, with the photoelectric system tracking the patient's position and respiratory motion in real time. The primary outcome was the accuracy of pulmonary nodule localization. Secondary outcomes included complication rate, procedural duration, and total radiation exposure.

Results

A total of 33 nodules were localized through the robotic-assisted navigation system. First-pass success rate was 100%, with a median deviation of 6.1 mm (range, 2.5–7.2 mm) between the localizer and the nodule center. The median localization time was 26.0 minutes, (range, 19.5 to 28.5 minutes), and the received radiation doses and dose-length product (DLP) were 1491.0 mAs (range, 1210.0–1781.5 mAs) and 534.0 mAs·cm (range, 374.0–645.0 mAs·cm), respectively. In our study, no observable complications occurred.

Conclusions

The novel robotic-assisted navigation system could optimize the process of percutaneous lung nodule localization. The method might be a safe and feasible alternative to traditional manual localization.

Legal entity responsible for the study

The author.

Funding

National Key Research and Development Program of China (2022YFB4702605).

Disclosure

The author has declared no conflicts of interest.

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