Volume 11, Issue 9 pp. 2690-2697
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Three-dimensional printed navigational template for localizing small pulmonary nodules: A case-controlled study

Rui Fu MD

Rui Fu MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

These authors contributed equally to this work.

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Yun-Fei Chai MD

Yun-Fei Chai MD

Anesthesiology Department of Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

These authors contributed equally to this work.

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Jia-Tao Zhang MD

Jia-Tao Zhang MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Tao Zhang MD

Tao Zhang MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Xiao-Kun Chen MD

Xiao-Kun Chen MD

3D Printing Joint Laboratory of Cardiovascular Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Song Dong MD

Song Dong MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Hong-Hong Yan MD

Hong-Hong Yan MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Xue-Ning Yang MD, PhD

Xue-Ning Yang MD, PhD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Mei-Ping Huang MD

Mei-Ping Huang MD

Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Yi-Long Wu MD

Yi-Long Wu MD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Jian Zhuang MD

Jian Zhuang MD

Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

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Wen-Zhao Zhong MD, PhD

Corresponding Author

Wen-Zhao Zhong MD, PhD

Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

Correspondence

Wen-Zhao Zhong, MD, PhD, Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China.

Tel: 86-20-83877855

Fax: 86-20-83844620

Email: [email protected]

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First published: 19 July 2020
Citations: 6

Abstract

Background

Localization of small pulmonary nodules is an inevitable challenge for the thoracic surgeon. This study aimed to investigate the accuracy of three-dimensional (3D) printing technology for localizing small pulmonary nodules, especially ground-glass nodules (GGNs).

Methods

This study enrolled patients with peripheral small pulmonary nodules (≤ 2 cm) who required preoperative localization. In the comparison period, patients underwent both computed tomography-guided (CT-G) and 3D-printing template guided (3D-G) localization to compare the accuracies of the two methods. In the testing period, the 3D-printing technique was implemented alone. The 3D-printing physical navigational template was designed based on data from perioperative CT images. Clinical data, imaging data, surgical data, and evaluation index were collected for further analysis. The learning curve of the 3D-printing localization technique was assessed using cumulative sum (CUSUM) analysis and multiple linear regression analysis.

Results

In the comparison period (n = 14), the success rates of CT-G and 3D-G were 100% and 92.9% (P = 0.31), respectively; in the testing period (n = 23), the success rate of 3D-G was 95.6%. The localization times of CT-G, 3D-G (comparison), and 3D-G (testing) were 23.6 ± 5.3, 19.3 ± 6.8, and 9.8 ± 4.6 minutes, respectively. The CUSUM learning curve was modeled using the equation: Y = 0.48X2 0.013X 0.454 (R2 = 0.89). The learning curve was composed of two phases, phase 1 (the initial 20 patients) and phase 2 (the remaining 17 patients).

Conclusions

3D printing localization has adequate accuracy and is a feasible and accessible strategy for use in localizing small pulmonary nodules, especially in right upper lobe. The use of this technique could facilitate lung nodule localization prior to surgery.

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