Enhancing CBCT-Guided Lung Nodule Puncture Efficiency With Generative AI
Phase: NA
Conditions: Lung Nodules
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

 
 
STUDY PROPTOCOL 
Generative AI–Powered Navigation System for Precision Puncture: A Multicenter 
Randomized Controlled Trial Evaluating Efficacy and Safety 
Co-Principal Investigators: Bo Du, Huangxuan Zhao 
Data: 17 June 2025 
Principal Investigators 
Bo Du, PhD 
School of Computer Science, Wuhan 
University. 
Location: 
Wuhan, 
Hubei 
Province, 
China. 
Email: dubo@whu.edu.cn. 
 
Huangxuan Zhao, PhD 
School of Computer Science, Wuhan 
University/ 
Union 
Hospital, Tongji 
Medical College, Huazhong University 
of Science and Technology. 
Location: 
Wuhan, 
Hubei 
Province, 
China. 
Location: 
Wuhan, 
Hubei 
Province, 
China. 
Email: zhao_huangxuan@sina.com 
Other Investigators 
Lei Chen, MD 
Department 
of 
Radiology, 
Union 
Hospital, 
Tongji 
Medical 
College, 
Huazhong University of Science and 
Technology. 
Location: 
Wuhan, 
Hubei 
Province, 
China. 
Email: chan0812@126.com. 
Version 
V 2.0 
 
 
 
 
 
 
 
 
 
 

Study Protocol 
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Contents 
Project Proposal Signature Page ........................................................................................................................3 
1 Introduction and rationale ................................................................................................................................4 
2 Objectives ............................................................................................................................................................4 
3 Study design ........................................................................................................................................................4 
4 Study population ................................................................................................................................................5 
5 Study process ......................................................................................................................................................5 
6 Randomization and binding ............................................................................................................................6 
6.1 Randomization .......................................................................................................................................6 
6.2 Binding ....................................................................................................................................................6 
7 Observation outcomes ......................................................................................................................................6 
7.1 Primary outcome ...................................................................................................................................6 
7.2 Secondary outcomes .............................................................................................................................6 
7.3 Other outcomes......................................................................................................................................7 
8 Complications .....................................................................................................................................................7 
9 Sample size estimation .....................................................................................................................................7 
10 Data analysis program ....................................................................................................................................8 
11 Data management and monitoring ...............................................................................................................8 
12 Ethical considerations ....................................................................................................................................9 
12.1 Ethical approved .................................................................................................................................9 
12.2 Informed consent ................................................................................................................................9 
13 Schedule of the trial ........................................................................................................................................9 
14 Discussion .........................................................................................................................................................9 
15 References ...................................................................................................................................................... 10 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Study Protocol 
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Project Proposal Signature Page 
Project Title: Generative AI–Powered Navigation System for Precision Puncture: A 
Multicenter Randomized Controlled Trial Evaluating Efficacy and Safety 
 
Version Number / V2.0 
 
I have read and understood this research proposal and confirm that it includes the 
necessary content for conducting the study, clearly outlining the responsibilities of 
researchers involved in this project. As the Principal Investigator, I will provide all 
participating researchers with a copy of this proposal and all relevant materials. I will 
discuss these materials with them to ensure they fully understand the implementation 
of this research proposal. I agree to strictly adhere to all applicable laws and regulations, 
the Declaration of Helsinki, Good Clinical Practice guidelines, and this research 
proposal in fulfilling my responsibilities. 
 
Principal Investigator (Print): Bo Du; Zhao Huangxuan   
Principal Investigator Signature:  
 
 
 
Clinical Research Institution:   
Union Hospital, Tongji Medical College, Huazhong University of Science and 
Technology 
 
 
 

Study Protocol 
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1 Introduction and rationale 
Lung nodules are highly prevalent in the population, and percutaneous lung puncture 
(PLP) is one of the gold standards for the qualitative diagnosis of lung nodules1,2. Cone-
beam CT (CBCT) plays a critical role in guiding percutaneous lung puncture3-5. 
However, the poor quality of CBCT imaging can complicate the procedure, especially 
in certain patient populations. Improving the quality of CBCT images may increase the 
success rate of CBCT-guided lung nodule puncture procedures, reduce radiation 
exposure, and minimize the number of punctures. Although several previous studies 
have used generative deep learning methods to improve CBCT image quality6-8, no 
prospective clinical trials have yet validated the practical application of these models. 
In this study, a Generative AI-Powered Puncture Surgery Navigation System for lung 
punctures (GPS-Lung) systems constructed based on the synthesis CT (sCT) images 
and intraoperative guidance system, and a prospective cohort is used to validate its 
efficacy in clinical practice. 
2 Objectives 
- To construct a GPS-Lung system to improve CBCT imaging quality in clinical practice 
and evaluate the integration of GPS-Lung system to guide lung puncture surgery, with 
the goal of improving puncture surgery efficiency, reducing radiation exposure, and 
minimizing procedural complications. 
3 Study design 
This study used a prospective randomized controlled study design. Participants 
scheduled to undergo percutaneous lung puncture were included and randomly assigned 
in a 1:1 ratio to either the GPS-Lung system guided puncture group or the CBCT-guided 
puncture group. 

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Study type: Prospective randomized controlled trial. 
Study schedule: January 2025-May 2025 (5 months total). 
Study location: Three Level 3A hospitals in China  (Wuhan Union Hospital, Wuhan 
Union West Hospital, and Wuhan Union Jinyinhu Hospital). 
4 Study population 
4.1 Study population 
Participants scheduled to undergo CBCT-guided lung puncture. 
4.2 Inclusion criteria 
- Participants require CBCT-guided PLP and meet the indications for the procedure. 
- Participants have pre-operation CT images. 
- Participant’s physical condition is suitable for PLP. 
4.3 Exclusion criteria 
- Participants have a history of allergy or serious adverse reaction to iodine contrast 
media or other related drugs. 
- Participants are pregnant or breastfeeding. 
- Participants are unwilling or unable to provide informed consent. 
5 Study process 
Group: A Web-based platform was used to randomize patients into experimental group 
or control group. 
Experimental group: Percutaneous lung puncture procedure using GPS-Lung system 
guidance. 
Control group: Percutaneous lung puncture procedure using CBCT guidance. 
Observation indicators: The number of puncture needles, radiation dose, puncture 
time and intraoperative and postoperative complications within 7 days. 
 

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6 Randomization and binding 
6.1 Randomization 
Randomization 
was 
performed 
using 
a 
Web-based 
system 
(https://www.randomizer.org/#randomize) 
that 
employed 
computer-generated, 
permuted-block sequences, with stratification by center. Notably, the Web-based central 
randomization service ensured concealment of trial-group assignments.  
6.2 Binding 
This study conducted a double-blind design to ensure that neither participants nor 
researchers were aware of the group assignments throughout the study. Participants,  
researchers, data collectors, and outcome assessors were blinded to the group allocation. 
Statisticians and authors were unaware of the subgroups during data analysis. Doctors, 
technicians and trial nurses were aware of the subgroups but were not involved in data 
collection or analysis. 
For image quality and lesion quality scoring, we will give a mixture of CBCT, sCT, and 
CT images to the scorers, and they unaware of the images type. 
7 Observation outcomes 
7.1 Primary outcome 
Puncture times of participants for GPS-Lung system and CBCT-guided lung puncture 
procedures. Puncture times was defined as the number of punctures performed 
throughout the PLP procedure. 
7.2 Secondary outcomes 
Radiation dose, duration of surgery and intraoperative and postoperative complications 

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within 7 days of participants for GPS-Lung system and CBCT-guided lung puncture 
procedures. 
7.3 Other outcomes 
Algorithmic performance for image quality enhancement and doctors’ scoring of image 
and lesion quality. 
8 Complications 
Potential complications are listed below: 
Pneumothorax: Air may enter the pleural cavity after a lung puncture, causing the lung 
atrophy. 
Bleeding: Puncture of lung tissue may result in bleeding within the lung or chest cavity. 
Pneumonia: The puncture procedure may trigger an infection, which can result in 
pneumonia. 
Atelectasis: Airway obstruction or atrophy of lung tissue may cause partial atelectasis. 
Intercostal neuralgia: The puncture procedure may irritate the intercostal nerves, 
resulting in postoperative pain. 
Lung abscess: Both intraoperative and postoperative infections may lead to localized 
abscess formation. 
Air embolism: Intraoperative entry of puncture gas into the vessel resulting in air 
embolism. 
9 Sample size estimation 
The sample size calculation in this study was estimated based on the number of 
intraoperative lung punctures. A total of 38 participants received CBCT-guided PLP 
from retrospective cohort were included from Wuhan Union Hospital. The mean (SD) 
puncture times of these patients were 2.3±2.2. We used a 2.5% one-sided significance 

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level with 90% efficacy for sample allocation on a 1:1 basis. The sample size was 
calculated based on the following formula: 
𝑛= 2(𝑍𝛼/2 + 𝑍𝛽/2)2 ∗𝜎2
𝛥2
 
n represents the sample size of each group. Zα/2 represents the critical value of the 
significance level (α), and the α=0.025 in the study. Zβ/2 represents the critical value of 
efficacy (1 - β), and the β=0.9 in the study. σ2 represents the within-group variance. In 
the current, σ was substituted by the standard deviation of puncture times from 38 
participants. Δ represents the smallest difference between the means of the two groups. 
In the current, the Δ was considered as 1. Therefore, the sample size to be included in 
this study for each group is 103. We will included 220 participants (each group 110 
participants). 
10 Data analysis program 
The baseline comparisons between the two groups, appropriate statistical tests will be 
employed based on the type of data (parametric or non-parametric). Categorical 
variables will be analyzed using the Chi-squared test or Fisher’s exact test, while 
continuous variables will be analyzed using the t-test or the Mann-Whitney U test. 
Paired t-tests will be used to compare the means of the scores for overall image quality 
and lesion visualization between paired groups. The weighted kappa coefficient (κ) or 
kendall coefficient of concordance (W) or intra-class correlation coefficient (ICC) will 
be used to assess the agreement of the image quality of all samples and the lesions score 
of sCT among different readers. 
11 Data management and monitoring 
Data management: All data will be anonymized and stored in a secure, specialized 
database (Picture Archiving and Communication Systems, PACS) to ensure data 
integrity and confidentiality. Researchers can access it in PACS. Data will be kept for a 

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period of 15 years. Imaging data is evaluated independently by an imaging assessment 
team. 
Data monitoring: An independent Data Safety Monitoring Committee (DMC) will be 
established to regularly review the progress, safety, and overall quality of the trial. 
12 Ethical considerations 
12.1 Ethical approved 
The study will be registered with the Clinical Trial Registry. This study has been 
approved by the ethics committees of the Wuhan Union Hospital (Reference number: 
UHCT-IEC-SOP-016-03-01).  
12.2 Informed consent 
All participants will be requested to sign an informed consent form prior to participation 
in the trial. The consent form will provide the participants with a clear explanation of 
the purpose of the study, potential risks, and possible benefits. Participants have the 
right to withdraw from the study at any time, without any impact on their subsequent 
treatment. 
13 Schedule of the trial 
January-March 2025: Trial initiated, ethical approval granted, and patient recruitment 
initiated. Data collection for participant recruitment and scanning completed. 
April-May 2025: Data analysis and summary report. 
14 Discussion 
In this study, the efficacy of the GPS-Lung system in guiding percutaneous lung 

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puncture procedures was comprehensively evaluated through a prospective randomized 
controlled trial. The expected results will provide strong evidence for the clinical 
application of GPS-Lung system to improve the success rate of percutaneous lung 
puncture procedures, while reducing the radiation exposure and minimize 
complications for patients. 
 
15 References 
1 
Mazzone, P. J. & Lam, L. Evaluating the Patient With a Pulmonary Nodule: A Review. Jama 
327, 264-273 (2022). https://doi.org/10.1001/jama.2021.24287 
2 
Steiling, K. et al. Research Priorities for Interventions to Address Health Disparities in Lung 
Nodule Management: An Official American Thoracic Society Research Statement. Am J Respir 
Crit Care Med 207, e31-e46 (2023). https://doi.org/10.1164/rccm.202212-2216ST 
3 
Rotolo, N. et al. Comparison of cone-beam CT-guided and CT fluoroscopy-guided transthoracic 
needle 
biopsy 
of 
lung 
nodules. 
European 
radiology 
26, 
381-389 
(2016). 
https://doi.org/10.1007/s00330-015-3861-6 
4 
Choo, J. Y. et al. Percutaneous transthoracic needle biopsy of small (≤ 1 cm) lung nodules under 
C-arm cone-beam CT virtual navigation guidance. European radiology 23, 712-719 (2013). 
https://doi.org/10.1007/s00330-012-2644-6 
5 
Ren, Q. et al. Imaging-guided percutaneous transthoracic needle biopsy of nodules in the lung 
base: fluoroscopy CT versus cone-beam CT. Clin Radiol 77, e394-e399 (2022). 
https://doi.org/10.1016/j.crad.2022.02.005 
6 
Deng, L., Ji, Y., Huang, S., Yang, X. & Wang, J. Synthetic CT generation from CBCT using 
double-chain-CycleGAN. Computers in biology and medicine 161, 106889 (2023). 
https://doi.org/10.1016/j.compbiomed.2023.106889 
7 
Li, Z. et al. Using RegGAN to generate synthetic CT images from CBCT images acquired with 
different linear accelerators. BMC Cancer 23, 828 (2023). https://doi.org/10.1186/s12885-023-
11274-7 
8 
Suwanraksa, C., Bridhikitti, J., Liamsuwan, T. & Chaichulee, S. CBCT-to-CT Translation Using 
Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. 
Cancers 15 (2023). https://doi.org/10.3390/cancers15072017