Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound

NCT06423066 | Not Yet Recruiting

• 1 other identifier: USPDPA01
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to investigate the accuracy of using pleural ultrasound (USP) to identify pleural adhesions in patients who plan to receive video-assisted thoracoscopic surgery. It employs three-dimensional convolutional neural network (3D-CNN) technology to process USP-related images and video data for machine learning, and to establish a diagnostic model for identifying pleural adhesions using 3D-CNN-USP. The study will determine the sensitivity, specificity, positive predictive value, and negative predictive value of 3D-CNN-USP in identifying pleural adhesions. Additionally, it will explore the feasibility and effectiveness of using 3D-CNN-USP for preoperative identification of pleural adhesions in VATS, thereby supporting the implementation of day surgery in thoracic surgery and ultimately serving clinical practice.

Conditions

Pleural Diseases; Lung Cancer; Machine Learning

Keywords

pleural adhesion; 3D-CNN; machine learning; VATS

Outcome Measures

Primary Outcomes (1)

• Sensitivity

  Sensitivity of three-dimensional convolutional neural network (3D-CNN) in identifying pleural adhesions using pleural ultrasound (USP). The sensitivity value ranges from 0 to 100, with higher values indicating greater sensitivity.

  From enrollment to the end of surgery.

Secondary Outcomes (3)

• Specificity

  From enrollment to the end of surgery.

• Positive predictive value

  From enrollment to the end of surgery.

• Negative predictive value

  From enrollment to the end of surgery.

Study Arms (1)

Pleural ultrasound group

Patients who accept pleural ultrasound preoperatively.

Diagnostic Test: Pleural ultrasound

Interventions

Pleural ultrasound DIAGNOSTIC_TEST

Patients who examine pleural ultrasound preoperatively.

Pleural ultrasound group

Eligibility Criteria

• Age: 12 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who plan to accept VATS surgery in Peking Union Medical college Hospital, and agree to perform preoperative plerual ultrasound examination.

You may qualify if:

• \. Patients who plan to accept VATS surgery.

You may not qualify if:

• Patients who can not obtain detailed clinical information;
• Patients or their family members who can not understand the conditions and objectives of the study or refuse to participate in the study;
• Patients with conditions affecting observation, such as skin lesions, infections, or scars in the area of the chest wall to be examined.

Sponsors & Collaborators

• Peking Union Medical College Hospital: lead

Study Sites (1)

Peking Union Medical College Hospital

Beijing, Beijing Municipality, 100730, China

Location

Related Publications (2)

• Mason AC, Miller BH, Krasna MJ, White CS. Accuracy of CT for the detection of pleural adhesions: correlation with video-assisted thoracoscopic surgery. Chest. 1999 Feb;115(2):423-7. doi: 10.1378/chest.115.2.423.

  PMID: 10027442 BACKGROUND

• Cassanelli N, Caroli G, Dolci G, Dell'Amore A, Luciano G, Bini A, Stella F. Accuracy of transthoracic ultrasound for the detection of pleural adhesions. Eur J Cardiothorac Surg. 2012 Nov;42(5):813-8; discussion 818. doi: 10.1093/ejcts/ezs144. Epub 2012 Apr 19.

  PMID: 22518039 BACKGROUND

MeSH Terms

Conditions

Pleural Diseases; Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases

Study Officials

• Shanqing Li, PhD, and MD

  Peking Union Medical College Hospital

  STUDY DIRECTOR

• Qingli Zhu, PhD, and MD

  Peking Union Medical College Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xuehan Gao, MD

CONTACT

18801341299; gaoxh1299@163.com

Yuanjing Gao, MD

CONTACT

16619765781; yuanjing1997@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Investigator

Study Record Dates

• First Submitted: May 15, 2024
• First Posted: May 21, 2024
• Study Start: June 1, 2024
• Primary Completion: December 30, 2025
• Study Completion: March 30, 2026
• Last Updated: May 21, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)