Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes
NCT ID: NCT04328792
Last Updated: 2020-04-02
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
UNKNOWN
1300 participants
OBSERVATIONAL
2018-07-01
2020-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
A Study of Contrast-enhanced EBUS in Lung Lesions and Intrathoracic Lymph Nodes
NCT07060378
Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence
NCT05739331
Prediction Model of Peripheral Pulmonary Lesions Based on R-EBUS Image
NCT04497233
EBUS/Spectrum Analysis
NCT01972386
Endobronchial Ultrasound Guided Transbronchial Aspiration (EBUS-TBNA) in Non Small Cell Lung Cancer (NSCLC) in a Tuberculosis-endemic Country
NCT01156623
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
EBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study.
During the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected.
Investigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
OTHER
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Prospectively validation group
Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy.
No interventions assigned to this group
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
2. Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
3. Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.
Exclusion Criteria
18 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Shanghai Chest Hospital
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Jiayuan Sun
Director,Department of Respiratory Endoscopy ,Shanghai Chest Hospital
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Jiayuan Sun, MD, PhD
Role: STUDY_DIRECTOR
Shanghai Chest Hospital
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Shanghai Chest Hospital
Shanghai, Shanghai Municipality, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
References
Explore related publications, articles, or registry entries linked to this study.
Steinfort DP, Conron M, Tsui A, Pasricha SR, Renwick WE, Antippa P, Irving LB. Endobronchial ultrasound-guided transbronchial needle aspiration for the evaluation of suspected lymphoma. J Thorac Oncol. 2010 Jun;5(6):804-9. doi: 10.1097/jto.0b013e3181d873be.
Sun J, Teng J, Yang H, Li Z, Zhang J, Zhao H, Garfield DH, Han B. Endobronchial ultrasound-guided transbronchial needle aspiration in diagnosing intrathoracic tuberculosis. Ann Thorac Surg. 2013 Dec;96(6):2021-7. doi: 10.1016/j.athoracsur.2013.07.005. Epub 2013 Sep 12.
Fujiwara T, Yasufuku K, Nakajima T, Chiyo M, Yoshida S, Suzuki M, Shibuya K, Hiroshima K, Nakatani Y, Yoshino I. The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system. Chest. 2010 Sep;138(3):641-7. doi: 10.1378/chest.09-2006. Epub 2010 Apr 9.
Nakajima T, Anayama T, Shingyoji M, Kimura H, Yoshino I, Yasufuku K. Vascular image patterns of lymph nodes for the prediction of metastatic disease during EBUS-TBNA for mediastinal staging of lung cancer. J Thorac Oncol. 2012 Jun;7(6):1009-14. doi: 10.1097/JTO.0b013e31824cbafa.
Wang L, Wu W, Hu Y, Teng J, Zhong R, Han B, Sun J. Sonographic Features of Endobronchial Ultrasonography Predict Intrathoracic Lymph Node Metastasis in Lung Cancer Patients. Ann Thorac Surg. 2015 Oct;100(4):1203-9. doi: 10.1016/j.athoracsur.2015.04.143. Epub 2015 Jul 28.
Izumo T, Sasada S, Chavez C, Matsumoto Y, Tsuchida T. Endobronchial ultrasound elastography in the diagnosis of mediastinal and hilar lymph nodes. Jpn J Clin Oncol. 2014 Oct;44(10):956-62. doi: 10.1093/jjco/hyu105. Epub 2014 Aug 13.
Saftoiu A, Vilmann P, Gorunescu F, Janssen J, Hocke M, Larsen M, Iglesias-Garcia J, Arcidiacono P, Will U, Giovannini M, Dietrich C, Havre R, Gheorghe C, McKay C, Gheonea DI, Ciurea T; European EUS Elastography Multicentric Study Group. Accuracy of endoscopic ultrasound elastography used for differential diagnosis of focal pancreatic masses: a multicenter study. Endoscopy. 2011 Jul;43(7):596-603. doi: 10.1055/s-0030-1256314. Epub 2011 Mar 24.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
SHCHE201906
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.