Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes

Lymph Node Disease Clinical Trial

Official title:

Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04328792
• Other study ID #: SHCHE201906
• Status: Recruiting
• Start date: July 1, 2018
• Est. completion date: December 31, 2020

Study information

• Verified date: March 2020
• Source: Shanghai Chest Hospital
• Contact: Jiayuan Sun, MD, PhD
• Phone: 86-21-22200000
• Email: jysun1976[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Endobronchial ultrasound (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. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.

Description:

Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1300
• Est. completion date: December 31, 2020
• Est. primary completion date: June 30, 2020
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Chest CT shows enlarged intrathoracic LNs (short diameter > 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ? 2.0) in intrathoracic LNs;

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:

• Patients having other situations that are not suitable for EBUS-TBNA.

Related Conditions & MeSH terms

• Lymph Node Disease

Locations

Country	Name	City	State
China	Shanghai Chest Hospital	Shanghai	Shanghai

Sponsors (1)

Lead Sponsor	Collaborator
Shanghai Chest Hospital

Country where clinical trial is conducted

China, 

References & Publications (8)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos	Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.	6 months post-procedure
Secondary	Diagnostic efficacy of traditional qualitative and quantitative methods	Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.	6 months post-procedure
Secondary	Diagnostic efficacy of multimodal deep learning model based on images	Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.	6 months post-procedure
Secondary	Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods	Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.	6 months post-procedure