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

NCT04328792 | Unknown DMC

• 1 other identifier: SHCHE201906
• Study Type: observational
• Target: 1,300
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Lymph Node Disease

Keywords

EBUS-TBNA; Intrathoracic lymph node; Multimodal image; Deep learning; Prediction model

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Diagnostic efficacy of traditional qualitative and quantitative methods

  6 months post-procedure

• Diagnostic efficacy of multimodal deep learning model based on images

  6 months post-procedure

• Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods

  6 months post-procedure

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with enlarged intrathoracic LNs that need to be diagnosed by EBUS-TBNA are enrolled in this study.

You may qualify if:

• 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;
• Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;
• Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.

You may not qualify if:

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

Sponsors & Collaborators

• Shanghai Chest Hospital: lead

Study Sites (1)

Shanghai Chest Hospital

Shanghai, Shanghai Municipality, 200030, China

RECRUITING

Related Publications (8)

• 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.

  PMID: 20521347 BACKGROUND

• 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.

  PMID: 24035300 BACKGROUND

• 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.

  PMID: 20382710 BACKGROUND

• 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.

  PMID: 22525556 BACKGROUND

• 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.

  PMID: 26228606 BACKGROUND

• 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.

  PMID: 25121724 BACKGROUND

• 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.

  PMID: 21437851 BACKGROUND

• 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.

  PMID: 27898976 BACKGROUND

Study Officials

• Jiayuan Sun, MD, PhD

  Shanghai Chest Hospital

  STUDY DIRECTOR

Central Study Contacts

Jiayuan Sun, MD, PhD

CONTACT

86-21-22200000; jysun1976@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director,Department of Respiratory Endoscopy ,Shanghai Chest Hospital

Study Record Dates

• First Submitted: December 2, 2019
• First Posted: March 31, 2020
• Study Start: July 1, 2018
• Primary Completion: June 30, 2020
• Study Completion: December 31, 2020
• Last Updated: April 2, 2020

Record last verified: 2020-03

Locations

CN (1)