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

March 31, 2020 updated by: Jiayuan Sun, Shanghai Chest Hospital

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

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.

Study Overview

Status

Unknown

Conditions

Detailed 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.

Study Type

Observational

Enrollment (Anticipated)

1300

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Shanghai
      • Shanghai, Shanghai, China, 200030
        • Recruiting
        • Shanghai Chest Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos
Time Frame: 6 months post-procedure
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
6 months post-procedure

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic efficacy of traditional qualitative and quantitative methods
Time Frame: 6 months post-procedure
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
6 months post-procedure
Diagnostic efficacy of multimodal deep learning model based on images
Time Frame: 6 months post-procedure
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
6 months post-procedure
Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods
Time Frame: 6 months post-procedure
Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.
6 months post-procedure

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Chest Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 1, 2018

Primary Completion (Anticipated)

June 30, 2020

Study Completion (Anticipated)

December 31, 2020

Study Registration Dates

First Submitted

December 2, 2019

First Submitted That Met QC Criteria

March 29, 2020

First Posted (Actual)

March 31, 2020

Study Record Updates

Last Update Posted (Actual)

April 2, 2020

Last Update Submitted That Met QC Criteria

March 31, 2020

Last Verified

March 1, 2020

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • SHCHE201906

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

Investigators may release the database after the study, but no decision has been made yet.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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