The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging

March 10, 2020 updated by: Wael Hanna, St. Joseph's Healthcare Hamilton

Development and Validation of a Computer-aided Algorithm Using Artificial Intelligence and Deep Neural Networks for the Segmentation of Ultrasonographic Features of Lymph Nodes During Endobronchial Ultrasound

This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration(EBUS-TBNA), using the technique of segmentation. Images will be created from 300 lymph nodes videos from a prospective library and will be used as a derivation set to develop the algorithm. An additional100 lymph node images will be prospectively collected to validate if NeuralSeg can correctly apply the score.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

52

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

  • Canada
    • Ontario
      • Hamilton, Ontario, Canada, L8N 4A6
        • St. Joseph's Healthcare Hamilton

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

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Phase A does not require patient enrollment. Phase B will require prospective enrollment of patients to obtain the validation set of new lymph node videos. All patients who are scheduled to undergo an EBUS-TBNA procedure for mediastinal staging of NSCLC at St. Joseph's Healthcare Hamilton will be eligible to enroll in this study. There are no exclusion criteria. All patients will undergo EBUS-TBNA as per routine care, except for the one difference where the procedures will be video-recorded so that they can be used for computer analysis at a later time.

Description

Inclusion Criteria:

  • must be diagnosed with confirmed or suspected lung cancer and be undergoing EBUS diagnosis/staging

Exclusion Criteria:

  • None

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Development of computer algorithm to identify lymph node ultrasonographic features
Time Frame: From retrospective data collection to algorithm development (1 month)
Objective: to determine whether a deep neural AI network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by EBUS, using the technique of segmentation on an existing (derivation) set of lymph node videos
From retrospective data collection to algorithm development (1 month)
Validation of computer algorithm to identify lymph node ultrasonographic features
Time Frame: From prospective data collection to algorithm validation (6 months)
Objective: to determine whether NeuralSeg can correctly apply the Canada Lymph Node Score to a new (validation) set of lymph node videos that it has never seen before
From prospective data collection to algorithm validation (6 months)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy and reliability of the segmentation performed by NeuralSeg
Time Frame: From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
Objective: to compare the accuracy and reliability of the segmentation performed by NeuralSeg to the segmentation performed by an experienced endoscopic surgeon using DICE-SORENSEN coefficients.
From segmentation performed by surgeon to segmentation performed by NeuralSeg (1 month)
NeuralSeg prediction of lymph node malignancy
Time Frame: From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)
Objective: to determine whether NeuralSeg can accurately predict malignancy in lymph node when compared to biopsy results of the lymph node that was examined.
From NeuralSeg algorithm used on EBUS imaging to biopsy report (estimated up to 2-3 months)

Collaborators and Investigators

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

Sponsor

St. Joseph's Healthcare Hamilton

Investigators

  • Principal Investigator: Wael C Hanna, St. Josephs Healthcare Hamilton

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)

April 8, 2019

Primary Completion (Actual)

September 23, 2019

Study Completion (Actual)

November 20, 2019

Study Registration Dates

First Submitted

February 19, 2019

First Submitted That Met QC Criteria

February 20, 2019

First Posted (Actual)

February 21, 2019

Study Record Updates

Last Update Posted (Actual)

March 11, 2020

Last Update Submitted That Met QC Criteria

March 10, 2020

Last Verified

March 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • StJoes EBUS AI (5636)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

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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