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AI-EBUS-Elastography for LN Staging (AI-EBUS-E)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Identifier: NCT04816981
Recruitment Status : Recruiting
First Posted : March 25, 2021
Last Update Posted : April 4, 2022
Information provided by (Responsible Party):
Wael Hanna, McMaster University

Brief Summary:
Before any treatment decisions are made for patients with lung cancer, it is crucial to determine whether the cancer has spread to the lymph nodes in the chest. Traditionally, this is determined by taking biopsy samples from these lymph nodes, using the Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA) procedure. Unfortunately, in 40% of the time, the results of EBUS-TBNA are not informative and wrong treatment decisions are made. There is, therefore, a recognized need for a better way to determine whether the cancer has spread to the lymph nodes in the chest. The investigators believe that elastography, a recently discovered imaging technology, can fulfill this need. In this study, the investigators are proposing to determine whether elastography can diagnose cancer in the lymph nodes. Elastography determines the tissue stiffness in the different parts of the lymph node and generates a colour map, where the stiffest part of the lymph node appears blue, and the softest part appears red. It has been proposed that if a lymph node is predominantly blue, then it contains cancer, and if it is predominantly red, then it is benign. To study this, the investigators have designed an experiment where the lymph nodes are imaged by EBUS-Elastography, and the images are subsequently analyzed by a computer algorithm using Artificial Intelligence. The algorithm will be trained to read the images first, and then predict whether these images show cancer in the lymph node. To evaluate the success of the algorithm, the investigators will compare its predictions to the pathology results from the lymph node biopsies or surgical specimens.

Condition or disease Intervention/treatment Phase
Artificial Intelligence Endobronchial Ultrasound Elastography NSCLC Lung Cancer Device: EBUS-Elastography Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 100 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Intervention Model Description: This is a single-centre, prospective clinical trial, in which patients will be enrolled in a consecutive sample and patient involvement will conclude when the procedure ends. No follow-up will be required after the study.
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Clinical Utility of Artificial Intelligence Augmented Endobronchial Ultrasound Elastography in Lymph Node Staging for Lung Cancer
Actual Study Start Date : September 1, 2021
Estimated Primary Completion Date : May 1, 2022
Estimated Study Completion Date : May 1, 2022

Resource links provided by the National Library of Medicine

Arm Intervention/treatment
Experimental: EBUS-Elastography Device: EBUS-Elastography
Patients undergoing LN staging for lung cancer with EBUS-TBNA will have digital images and biopsy of every LN obtained in accordance with standards of care. Prior to the lymph node biopsy by EBUS-TBNA, elastography will be performed. The relative strain of tissues in the scanned area of the LNs will be displayed as a colour map, with stiffer areas in blue and softer tissue in red. Elastography and B-mode images will be displayed side by side and images recorded and saved onto an external drive for analysis. Elastography images will be fed to the NeuralSeg algorithm which has a network architecture similar to the standard U-Net for image segmentation. The automatically identified regions of interest will be overlaid onto the EBUS Elastography images to extract the LN stiffness measurements. After overlaying, NeuralSeg will determine the proportion of the LN area within 9 previously defined stiffness thresholds.

Primary Outcome Measures :
  1. Stiffness Area Ratio [ Time Frame: 8 months ]
    Identifying whether the percent area of a lymph node above a defined blue colour threshold is independently associated with malignancy

Secondary Outcome Measures :
  1. NeuralSeg's prediction of lymph node malignancy [ Time Frame: 2 months ]
    Determine whether NeuralSeg can accurately predict malignancy in lymph nodes when compared to biopsy results of the lymph nodes that were examined

  2. The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values [ Time Frame: 2 months ]
    The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No

Inclusion Criteria:

  • Patients that are diagnosed with suspected or confirmed NSCLC that have been referred to mediastinal staging through EBUS-TBNA at St. Joseph's Healthcare Hamilton will be eligible for this study.

Exclusion Criteria:

  • No exclusion criteria will apply.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT04816981

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Contact: Yogita S Patel 905-522-1155 ext 35096
Contact: Nikkita Mistry 905-522-1155 ext 35096

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Canada, Ontario
St. Joseph's Healthcare Hamilton Recruiting
Hamilton, Ontario, Canada, L8N 4A6
Contact: Yogita S Patel    905-522-1155 ext 35096   
Sponsors and Collaborators
St. Joseph's Healthcare Hamilton
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Principal Investigator: Wael C Hanna, MDCM, MBA, FRCSC McMaster University
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Responsible Party: Wael Hanna, Associate Professor, McMaster University Identifier: NCT04816981    
Other Study ID Numbers: AI-EBUS-Elastography_19032021
First Posted: March 25, 2021    Key Record Dates
Last Update Posted: April 4, 2022
Last Verified: March 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Lung Neoplasms
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Lung Diseases
Respiratory Tract Diseases