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

Clinical Utility of Artificial Intelligence-Augmented Endobronchial Ultrasound-Elastography in Lymph Node Staging for Lung Cancer

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.

Study Overview

• Status: Completed
• Conditions: NSCLC; Lung Cancer; Artificial Intelligence; Elastography; Endobronchial Ultrasound
• Intervention / Treatment: Device: EBUS-Elastography

• Study Type: Interventional
• Enrollment (Actual): 100
• Phase: Not Applicable

Contacts and Locations

Study Locations

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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Stiffness Area Ratio	Identifying whether the percent area of a lymph node above a defined blue colour threshold is independently associated with malignancy	8 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
NeuralSeg's prediction of lymph node malignancy	Determine whether NeuralSeg can accurately predict malignancy in lymph nodes when compared to biopsy results of the lymph nodes that were examined	2 months
The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values	The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values	2 months

Collaborators and Investigators

• Sponsor: St. Joseph's Healthcare Hamilton
• Investigators: Principal Investigator: Wael C Hanna, MDCM, MBA, FRCSC, McMaster University

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2021
• Primary Completion (Actual): May 1, 2022
• Study Completion (Actual): May 1, 2022

Study Registration Dates

• First Submitted: March 19, 2021
• First Submitted That Met QC Criteria: March 24, 2021
• First Posted (Actual): March 25, 2021

Study Record Updates

• Last Update Posted (Actual): January 18, 2024
• Last Update Submitted That Met QC Criteria: January 16, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: AI-EBUS-Elastography_19032021

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