AI-EBUS-Elastography for LN Staging

NCT04816981 | Completed

• 1 other identifier: AI-EBUS-Elastography_19032021
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

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.

Conditions

Artificial Intelligence; Endobronchial Ultrasound; Elastography; NSCLC; Lung Cancer

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (2)

• NeuralSeg's prediction of lymph node malignancy

  2 months

• The agreement between NeuralSeg's predictions and pathology results, as measured by diagnostic accuracy, sensitivity, specificity, positive and negative predictive values

  2 months

Study Arms (1)

EBUS-Elastography

EXPERIMENTAL

Device: EBUS-Elastography

Interventions

EBUS-Elastography DEVICE

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.

EBUS-Elastography

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

Sponsors & Collaborators

• St. Joseph's Healthcare Hamilton: lead

Study Sites (1)

St. Joseph's Healthcare Hamilton

Hamilton, Ontario, L8N 4A6, Canada

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Wael C Hanna, MDCM, MBA, FRCSC

  McMaster University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Model Details: 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.

Study Record Dates

• First Submitted: March 19, 2021
• First Posted: March 25, 2021
• Study Start: September 1, 2021
• Primary Completion: May 1, 2022
• Study Completion: May 1, 2022
• Last Updated: January 18, 2024

Record last verified: 2024-01

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)