AI-EBUS-Elastography for LN Staging

Not Applicable

Completed

• Conditions: NSCLC; Endobronchial Ultrasound; Artificial Intelligence; Elastography; Lung Cancer
• Interventions: Device: EBUS-Elastography

• Registration Number: NCT04816981
• Lead Sponsor: St. Joseph's Healthcare Hamilton

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-03-19
• Study First Submitted QC: 2021-03-24
• Study First Posted: 2021-03-25
• Study Start: 2021-09-01
• Primary Completion: 2022-05-01
• Study Completion: 2022-05-01
• Status Verified: 2024-01
• Last Update Submitted: 2024-01-16
• Last Update Posted: 2024-01-18

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 100

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
EBUS-Elastography	EBUS-Elastography	-

Primary Outcome Measures

Name	Time	Method
Stiffness Area Ratio	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

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

Trial Locations

Locations (1)

St. Joseph's Healthcare Hamilton; 🇨🇦 Hamilton, Ontario, Canada