The Development, Safety, and Feasibility of an Artificial Intelligence-Powered Platform (NodeAI) for Real-Time Prediction of Mediastinal Lymph Node Malignancy During Endobronchial Ultrasound Staging for Lung Cancer

NCT06540196 | Recruiting

• 1 other identifier: NodeAI Feasibility
• Study Type: interventional
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

Lung cancer is the leading cause of annual cancer deaths globally, more than breast, prostate, and colon cancers combined. The staging of chest lymph nodes (LNs) is a crucial step in the lung cancer diagnostic pathway because it aids in treatment decisions - whether a patient is a candidate for lung resection, chemotherapy, radiation, or multimodal treatments. Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA) is the current standard for chest nodal staging for non-small cell lung cancer (NSCLC), and guidelines mandate that Systematic Sampling (SS) of at least 3 chest LN stations be routinely performed for accurate staging. Unfortunately, EBUS-TBNA yields inaccurate results in 40% of patients, leading to misinformed treatment decisions. This proportion is much higher in patients with Triple Normal LNs \[LNs that appear normal on computed tomography (CT) scans, positron emission tomography (PET) scans, and EBUS\], which have been found to have a \> 93% chance of being truly benign. This is because EBUS-TBNA is based on ultrasound, whose success highly depends on the skill of the person performing it (operator). When the operator makes an error, the entire procedure is jeopardized. This causes downstream delays in treatment due to repeated testing and ill-informed treatment decisions. Over the past decade, the investigator has been conducting a series of research studies and trials: the development and validation of the Canada Lymph Node Score (CLNS) - a surgeon-derived semi-quantitative measure of LN malignancy; an Artificial Intelligence (AI)-based version of the CLNS to predict malignancy; and a fully autonomous AI that learned to predict malignancy directly from ultrasound images, to introduce AI to the decision-making pathway in NSCLC. This resulted in the creation of an AI-powered software to predict malignancy in mediastinal LNs of patients with lung cancer. The software is currently housed in cloud storage and its applications are latent - which means that LN images must be uploaded to the software, and results are received at a future time. In its current form, the software is not ready for clinical application due to this latency. In this project, the investigator aims to build a point-of-care device which will house the software (NodeAI) and deliver real-time results to the surgeon, and this device will be tested in a clinical trial.

Conditions

Lung Cancer; Non Small Cell Lung Cancer

Outcome Measures

Primary Outcomes (1)

• The ability of NodeAI to predict lymph node malignancy from real-time ultrasound images of lymph nodes during EBUS at the bedside

  This will be quantified by the percent of lymph nodes where the above is successful when compared to pathology

  3 weeks post-EBUS procedure

Study Arms (2)

NodeAI

EXPERIMENTAL

The ultrasound video and images of each LN will be analyzed by NodeAI, which will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

Diagnostic Test: NodeAI

Surgeon

ACTIVE COMPARATOR

The ultrasound video and images of each LN will first be analyzed by the surgeon, who will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

Diagnostic Test: Surgeon

Interventions

NodeAI DIAGNOSTIC_TEST

The ultrasound video and images of each LN will be analyzed by NodeAI, which will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

NodeAI

Surgeon DIAGNOSTIC_TEST

The ultrasound video and images of each LN will first be analyzed by the surgeon, who will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

Surgeon

Eligibility Criteria

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

You may qualify if:

• Patients ≥ 18 years of age diagnosed with suspected or confirmed NSCLC based on CT and PET scans that are referred for chest staging by EBUS-TBNA
• CT and PET scans completed

You may not qualify if:

• Patients with cN0 disease AND peripheral tumors AND tumors \< 2 cm in diameter (those do not require chest staging)

Sponsors & Collaborators

• St. Joseph's Healthcare Hamilton: lead

Study Sites (1)

St. Joseph's Healthcare Hamilton / McMaster University

Hamilton, Ontario, L8N 4A6, Canada

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms; Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Carcinoma, Bronchogenic; Bronchial Neoplasms

Central Study Contacts

Waël C. Hanna, MDCM, MBA, FRCSC

CONTACT

(905) 522-1155; hannaw@mcmaster.ca

Yogita S. Patel, BSc

CONTACT

(905) 522-1155; patelys@mcmaster.ca

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Division, Thoracic Surgery

Study Record Dates

• First Submitted: July 29, 2024
• First Posted: August 6, 2024
• Study Start: January 10, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: June 12, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)