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

NCT ID: NCT06540196

Last Updated: 2025-06-12

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 600 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-01-10
• Study Completion Date: 2026-12-31

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

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

NodeAI

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.

Group Type: EXPERIMENTAL

NodeAI

Intervention Type: 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.

Surgeon

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.

Group Type: ACTIVE_COMPARATOR

Surgeon

Intervention Type: 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.

Interventions

NodeAI

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.

Intervention Type: DIAGNOSTIC_TEST

Surgeon

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

St. Joseph's Healthcare Hamilton

OTHER

Sponsor Role: lead

Responsible Party

Wael Hanna

Head of Division, Thoracic Surgery

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

St. Joseph's Healthcare Hamilton / McMaster University

Hamilton, Ontario, Canada

Site Status: RECRUITING

Countries

Canada

Central Contacts

Waël C. Hanna, MDCM, MBA, FRCSC

Role: CONTACT

hannaw@mcmaster.ca

(905) 522-1155 ext. 35916

Yogita S. Patel, BSc

Role: CONTACT

patelys@mcmaster.ca

(905) 522-1155 ext. 35096

Facility Contacts

Yogita S. Patel, BSc.

Role: primary

patelys@mcmaster.ca

905-522-1155 ext. 35096

Other Identifiers

NodeAI Feasibility

Identifier Type: -

Identifier Source: org_study_id