Lung CancerNodeAINCT06540196
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
A study testing NodeAI and Surgeon in people with Lung Cancer and Non Small Cell Lung Cancer, run by McMaster University at 1 site in Canada.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Non Small Cell Lung Cancer
- What is being tested?
- NodeAI and Surgeon
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 600 participants
- Where
- 1 siteCanada
- Study dates
- 10 Jan 2025to 31 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Patients with cN0 disease AND peripheral tumors AND tumors < 2 cm in diameter (those do not require chest staging)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Canada1 site, 1 recruiting
- Recruiting
St. Joseph's Healthcare Hamilton
Hamilton, Ontario, L8N 4A6
Yogita S. Patel, BSc., 905-522-1155 ext. 35096, patelys@mcmaster.ca
About this study
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
- Being tested
- NodeAI, Surgeon
- Medical subject headings
- Lung Neoplasms, Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
NodeAIDiagnostic 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.
SurgeonDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Diagnostic Test: Surgeon |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 weeks post-EBUS procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2024
- Study start10 Jan 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by McMaster UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- McMaster University
- Sponsor type
- University, hospital or other
- Collaborators
- St. Joseph's Healthcare Hamilton
- Responsible party
- Principal investigator, Wael C. Hanna, McMaster University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06540196
- Sponsor's protocol ID
- NodeAI Feasibility
- Acronym
- NodeAI
- Status last verified
- Jul 2026
- First submitted
- 29 Jul 2024
- Last updated
- 30 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT06540196
- Acronym / sponsor ID
- NodeAI / NodeAI Feasibility
- Conditions
- Lung Cancer, Non Small Cell Lung Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- McMaster UniversityUniversity, hospital or other
- Collaborators
- St. Joseph's Healthcare Hamilton
- Study start
- 10 Jan 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 6 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| NodeAI | Experimental | Diagnostic Test: NodeAIThe 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 | Active comparator | Diagnostic Test: SurgeonThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (1 listed)
- Patients with cN0 disease AND peripheral tumors AND tumors < 2 cm in diameter (those do not require chest staging)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| NodeAIThe 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 |
| SurgeonThe 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 |
Primary outcome
The ability of NodeAI to predict lymph node malignancy from real-time ultrasound images of lymph nodes during EBUS at the bedside
Time frame: 3 weeks post-EBUS procedure
This will be quantified by the percent of lymph nodes where the above is successful when compared to pathology
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing NodeAI and Surgeon in people with Lung Cancer and Non Small Cell Lung Cancer, run by McMaster University at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Waël C. Hanna, MDCM, MBA, FRCSC ((905) 522-1155 ext. 35916, hannaw@mcmaster.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The ability of NodeAI to predict lymph node malignancy from real-time ultrasound images of lymph nodes during EBUS at the bedside (time frame: 3 weeks post-EBUS procedure). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jul 2026.