Comparing NodeAI with Surgeon for lung cancer
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
- 2 groups
- One site, in Hamilton
- Recruiting
Interventions
- Other intervention
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.
- Other intervention
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.
Canadian Sites (1)
1 of 1 recruiting
- Recruiting
St. Joseph's Healthcare Hamilton
Hamilton, Ontario
Eligibility Criteria
See who this study is looking for3 criteria
The trial’s own eligibility text, word for word from the registry. Only the trial site can say who takes part.
Inclusion
- +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
- −Patients with cN0 disease AND peripheral tumors AND tumors \< 2 cm in diameter (those do not require chest staging)
In plain language
Assembled directly from this trial’s registry record. Every sentence traces to a field below — nothing here is generated or interpreted.
What this study is
This study is testing a way to identify or measure a condition.
This study is not organised into the usual phase numbers — that is common for device, behavioural, and diagnostic research.
From the trial registry
Built from these fields:
- designModule.designInfo.primaryPurpose
- designModule.phases
Who receives what
There are 2 groups in this study.
Group A receives NodeAI.
Registry label: A: NodeAI
Group B, the comparison group, receives Surgeon.
Registry label: B: Surgeon
From the trial registry
Built from these fields:
- armsInterventionsModule.armGroups[].label
- armsInterventionsModule.armGroups[].type
- armsInterventionsModule.armGroups[].interventionNames
How the study is run
Groups are assigned by the study team using set rules, rather than by chance.
This study is open-labelOpen-labelEveryone knows which treatment is being given — nothing is hidden.Read more →: everyone knows which treatment is being given, including you and the study team.
From the trial registry
Built from these fields:
- designModule.designInfo.allocation
- designModule.designInfo.maskingInfo.masking
Is there a placebo?
This study does not list a placeboPlaceboA dummy treatment with no active medicine, used for comparison.Read more → group.
One group receives an existing treatment, so the two can be compared.
From the trial registry
Built from these fields:
- armsInterventionsModule.armGroups[].type
- armsInterventionsModule.armGroups[].interventionNames
Who the study is looking for
The study lists a minimum age of 18 years, with no upper limit given.
The study is open to people of any sex.
The study does not accept healthy volunteersHealthy volunteerSomeone without the condition being studied who takes part anyway.Read more →.
These are the criteria the study lists. Only the trial siteTrial siteA hospital or clinic where a study is actually run.Read more → can decide whether someone can take part.
From the trial registry
Built from these fields:
- eligibilityModule.minimumAge
- eligibilityModule.sex
- eligibilityModule.healthyVolunteers
How big and how long
The study aims to enrol about 600 people.
The study is currently expected to finish around December 2026.
The main measurement is taken over: 3 weeks post-EBUS procedure.
From the trial registry
Built from these fields:
- designModule.enrollmentInfo.count
- statusModule.completionDateStruct.date
- outcomesModule.primaryOutcomes[].timeFrame
What the study measures
The ability of NodeAI to predict lymph node malignancy from real-time ultrasound images of lymph nodes during EBUS at the bedside — measured over 3 weeks post-EBUS procedure.
From the trial registry
Built from these fields:
- outcomesModule.primaryOutcomes[].measure
- outcomesModule.primaryOutcomes[].timeFrame
Source: NCT06540196 on ClinicalTrials.gov. The full registry text is further down this page — this summary never replaces it.
Not medical advice. What do these terms mean?
What is being tested — in plain terms
About 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.
From the trial registry — its own words, unedited.
What a diagnostic test is here: A scan, blood test, or other test being studied for how well it detects or measures something.
Read the full explanation → · in clinical review
About 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.
From the trial registry — its own words, unedited.
What a diagnostic test is here: A scan, blood test, or other test being studied for how well it detects or measures something.
Read the full explanation → · in clinical review
Common questions
Answered from this trial’s registry record. Where the record doesn’t say, these answers say so rather than filling the gap.
Am I eligible for this trial?
Only the trial siteTrial siteA hospital or clinic where a study is actually run.Read more → can decide whether someone can take part. Concord cannot make that determination, and neither can any tool that has not examined you.
What the study lists: a minimum age of 18 years.
The full inclusionInclusion criteriaThe things you must have or be for a study to consider you.Read more → and exclusion criteriaExclusion criteriaThe things that would prevent someone from taking part.Read more → are published on this page, exactly as the study team wrote them.
The useful next step is to bring this trial to your doctor. Answering a few questions first gives them something concrete to review.
From the trial registry
- eligibilityModule.minimumAge
- eligibilityModule.eligibilityCriteria
Is there a placebo?
This study does not list a placeboPlaceboA dummy treatment with no active medicine, used for comparison.Read more → group.
One group receives an existing treatment so the two can be compared.
From the trial registry
- armsInterventionsModule.armGroups[].type
Would I know which treatment I am getting?
This study is open-labelOpen-labelEveryone knows which treatment is being given — nothing is hidden.Read more →: everyone knows which treatment is being given, including you and the study team.
Groups are assigned by the study team using set rules, rather than by chance.
From the trial registry
- designModule.designInfo.maskingInfo.masking
- designModule.designInfo.allocation
Who can join?
The study lists a minimum age of 18 years, with no upper limit given.
It is open to people of any sex.
It does not accept healthy volunteersHealthy volunteerSomeone without the condition being studied who takes part anyway.Read more →.
Only the trial siteTrial siteA hospital or clinic where a study is actually run.Read more → can confirm whether a particular person can take part.
From the trial registry
- eligibilityModule.minimumAge
- eligibilityModule.sex
- eligibilityModule.healthyVolunteers
How long would this take?
The study's main measurement is taken over: 3 weeks post-EBUS procedure.
The study as a whole is currently expected to finish around 2026-12-31.
How long any one person takes part can differ from the study length. The study team can tell you what the schedule looks like in practice.
From the trial registry
- outcomesModule.primaryOutcomes[].timeFrame
- statusModule.completionDateStruct.date
How many people are taking part?
The study aims to enrol about 600 people.
From the trial registry
- designModule.enrollmentInfo.count
Where is this happening?
This study lists one location: Hamilton, Ontario, Canada.
Sites can open and close during a study, so confirm with the team before travelling.
From the trial registry
- trial_locations
About This Trial
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.
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See if this trial could fit youThis page is for informational purposes only and does not constitute medical advice. Clinical trial eligibility can only be determined by the trial site after proper screening. Trial information is sourced from ClinicalTrials.gov and may not reflect the most current status. Always consult your healthcare provider before making decisions about clinical trial participation.