AI platform for predicting lung cancer lymph node malignancy during ultrasound staging
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
This study is testing a new AI tool to see if it can help doctors better determine if lymph nodes are cancerous in patients with non-small cell lung cancer during ultrasound procedures.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 600 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | St. Joseph's Healthcare Hamilton Academic / other |
| Drugs / interventions | chemotherapy, radiation |
| Locations | 1 site (Hamilton, Ontario) |
| Trial ID | NCT06540196 on ClinicalTrials.gov |
What this trial studies
This study evaluates an artificial intelligence-powered platform called NodeAI, designed to enhance the accuracy of mediastinal lymph node staging in patients with non-small cell lung cancer (NSCLC) during endobronchial ultrasound (EBUS) procedures. The platform aims to provide real-time predictions regarding the malignancy of lymph nodes, addressing the current challenge of inaccurate staging results that can lead to misinformed treatment decisions. By utilizing advanced algorithms, NodeAI seeks to improve the diagnostic pathway for lung cancer and optimize treatment strategies for patients. The study will involve patients who have undergone CT and PET scans and are referred for chest staging by EBUS-TBNA.
Who should consider this trial
Good fit: Ideal candidates for this study are adults aged 18 and older diagnosed with suspected or confirmed NSCLC who have completed CT and PET scans and are referred for chest staging by EBUS-TBNA.
Not a fit: Patients with cN0 disease, peripheral tumors, or tumors smaller than 2 cm may not benefit from this study as they do not require chest staging.
Why it matters
Potential benefit: If successful, this platform could significantly improve the accuracy of lung cancer staging, leading to better-informed treatment decisions and potentially improved patient outcomes.
How similar studies have performed: While the use of AI in medical diagnostics is an emerging field, this specific approach to real-time prediction of lymph node malignancy during EBUS staging is novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion 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)
Where this trial is running
Hamilton, Ontario
- St. Joseph's Healthcare Hamilton / McMaster University — Hamilton, Ontario, Canada (Recruiting)
Study contacts
- Study coordinator: Waël C. Hanna, MDCM, MBA, FRCSC
- Email: hannaw@mcmaster.ca
- Phone: (905) 522-1155
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.