Using AI to improve lung cancer screening
Official title: A Randomized Controlled Study of Including a Deep Learning-based Analysis of Chest Computed Tomography as an Aid to Decision Making of Multidisciplinary Team Meetings for Lung Cancer Screening in Eligible Patients
Run by Centre Hospitalier Universitaire de Nice · NCT05704920 · Data updated 2026-11-12
This study is testing whether using artificial intelligence to analyze chest scans can help improve lung cancer screening for patients by making it more accurate and reducing unnecessary follow-ups.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years to 80 Years |
| Sex | All |
| Enrollment | 2722 (estimated) |
| Sponsor | Centre Hospitalier Universitaire de Nice Academic / other |
| Locations | 1 site (Nice, Alpes-maritimes) |
| Status | Recruiting |
| Started | April 8, 2024 |
| Main results expected | March 1, 2029 |
| Study ends | October 1, 2030 |
| Registry record updated | October 1, 2026 |
| Trial ID | NCT05704920 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are individuals aged 50 to 80 who are current or recent smokers with a significant smoking history.
✕Probably not a fit
Patients with clinical signs of cancer or recent chest scans for other causes may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study aims to enhance lung cancer screening by integrating artificial intelligence into the process of analyzing low-dose chest CT scans. It addresses two major challenges: the shortage of thoracic radiologists and the high rate of false positives in lung cancer screenings. By leveraging AI, the study seeks to improve the accuracy of identifying indeterminate lung nodules, potentially reducing unnecessary follow-ups and anxiety for patients. Participants will be monitored over time to assess the effectiveness of this approach in a real-world setting.
Why it matters
Potential benefit: If successful, this could lead to more accurate lung cancer screenings and earlier detection, ultimately improving patient outcomes.
How similar studies have gone: Other studies have shown promise in using AI for medical imaging, suggesting potential success for this novel approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age between 50 and 80 years old * active smoker or ex-smoker who quit smoking less than 15 years ago * smoking history of at least 20 pack-years * signature of the informed consent * affiliation to French social security Exclusion Criteria: * clinical signs suggestive of cancer * recent chest scan (\<1 year) for another cause * radiological abnormality requiring follow-up or additional investigations * health problem significantly limiting life expectancy from the clinician's point of view * health problem limiting ability or willingness to undergo lung surgery * Patients with active neoplasia, except basal cell carcinoma of the skin. * vulnerable people: adults under guardianship, adults under curatorship medical and/or psychiatric problems of sufficient severity to limit full adherence to the study or expose patients to excessive risk
Where it's running
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CHU de Nice - Hôpital de Pasteur
Nice, Alpes-maritimes, France Recruiting
Study contacts
- Principal investigator: Marquette Charles-Hugo — CHU de Nice, Service de Pneumologie
- Study coordinator: Marquette Charles-Hugo, PhD
- Email: marquette.c@chu-nice.fr
- Phone: +33492037777
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.