Using AI to identify and prevent smoking-related diseases
ARtificial Intelligence for heAlth and Prevention of Smoking-related Diseases
This study is testing a new AI tool to see if it can help find and tell the difference between harmful and harmless lung nodules in smokers and former smokers over 50 who are at high risk for lung cancer.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 2840 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Scientific Institute San Raffaele Academic / other |
| Locations | 1 site (Milan) |
| Trial ID | NCT06626178 on ClinicalTrials.gov |
What this trial studies
This interventional pilot study aims to develop and validate an artificial intelligence model that can accurately detect lung nodules and differentiate between malignant and benign tumors in high-risk individuals. The study involves a single-center approach at the Scientific Institute Ospedale San Raffaele in Milan, where participants will undergo low-dose CT scans, blood sampling, spirometry, and complete various questionnaires. The target population includes smokers and former smokers over 50 years old who are at high risk for lung cancer, as well as those enrolled in previous screening cohorts.
Who should consider this trial
Good fit: Ideal candidates for this study are smokers and former smokers over the age of 50 with a significant smoking history and at high risk for lung cancer.
Not a fit: Patients with previous or concurrent neoplastic diseases, severe pulmonary conditions, or cognitive impairments may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to earlier detection and better prevention strategies for smoking-related diseases, particularly lung cancer.
How similar studies have performed: Other studies have shown promise in using AI for cancer detection, suggesting that this approach could be effective, though this specific application may be novel.
Eligibility criteria
Show full inclusion / exclusion criteria
High-risk screening subjects Inclusion Criteria: * Age \>= 50 years old * Active smokers * Former smokers (from no more than 15 years) * Pack/year \>20 * Risk-prediction model from Prostate, Lung, Colorectal, and Ovarian study (PLCOm2012) \>1.2% * Provision and signature of informed consent Exclusion Criteria: * Previous or concurrent neoplastic disease, excluding skin cancers * Cognitive or other problems that could hinder the collection of informed consent * Severe pulmonary or extra pulmonary disease * Previous low-dose computed tomography (CT) scan in the past 12 months Previous high-risk positive screening subjects Inclusion Criteria: * Subjects enrolled in previous lung cancer screening with the presence of lung nodules \>4 mm and candidate to additional computed tomography (CT) * Signed informed consent Exclusion Criteria: \- None Previous high-risk negative screening subjects Inclusion Criteria: * Subjects enrolled in previous lung cancer screening in this Institute with negative computed tomography (CT) * Signed informed consent Exclusion Criteria: \- None Lung Cancer patients Inclusion Criteria: * Patients with diagnosis or suspicious diagnosis of lung cancer candidate to surgical treatment or already submitted to it * Patients with diagnosis of lung cancer treated with surgical resection * Signed informed consent Exclusion Criteria: * computed tomography (CT) scans not available at San Raffaele Hospital * Previous neoadjuvant treatment
Where this trial is running
Milan
- Scientific Institute Ospedale San Raffaele — Milan, Italy (Recruiting)
Study contacts
- Study coordinator: Piergiorgio Muriana, MD
- Email: muriana.piergiorgio@hsr.it
- Phone: 0226437232
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.