Artificial Intelligence in Lung Cancer Screening (INAIL BRIC)
Development of an Artificial Intelligence Model in Lung Cancer Screening for the Diagnosis of Lung Nodules and Risk Stratification in Subjects With Occupational and/or Smoking Exposure
Study Overview
Status
Conditions
Detailed Description
The project we propose is a study whose objective was to develop an artificial intelligence program integrated into a web-based platform for the optimization of the performance of lung cancer screening for the diagnosis of lung nodules and risk stratification in subjects exposed to environmental carcinogens and/or cigarette smoke.
Inclusion criteria:
Age > 50; smokers for at least 20 pack-years (20 cigarillos a day for 20 years) or former heavy smokers if they quit less than 15 years ago; and/or previous professional exposure to asbestos; absence of lung cancer symptoms; who performed lung cancer screening after the year 2000 upon approval of the study by the relevant EC.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
-
-
-
Milan, Italy, 20132
- IRCCS San Raffaele Scientific Institute
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Age > 50 years; smokers for at least 20 pack-years (20 cigarettes a day for 20 years) or former heavy smokers if they quit less than 15 years ago; and/or previous professional exposure to asbestos.
All subjects were enrolled in lung cancer screening program.
Description
Inclusion Criteria:
- Age > 50 years;
- smokers for at least 20 pack-years (20 cigarettes a day for 20 years) or former heavy smokers if they quit less than 15 years ago;
- and/or previous professional exposure to asbestos;
- absence of lung cancer symptoms;
- who performed lung cancer screening after the year 2000 upon approval of the study by the relevant Etical Committee
Exclusion Criteria:
- Age < 50 years
- never smokers
- lung cancer symptoms
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
|---|
|
subjects enrolled in lung cancer screening
Actual Smokers or formers smoker; Age > 50 years.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
AIM 1 Pilot deep learning model
Time Frame: from enrollment to the end of treatment at 2 years
|
Development and fine-tuning of a pilot deep learning model for automatic detection and diagnosis of screen-detected nodules for risk stratification in subjects with asbestos exposure as part of a lung cancer screening program in high-risk subjects for exposure to asbestos and smoking on retrospective data.
|
from enrollment to the end of treatment at 2 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
AIM 2 Clinical database
Time Frame: from enrollment to the end of treatment at 2 years
|
Development of an integrated system between the clinical database and several existing imaging volumetric software and risk models for the creation of a pilot platform in order to optimize the organizational management of lung cancer screening.
|
from enrollment to the end of treatment at 2 years
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 116/INT/2022
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.