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

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY

Lung cancer (LC) screening using low-dose chest CT (LDCT) has already proven its efficacy. The mortality reduction associated with LC screening is around 20%, much higher than the reduction in mortality associated with screening for breast, colon or prostate cancers. Implementing lung cancer screening on a large scale faces two main obstacles: 1. The lack of thoracic radiologists and LDCT necessary for the eligible population (between 1.6 and 2.2 million people in France); 2. The high frequency of false positive screenings: in the NLST trial, more than 20% of the subjects screened were found to have at least one nodule of an indeterminate lung nodule (ILN) whereas less than 3% of ILNs are actually LC. The gold standard for determining on the benign or malignant nature of a nodule is definitive histology. Otherwise, the evolution of the nodule on serial thoracic imaging is a good alternative. The period of indeterminacy of a nodule can be as long as 24 months in many cases, which can be a source of prolonged and sometimes unjustified anxiety for screening candidates. The purpose of this randomized controlled study that focuses on LC screening in patients aged 50 to 80 years, who smoked more than 20 packs/ year or stopped smoking less than 15 years ago. Its objective is to determine whether assisting multidisciplinary team (MDT) meetings with an AI-based analysis of screening LDCT accelerates the definitive classification of nodules into malignant or benign.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Maximum Age: 80
Healthy Volunteers: f
View:

• 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

Locations
Other Locations
France
CHU de Nice - Hôpital de Pasteur
RECRUITING
Nice
Contact Information
Primary
Marquette Charles-Hugo, PhD
marquette.c@chu-nice.fr
+33492037777
Backup
Boutros Jacques
boutros.j@chu-nice.fr
+33492037777
Time Frame
Start Date: 2024-04-08
Estimated Completion Date: 2030-10-01
Participants
Target number of participants: 2722
Treatments
Experimental: IA Group
Patients with at least one nodule (\> 6mm) for whom the multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography
Other: Group not IA analysis
Patients with at least one nodule (\> 6mm) for whom the multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography
Related Therapeutic Areas
Sponsors
Leads: Centre Hospitalier Universitaire de Nice

This content was sourced from clinicaltrials.gov