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Trial Title:
Integrating Artificial Intelligence Into Lung Cancer Screening.
NCT ID:
NCT05704920
Condition:
Lung Cancer
Conditions: Official terms:
Lung Neoplasms
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
Randomized
Intervention model:
Parallel Assignment
Primary purpose:
Diagnostic
Masking:
None (Open Label)
Intervention:
Intervention type:
Other
Intervention name:
IA
Description:
The multidisciplinary team meeting discussion is informed of the AI-based analysis of
their chest computed tomography
Arm group label:
IA Group
Intervention type:
Other
Intervention name:
Not IA
Description:
The multidisciplinary team meeting discussion is not informed of the AI-based analysis of
their chest computed tomography
Arm group label:
Group not IA analysis
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.
Criteria for eligibility:
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
Gender:
All
Minimum age:
18 Years
Maximum age:
80 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
CHU de Nice - Hôpital de Pasteur
Address:
City:
Nice
Zip:
06001
Country:
France
Status:
Recruiting
Contact:
Last name:
Marquette Charles-Hugo, PhD
Phone:
+33492037777
Email:
marquette.c@chu-nice.fr
Contact backup:
Last name:
Boutros Jacques
Phone:
+33492037777
Email:
boutros.j@chu-nice.fr
Investigator:
Last name:
Marquette Charles-Hugo, PhD
Email:
Principal Investigator
Start date:
April 8, 2024
Completion date:
October 1, 2030
Lead sponsor:
Agency:
Centre Hospitalier Universitaire de Nice
Agency class:
Other
Source:
Centre Hospitalier Universitaire de Nice
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05704920
