Integrating Artificial Intelligence Into Lung Cancer Screening.

Recruiting now · Not applicable

Conditions studied: Lung Cancer

In brief

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.

Key facts

Study ID
NCT05704920
Run by
Centre Hospitalier Universitaire de Nice
People needed
2722
Starts
2024-04-08
Expected to finish
2030-10-01
Last updated by the study team
2024-04-12

Who can join

Age: 18 and older, up to 80. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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