No phaseRecruiting
Integrating Artificial Intelligence Into Lung Cancer Screening. (DACAPO)
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
- Sponsor
- Centre Hospitalier Universitaire de Nice (Academic or other)
- Enrolment
- 2,722 planned
- Conditions
- Lung Cancer
- Interventions
- IA; Not IA
- Ages
- adults, older adults
- Registry
- NCT05704920
From the registry
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…
Primary outcomes
- Diagnosis of lung disease