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
Full record on ClinicalTrials.gov