Integrating Artificial Intelligence Into Lung Cancer Screening.

Lung Cancer Clinical Trial

— DACAPO  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05704920
• Other study ID #: 22-PP-12
• Status: Recruiting
• Phase: N/A
• Start date: April 8, 2024
• Est. completion date: October 1, 2030

Study information

• Verified date: April 2024
• Source: Centre Hospitalier Universitaire de Nice
• Contact: Marquette Charles-Hugo, PhD
• Phone: +33492037777
• Email: marquette.c[@]chu-nice.fr
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2722
• Est. completion date: October 1, 2030
• Est. primary completion date: March 1, 2029
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Intervention

Other:
• IA
The multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography
• Not IA
The multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography

Locations

Country	Name	City	State
France	CHU de Nice - Hôpital de Pasteur	Nice	Alpes-maritimes

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire de Nice

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnosis of lung disease	Elapsed time between lung nodule discovery and MDT decision making.	At 3 years
Secondary	Operating characteristics of Ai-based strategy		At 3 years