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
1 other identifier
interventional
2,722
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable lung-cancer
Started Apr 2024
Longer than P75 for not_applicable lung-cancer
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
January 19, 2023
CompletedFirst Posted
Study publicly available on registry
January 30, 2023
CompletedStudy Start
First participant enrolled
April 8, 2024
CompletedPrimary Completion
Last participant's last visit for primary outcome
March 1, 2029
ExpectedStudy Completion
Last participant's last visit for all outcomes
October 1, 2030
April 12, 2024
April 1, 2024
4.9 years
January 19, 2023
April 11, 2024
Conditions
Outcome Measures
Primary Outcomes (1)
Diagnosis of lung disease
Elapsed time between lung nodule discovery and MDT decision making.
At 3 years
Secondary Outcomes (1)
Operating characteristics of Ai-based strategy
At 3 years
Study Arms (2)
IA Group
EXPERIMENTALPatients with at least one nodule (\> 6mm) for whom the multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography
Group not IA analysis
OTHERPatients with at least one nodule (\> 6mm) for whom the multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography
Interventions
Eligibility Criteria
You may qualify if:
- 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
You may not qualify if:
- 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
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
CHU de Nice - Hôpital de Pasteur
Nice, Alpes-maritimes, 06001, France
Related Publications (1)
Benzaquen J, Hofman P, Lopez S, Leroy S, Rouis N, Padovani B, Fontas E, Marquette CH, Boutros J; Da Capo Study Group. Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol. BMJ Open. 2024 Feb 13;14(2):e074680. doi: 10.1136/bmjopen-2023-074680.
PMID: 38355174DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Marquette Charles-Hugo
CHU de Nice, Service de Pneumologie
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- DIAGNOSTIC
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
January 19, 2023
First Posted
January 30, 2023
Study Start
April 8, 2024
Primary Completion (Estimated)
March 1, 2029
Study Completion (Estimated)
October 1, 2030
Last Updated
April 12, 2024
Record last verified: 2024-04
Data Sharing
- IPD Sharing
- Will not share
Data are available upon reasonable request