Lung CancerDACAPONCT05704920
Integrating Artificial Intelligence Into Lung Cancer Screening.
A study testing IA and Not IA in people with Lung Cancer, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Lung Cancer
- What is being tested?
- IA and Not IA
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,722 participants
- Where
- 1 siteFrance
- Study dates
- 8 Apr 2024to 1 Oct 2030
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site, 1 recruiting
- Recruiting
CHU de Nice - Hôpital de Pasteur
Nice, Alpes-maritimes, 06001
Marquette Charles-Hugo, PhD, +33492037777, marquette.c@chu-nice.fr
Boutros Jacques, +33492037777, boutros.j@chu-nice.fr
About this study
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
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.
Read more
Implementing lung cancer screening on a large scale faces two main obstacles:
- The lack of thoracic radiologists and LDCT necessary for the eligible population (between 1.6 and 2.2 million people in France);
- 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.
- Conditions
- Lung Cancer
- Being tested
- IA, Not IA
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
IAOther
The multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography
Not IAOther
The multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography
| Study group | Role | What they receive |
|---|---|---|
| IA Group | Experimental | Patients 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 Receives: Other: IA |
| Group not IA analysis | Other | Patients 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 Receives: Other: Not IA |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosis of lung disease
Elapsed time between lung nodule discovery and MDT decision making.
Measured: At 3 years
Secondary outcomes (1)
Operating characteristics of Ai-based strategy
Measured: At 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2023
- Study start8 Apr 2024
- Primary completion1 Mar 2029 (estimated)
- Study completion1 Oct 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 2,722 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nice
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Marquette Charles-Hugo, Principal investigator, CHU de Nice, Service de Pneumologie
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05704920
- Sponsor's protocol ID
- 22-PP-12
- Acronym
- DACAPO
- Status last verified
- Sep 2026
- First submitted
- 19 Jan 2023
- Last updated
- 1 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data are available upon reasonable request
Publications
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.
PubMed 38355174
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT05704920
- Acronym / sponsor ID
- DACAPO / 22-PP-12
- Conditions
- Lung Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 2,722 participants (estimated)
- Lead sponsor
- Centre Hospitalier Universitaire de NiceUniversity, hospital or other
- Study start
- 8 Apr 2024 (actual)
- Primary completion
- 1 Mar 2029 (estimated)
- Study completion
- 1 Oct 2030 (estimated)
- First posted
- 30 Jan 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- The lack of thoracic radiologists and LDCT necessary for the eligible population (between 1.6 and 2.2 million people in France);
- 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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| IA Group | Experimental | Other: IAPatients 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 | Other | Other: Not IAPatients 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 |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (7 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| IAThe multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography | Other | IA Group |
| Not IAThe multidisciplinary team meeting discussion is not informed of the AI-based analysis of their chest computed tomography | Other | Group not IA analysis |
Primary outcome
Diagnosis of lung disease
Time frame: At 3 years
Elapsed time between lung nodule discovery and MDT decision making.
1 secondary outcome
Operating characteristics of Ai-based strategy
Time frame: At 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2026.
- Derived from this study: PubMed 38355174Benzaquen 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.
- Individual participant dataSharing planned: No. Data are available upon reasonable request
Related trials for Lung Cancer
Recruiting
MRD-Guided Postoperative Surveillance and Adaptive Treatment for Resectable Lung Cancer
Lung Neoplasms, Neoadjuvant Therapy
Not yet recruitingPhase 3
Intraarterial Therapies Plus Tislelizumab Plus Lenvatinib Versus Tislelizumab Plus Gemcitabine-Cisplatin in Unresectable Intrahepatic Cholangiocarcinoma
Intrahepatic Cholangiocarcinoma (Icc)
Active, not recruitingPhase 3
A Study to Compare the Efficacy, Safety, Pharmacokinetics, and Immunogenicity Between SB27 and Keytruda in Subjects With Metastatic Non-squamous Non-small Cell Lung Cancer
Non-small Cell Lung Cancer Stage IV
RecruitingObservational study
Label-free Femtosecond Laser Imaging Combined With the Fast Lung Artificial Intelligence Model for Rapid Intraoperative Diagnosis of Lung Surgical Specimens: A Multicentre, Prospective, Parallel-workflow, Non-inferiority Study.
Lung Cancer
RecruitingPhase 3
A Study of Calderasib (MK-1084) Plus Pembrolizumab (MK-3475) in Participants With KRAS G12C Mutant Non-small Cell Lung Cancer (NSCLC) With Programmed Cell Death Ligand 1 (PD-L1) Tumor Proportion Score (TPS) ≥50% (MK-1084-004/KANDLELIT-004)
Non-small Cell Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Marquette Charles-Hugo principal investigator
CHU de Nice, Service de Pneumologie
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing IA and Not IA in people with Lung Cancer, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marquette Charles-Hugo, PhD (+33492037777, marquette.c@chu-nice.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosis of lung disease (time frame: At 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05704920, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2026.