DACAPO: Integrating Artificial Intelligence Into Lung Cancer Screening.

Sponsor
Centre Hospitalier Universitaire de Nice (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT05704920
Collaborator
(none)
2,722
1
2
72
37.8

Study Details

Study Description

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.

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.

Condition or Disease Intervention/Treatment Phase
  • Other: IA
  • Other: Not IA
N/A

Study Design

Study Type:
Interventional
Anticipated Enrollment :
2722 participants
Allocation:
Randomized
Intervention Model:
Parallel Assignment
Masking:
None (Open Label)
Primary Purpose:
Diagnostic
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
Anticipated Study Start Date :
Apr 1, 2023
Anticipated Primary Completion Date :
Oct 1, 2028
Anticipated Study Completion Date :
Apr 1, 2029

Arms and Interventions

Arm Intervention/Treatment
Experimental: IA Group

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

Other: IA
The multidisciplinary team meeting discussion is informed of the AI-based analysis of their chest computed tomography

Other: Group not IA analysis

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

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

Outcome Measures

Primary Outcome Measures

  1. Diagnosis of lung disease [At 3 years]

    Elapsed time between lung nodule discovery and MDT decision making.

Secondary Outcome Measures

  1. Operating characteristics of Ai-based strategy [At 3 years]

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years to 80 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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

Contacts and Locations

Locations

Site City State Country Postal Code
1 CHU de Nice - Hôpital de Pasteur Nice Alpes-maritimes France 06001

Sponsors and Collaborators

  • Centre Hospitalier Universitaire de Nice

Investigators

  • Principal Investigator: Marquette Charles-Hugo, CHU de Nice, Service de Pneumologie

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Centre Hospitalier Universitaire de Nice
ClinicalTrials.gov Identifier:
NCT05704920
Other Study ID Numbers:
  • 22-PP-12
First Posted:
Jan 30, 2023
Last Update Posted:
Jan 30, 2023
Last Verified:
Jan 1, 2023
Individual Participant Data (IPD) Sharing Statement:
Undecided
Plan to Share IPD:
Undecided
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Jan 30, 2023