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

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.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Other: IA; Other: Not IA

• Study Type: Interventional
• Enrollment (Estimated): 2722
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Marquette Charles-Hugo, PhD; Phone Number: +33492037777; Email: marquette.c@chu-nice.fr
• Study Contact Backup: Name: Boutros Jacques; Phone Number: +33492037777; Email: boutros.j@chu-nice.fr

Study Locations

• France
  • Alpes-maritimes
    • Nice, Alpes-maritimes, France, 06001
      • Recruiting
      • CHU de Nice - Hôpital de Pasteur
      • Contact: Marquette Charles-Hugo, PhD; Phone Number: +33492037777; Email: marquette.c@chu-nice.fr
      • Contact: Boutros Jacques; Phone Number: +33492037777; Email: boutros.j@chu-nice.fr
      • Principal Investigator: Marquette Charles-Hugo, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnosis of lung disease	Elapsed time between lung nodule discovery and MDT decision making.	At 3 years

Secondary Outcome Measures

Outcome Measure	Time Frame
Operating characteristics of Ai-based strategy	At 3 years

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nice
• Investigators: Principal Investigator: Marquette Charles-Hugo, CHU de Nice, Service de Pneumologie

Study record dates

Study Major Dates

• Study Start (Actual): April 8, 2024
• Primary Completion (Estimated): March 1, 2029
• Study Completion (Estimated): October 1, 2030

Study Registration Dates

• First Submitted: January 19, 2023
• First Submitted That Met QC Criteria: January 27, 2023
• First Posted (Actual): January 30, 2023

Study Record Updates

• Last Update Posted (Actual): April 12, 2024
• Last Update Submitted That Met QC Criteria: April 11, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 22-PP-12

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Data are available upon reasonable request

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No