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Integrating Artificial Intelligence Into Lung Cancer Screening.

NCT05704920 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 2722

Last updated 2024-04-12

No results posted yet for this study

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.

Conditions

Interventions

OTHER

IA

The multidisciplinary team meeting discussion is 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

Sponsors & Collaborators

  • Centre Hospitalier Universitaire de Nice

    lead OTHER

Principal Investigators

  • Marquette Charles-Hugo · CHU de Nice, Service de Pneumologie

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-08
Primary Completion
2029-03-01
Completion
2030-10-01

Countries

  • France

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05704920 on ClinicalTrials.gov