Lung Nodule Characterization by Artificial Intelligence Techniques (CARANOD-IA)

University Hospital, Strasbourg, France

Status: Unknown

Conditions: Incidental Lung Nodule

Study type: Observational

Funder types: Other

Identifiers

NCT03843164

7311

Details and patient eligibility

About

Management of incidental lung nodule is difficult and mainly based on simple morphometric characteristics such as maximum size and shape. Radiomics could play a role in simplifying this management by orientating towards a benign or a malignant origin, by comparing advanced characteristics to a large database of lung nodules.

The primary purpose is to evaluate the performances of a novel tool based on radiomics to characterize incidental lung nodules, discovered on computed tomography.

The secondary objectives are:

• to evaluate the variation in the performances of the software based on various technical aspects of the CT, such as radiation dose, reconstruction algorithm, type of scanner,...
• to compare the performances of this software to those of expert readers,
• to analyze the potential impact of this software on patient's management.

Enrollment: 50 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Adult patient (at least 18 years old);
• With a chest CT acquired between Jan 1 2012 and Oct 1 2018 at the Strasbourg University Hospital;
• CT being available over the PACS and exhibiting at least one lung nodule;
• Patient having given its authorization for the exploitation of his medical data for this research.

Exclusion criteria

• Patient having expressed direct opposition to participation in this study;
• Patient under juridical protection;
• Patient under tutelage or guardianship.