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

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.

Study Overview

• Status: Unknown
• Conditions: Incidental Lung Nodule

• Study Type: Observational
• Enrollment (Anticipated): 50

Contacts and Locations

• Study Contact: Name: Aissam LABANI, MD; Phone Number: 33 3 69 55 11 17; Email: aissam.labani@chru-strasbourg.fr

Study Locations

• France
  • Strasbourg, France, 67091
    • Recruiting
    • Service de Radiologie B - NHC
    • Contact: Aissam LABANI, MD; Phone Number: 33 3 69 55 11 17; Email: aissam.labani@chru-strasbourg.fr
    • Sub-Investigator: Aissam LABANI, MD
    • Sub-Investigator: Pierre Alexis AUTRUSSEAU

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients with a chest CT acquired between Jan 1 2012 and Oct 1 2018 at the Strasbourg University Hospital;

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluate the performances of a novel tool based on radiomics to characterize incidental lung nodules, discovered on computed tomography.	The study concerns patients who performed a chest CT scan at Strasbourg University Hospitals between 01/01/2012 and 01/10/2018	he period from January 1st, 2012 to October 01, 2018 will be examined

Collaborators and Investigators

• Sponsor: University Hospital, Strasbourg, France

Study record dates

Study Major Dates

• Study Start (ACTUAL): March 12, 2019
• Primary Completion (ANTICIPATED): March 1, 2021
• Study Completion (ANTICIPATED): March 1, 2021

Study Registration Dates

• First Submitted: February 13, 2019
• First Submitted That Met QC Criteria: February 13, 2019
• First Posted (ACTUAL): February 15, 2019

Study Record Updates

• Last Update Posted (ACTUAL): April 4, 2019
• Last Update Submitted That Met QC Criteria: April 3, 2019
• Last Verified: February 1, 2019

More Information

Terms related to this study

• Keywords: Multidetector Computed Tomography; Artificial Intelligence; Solitary Pulmonary Nodule
• Other Study ID Numbers: 7311 (CTEP)

Drug and device information, study documents

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