Standalone Observational Study Assessing the Performance of an AI/ML Tech-based SaMD on Chest LDCT Images (REALITY)

NCT ID: NCT06576232

Last Updated: 2024-08-28

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1147 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-09-21
• Study Completion Date: 2024-08-21

Brief Summary

This is a Multinational, Multicenter, retrospective study for the evaluation of the standalone efficacy and safety of an Artificial Intelligence/Machine Learning (AI/ML) technology-based end-to-end Computer assisted Detection/Computer Assisted Diagnosis (CADe/CADx) Software as a Medical Device (SaMD) developed to detect, localize and characterize malignant, and suspicious for lung cancer nodules on Low Dose Computed Tomography (LDCT) scans taken as part of a Lung Cancer Screening (LCS) program.

LDCT Digital Imaging and Communications in Medicine (DICOM) images of patients who underwent lung cancer screening were selected and included into the study. Selected scans will then be analyzed by the CADe/CADx SaMD and compared to radiologist generated reference standards including lesions localization and lesion cancer diagnosis.

Figures of merit at patient level and lesion level detection and diagnostic efficacy will be calculated as well as sub-class analysis to ensure algorithm performance generalizability.

Conditions

High Risk Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

Median LCS

End-to-end processing of chest LDCT DICOM images by an AI/ML tech-based SaMD to detect, localize, and characterize (assign a malignancy score) each detected pulmonary nodule. The output of the device is a DICOM File (Median LCS result report) summarizing results per patient.

Intervention Type: DEVICE

Other Intervention Names

eyonis LCS

Eligibility Criteria

Inclusion Criteria

• ≥50-80 Years of age;
• Current or ex-smoker (>=20 pack years);
• Patient screened and surveilled for lung cancer screening following lung cancer screening guidelines (equivalent to United States Preventive Services Task Force (USPSTF) 2021 Criteria);
• Received LDCT due to inclusion in high-risk category for lung cancer.

Exclusion Criteria

• Prior lung resection;
• Pacemaker or other indwelling metallic medical devices in the thorax that interfere with CT acquisition;
• Patients/images used during AI model development;
• Patients with only hilar and/or mediastinal cancer(s);
• Patients with only ground glass cancer(s);
• Patients with nodules, solid or part-solid >30mm (masses);
• Patients that are not accompanied with the required clinical information;
• Patients with imaging with any of the following: missing slices, slice thickness >3mm;
• Partial cover of the lung.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Median Technologies

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Anil VACHANI, MD

Role: PRINCIPAL_INVESTIGATOR

University of Pennsylvania

Locations

University of Pennsylvania - Penn Center for Innovation

Philadelphia, Pennsylvania, United States

Baptist Clinical Research Institute

Memphis, Tennessee, United States

The University of Texas M.D. Anderson Cancer Center

Houston, Texas, United States

Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD)

Madrid, , Spain

Universidad de Navarra

Pamplona, , Spain

Countries

United States; Spain

Other Identifiers

MT-LCS-002

Identifier Type: -

Identifier Source: org_study_id