Retrospective Clinical Trial Comparing Radiologists' Diagnosis Accuracy in Lung Cancer Screening Population With and Without the Help of an AI/ML Tech-based SaMD

NCT ID: NCT06751576

Last Updated: 2025-05-06

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 480 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-09-21
• Study Completion Date: 2025-03-12

Brief Summary

This is a two arm, randomized, controlled, blinded, multi-case multi reader (MRMC), retrospective study for the evaluation of the efficacy and safety of an AI/ML technology-based CADe/x developed to detect, localize and characterize malignancy score of pulmonary nodules on LDCT chest scans taken as part of a lung cancer screening program.

LDCT DICOM images of patients who underwent routine lung cancer screening will be selected and enrolled into the study. Enrolled scans analyzed by radiologists with the assistance of the Median LCS (formerly iBiopsy) device are compared to the analysis by radiologists without the assistance of the Median LCS device.

Figures of merit for patient level and lesion level detection and diagnostic efficacy will be calculated and compared, sub-class analysis will be performed to ensure device generalizability.

Conditions

High Risk Cancer; Lung Cancer

Study Design

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

Study Groups

Control arm

low-dose CT scan image readings performed by radiologists without the assistance of Median LCS

No interventions assigned to this group

Test arm

low-dose CT scan image readings performed by radiologists with the assistance of Median LCS.

Median LCS

Intervention Type: DEVICE

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.

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-004

Identifier Type: -

Identifier Source: org_study_id