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

NCT06576232 | Completed FDA Device

• 1 other identifier: MT-LCS-002
• Study Type: observational
• Target: 1,147
• Locations: 2 countries
• Sites: 5

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

Outcome Measures

Primary Outcomes (1)

• AUROC (Area under ROC curve) at patient level

  AUROC that measures Median LCS performance at patient level is strictly superior to 0.8. Support for Primary Endpoint: Derived from the patient level AUROC at the product fixed operating point : Sensitivity, Specificity, PPV, NPV.

  12 months

Secondary Outcomes (9)

• Sensitivity > 70% when Specificity=70%

  12 months

• Specificity > 70% when Sensitivity=70%

  12 months

• AUC of LROC > 0.75

  12 months

• Detection sensitivity>0.8 with average FP rate per scan<1

  12 months

• ICC>0.8 for average diameter

  12 months

Interventions

Median LCS 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.

Also known as: eyonis LCS

Eligibility Criteria

• Age: 50 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

High risk lung cancer population from Radiology or Pneumology hospital departments. Patients enrolled in this study were retrospectively collected from centers across the EU and USA where they were enlisted into lung cancer screening due to high risk of lung cancer according to established lung cancer screening guidelines. The cohort used for testing the efficacy and safety of the device will be an "enriched cohort" with a 1:2 distribution of cancer positive and benign patients

You may qualify if:

• ≥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);

You may not qualify if:

• 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.

Sponsors & Collaborators

• Median Technologies: lead

Study Sites (5)

University of Pennsylvania - Penn Center for Innovation

Philadelphia, Pennsylvania, 19104, United States

Location

Baptist Clinical Research Institute

Memphis, Tennessee, 38120, United States

Location

The University of Texas M.D. Anderson Cancer Center

Houston, Texas, 77030, United States

Location

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

Madrid, 28040, Spain

Location

Universidad de Navarra

Pamplona, 31009, Spain

Location

Study Officials

• Anil VACHANI, MD

  University of Pennsylvania

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 23, 2024
• First Posted: August 28, 2024
• Study Start: September 21, 2022
• Primary Completion: July 24, 2024
• Study Completion: August 21, 2024
• Last Updated: August 28, 2024

Record last verified: 2024-08

Locations

ES (2); US (3)