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

A Multi-Reader Multi-Case Controlled Clinical Trial to Evaluate the Comparative Accuracy Of Readers Assisted By an AI/ML Technology-Based End-To-End CADe/CADx SaMD Versus Alone in the Detection, Localization and Characterization of Pulmonary Nodules in Populations With High Risk of Lung Cancer (RELIVE)

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.

Study Overview

• Status: Completed
• Conditions: Lung Cancer; High Risk Cancer
• Intervention / Treatment: Device: Median LCS

• Study Type: Observational
• Enrollment (Actual): 480

Contacts and Locations

Study Locations

• Spain
  • Madrid, Spain, 28040
    • Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD)
  • Pamplona, Spain, 31009
    • Universidad de Navarra
• United States
  • Pennsylvania
    • Philadelphia, Pennsylvania, United States, 19104
      • University of Pennsylvania - Penn Center for Innovation
  • Tennessee
    • Memphis, Tennessee, United States, 38120
      • Baptist Clinical Research Institute
  • Texas
    • Houston, Texas, United States, 77030
      • The University of Texas M.D. Anderson Cancer Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Control arm; low-dose CT scan image readings performed by radiologists without the assistance of Median LCS
Test arm; low-dose CT scan image readings performed by radiologists with the assistance of Median LCS.	Device: 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.; Other Names: eyonis LCS

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
∆ AUC of ROCs > 0. Delta Area between the Response operating curve (AUROC) value with Median LCS and AUROC without Median LCS at patient level data is superior to 0.	Demonstrate that patient diagnosis with Median LCS is improved compared to without Median LCS.	12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity at max Youden	Demonstrate that Median LCS aided sensitivity is non inferior (H2) , superior (H8) to radiologist alone. (Sensitivity with Median LCS-Patient) non inferior using non-inferiority margin delta = 0.1 to (Sensitivity Control Arm-Patient). First, non-inferiority. If passed, superiority will be performed.	12 months
Specificity at max Youden	Demonstrate that Median LCS assisted specificity is not inferior (H3), superior (H9) to radiologist alone. (Sensitivity with Median LCS-Patient) non inferior using non-inferiority margin delta = 0.1 to (Sensitivity Control Arm-Patient). First, non-inferiority. If passed, superiority will be performed.	12 months
∆ AUC of LROC > 0	Demonstrate that Median LCS improves clinician's performance in finding detection and diagnosis.	12 months
Recall rates for non-cancer patients (Specificity)	Demonstrate that Median LCS aids to rule out non-cancer patients compared to radiologist alone. "Non-Cancer-Recall-Rate will be calculated and compared between the two modalities using margin of 10%". First, non-inferiority. If passed, superiority will be performed.	12 months
Recall rates for cancer patients (Sensitivity)	Demonstrate that Median LCS aid to diagnose cancer patients compared to radiologist alone. "Cancer-Recall-Rate will be calculated and compared between the two modalities using margin of 10%". First, non-inferiority. If passed, superiority will be performed.	12 months
Time analysis	Demonstrate that Median LCS decreases the time of analysis per patient.	12 months

Collaborators and Investigators

• Sponsor: Median Technologies
• Investigators: Principal Investigator: Anil Vachani, MD, University of Pennsylvania

Study record dates

Study Major Dates

• Study Start (Actual): September 21, 2022
• Primary Completion (Actual): January 31, 2025
• Study Completion (Actual): March 12, 2025

Study Registration Dates

• First Submitted: December 20, 2024
• First Submitted That Met QC Criteria: December 20, 2024
• First Posted (Actual): December 30, 2024

Study Record Updates

• Last Update Posted (Actual): May 6, 2025
• Last Update Submitted That Met QC Criteria: May 5, 2025
• Last Verified: May 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: MT-LCS-004

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No