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Completed

NCT Number: NCT06751576

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

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.

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Key information

Conditions

High Risk Cancer Lung Cancer Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

50 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

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

Loading trial locations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Other names: eyonis LCS

Primary outcomes

  1. ∆ 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.

    Time frame: 12 months

    Demonstrate that patient diagnosis with Median LCS is improved compared to without Median LCS.

Secondary outcomes

  1. Sensitivity at max Youden

    Time frame: 12 months

    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.

  2. Specificity at max Youden

    Time frame: 12 months

    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.

  3. ∆ AUC of LROC > 0

    Time frame: 12 months

    Demonstrate that Median LCS improves clinician's performance in finding detection and diagnosis.

  4. Recall rates for non-cancer patients (Specificity)

    Time frame: 12 months

    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.

  5. Recall rates for cancer patients (Sensitivity)

    Time frame: 12 months

    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.

  6. Time analysis

    Time frame: 12 months

    Demonstrate that Median LCS decreases the time of analysis per patient.

Sponsors and collaborators

Lead sponsor

Median Technologies

Industry

Registry information

Official study title

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)

Acronym: RELIVE

Important dates

Study start
2022
Primary completion
2025
Study completion
2025
First posted
Dec 30, 2024
Registry last updated
May 6, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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