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

Completed

• Conditions: High Risk Cancer; Lung Cancer

• Registration Number: NCT06751576
• Lead Sponsor: Median Technologies

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-09-21
• Study First Submitted: 2024-12-20
• Study First Submitted QC: 2024-12-20
• Study First Posted: 2024-12-30
• Primary Completion: 2025-01-31
• Study Completion: 2025-03-12
• Status Verified: 2025-05
• Last Update Submitted: 2025-05-05
• Last Update Posted: 2025-05-06

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 480

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
∆ 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.	12 months	Demonstrate that patient diagnosis with Median LCS is improved compared to without Median LCS.

Secondary Outcome Measures

Name	Time	Method
Sensitivity at max Youden	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.
Specificity at max Youden	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.
∆ AUC of LROC > 0	12 months	Demonstrate that Median LCS improves clinician's performance in finding detection and diagnosis.
Recall rates for non-cancer patients (Specificity)	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.
Recall rates for cancer patients (Sensitivity)	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.
Time analysis	12 months	Demonstrate that Median LCS decreases the time of analysis per patient.

Trial Locations

Locations (5)

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