Standalone Observational Study Assessing the Performance... | NCT06576232

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

NCT06576232

Sponsor

Median Technologies

Status

Completed

Last Update Posted

Aug 28, 2024Actual

Enrollment

1,147Actual

Phase

Not provided

Conditions

High Risk Cancer

Interventions

Median LCS

Countries

United States
Spain

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Aug 2024

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Sep 21, 2022Actual

Primary Completion Date

Jul 24, 2024Actual

Completion Date

Aug 21, 2024Actual

First Submitted Date

Aug 23, 2024

First Submission Date that Met QC Criteria

Aug 26, 2024

First Posted Date

Aug 28, 2024Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Aug 26, 2024

Last Update Posted Date

Aug 28, 2024Actual

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

1,147Actual

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
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.		eyonis LCS

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
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

Measure	Description	Time Frame
Sensitivity > 70% when Specificity=70%		12 months
Specificity > 70% when Sensitivity=70%		12 months
AUC of LROC > 0.75

Other Outcomes

Not provided

Eligibility Module

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.

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

50 Years

Maximum Age

80 Years

Standard Ages

AdultOlder Adult

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

Sampling Method

Probability Sample