Clinical Evaluation of the Lung Cancer AI-based Decision Support Tool in Low-Dose Lung CT

NCT ID: NCT07052773

Last Updated: 2025-07-07

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 100 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-10-26
• Study Completion Date: 2024-05-05

Brief Summary

The goal of this observational study is to clinically validate the accuracy of an AI-based decision support tool-the Lung Cancer Detection System (LCDS)-for detecting lung nodules in asymptomatic adults aged 50-79 with a history of heavy smoking who underwent low-dose chest CT (LDCT) scans.

The main questions it aims to answer are:

• Can the LCDS accurately detect the presence of solid pulmonary nodules on LDCT scans, as measured by sensitivity and specificity?
• How does the LCDS's performance compare to existing AI systems using the Area Under the Curve-Receiver Operating Characteristic (AUC/ROC) Curve?

Researchers will compare the AI-based interpretations to a ground truth established by consensus among radiologists' double-readings to see if the LCDS can accurately classify cases as 'lung nodule presence' or 'lung nodule absence'.

Participants will:

• Have their de-identified LDCT scans (collected between 2018 and 2023) reviewed retrospectively.
• Be evaluated through the LCDS tool, which will classify cases based on lung nodule presence.

Contribute to performance evaluation using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ROC analysis.

Conditions

Lung Cancer Screening

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

Retrospective LDCT Scan Cohort

This cohort consists of 100 de-identified low-dose CT (LDCT) chest scans collected from individuals aged 50-79 years, with a ≥20 pack-year smoking history. These scans, acquired between 2018 and 2023 during routine lung cancer screening, include cases both with and without radiologically confirmed pulmonary nodules. The scans will be retrospectively evaluated by the AI-based Lung Cancer Detection System (LCDS).

Lung Cancer Detection System (LCDS)

Intervention Type: DEVICE

An AI-based decision support software designed to detect solid pulmonary nodules on LDCT chest scans. In this study, the LCDS is applied retrospectively to 100 previously acquired LDCT scans, and its performance is compared to a ground truth established by double-read radiologist reports with arbitration.

Interventions

Lung Cancer Detection System (LCDS)

An AI-based decision support software designed to detect solid pulmonary nodules on LDCT chest scans. In this study, the LCDS is applied retrospectively to 100 previously acquired LDCT scans, and its performance is compared to a ground truth established by double-read radiologist reports with arbitration.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Undergone an LDCT scan between 2018 and 2023, while a diagnosis record exists.
• Age is between 50-79 years old.
• History of smoking at least a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.

Exclusion Criteria

• History of lung cancer: Subjects with a previous diagnosis of lung cancer may be excluded to ensure that the study focuses on detecting new cases or evaluating the progression of the disease.
• Prior lung nodule detection: Individuals who have previously undergone LDCT scans with documented lung nodules that required medical intervention may be excluded to avoid potential confounding factors in the analysis.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 79 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Genesis Medical AI

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Arnon Makori, MD

Role: PRINCIPAL_INVESTIGATOR

Assuta Medical Center

Shay Cohen, MBA

Role: STUDY_DIRECTOR

Genesis Medical AI

Locations

Assuta Medical Center

Tel Aviv, , Israel

Countries

Israel

Other Identifiers

privately funded

Identifier Type: OTHER

Identifier Source: secondary_id

0077-23-ASMC

Identifier Type: -

Identifier Source: org_study_id