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

NCT07052773 | Completed

• 2 other identifiers: 0077-23-ASMCprivately funded
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

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

Keywords

Lung Cancer Screening; Artificial Intelligence; Low-Dose Computed Tomography; Pulmonary Nodule Detection

Outcome Measures

Primary Outcomes (2)

• Sensitivity of LCDS for Detection of Solid Pulmonary Nodules

  Proportion of true positive cases correctly identified by the AI-based Lung Cancer Detection System (LCDS) out of all subjects with radiologist-confirmed pulmonary nodules (Ground Truth).

  Through study completion, an average of 1 year

• Specificity of LCDS for Detection of Solid Pulmonary Nodules

  Proportion of true negative cases correctly identified by the LCDS out of all subjects without pulmonary nodules, as defined by the radiologist consensus ground truth.

  Through study completion, an average of 1 year

Secondary Outcomes (2)

• Area Under the ROC Curve (AUC) for LCDS Performance

  Through study completion, an average of 1 year

• False Positive Rate per Case

  Through study completion, an average of 1 year

Study Arms (1)

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).

Device: Lung Cancer Detection System (LCDS)

Interventions

Lung Cancer Detection System (LCDS) 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.

Retrospective LDCT Scan Cohort

Eligibility Criteria

• Age: 50 Years - 79 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The population in this study reflects the typical clinical screening cohort targeted by current lung cancer early detection guidelines and is suitable for evaluating the diagnostic accuracy of an AI-based decision support tool under real-world conditions.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Genesis Medical AI: lead

Study Sites (1)

Assuta Medical Center

Tel Aviv, Israel

Location

Study Officials

• Arnon Makori, MD

  Assuta Medical Center

  PRINCIPAL INVESTIGATOR

• Shay Cohen, MBA

  Genesis Medical AI

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 22, 2025
• First Posted: July 7, 2025
• Study Start: October 26, 2023
• Primary Completion: May 5, 2024
• Study Completion: May 5, 2024
• Last Updated: July 7, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

IL (1)