Lung Cancer ScreeningGenMedAILCSD1NCT07052773
Clinical Evaluation of the Lung Cancer AI-based Decision Support Tool in Low-Dose Lung CT
An observational study of Lung Cancer Screening, run by Genesis Medical AI at 1 site in Israel.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 79 years
- Conditions the study is for
- Lung Cancer Screening
- What is being tested?
- Lung Cancer Detection System (LCDS)
- Type of treatment
- Medical device
- How many people
- 100 participants
- Where
- 1 siteIsrael
- Study dates
- 26 Oct 2023to 5 May 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 79 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Blinded Retrospective Study to Clinically Validate the Accuracy of the Lung Cancer Detection System (LCDS) AI-based Decision Support Tool for Lung Cancer Low-Dose CT
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:
Read more
- 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
- Being tested
- Lung Cancer Detection System (LCDS)
- Keywords
- Lung Cancer Screening, Artificial Intelligence, Low-Dose Computed Tomography, Pulmonary Nodule Detection
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Device: Lung Cancer Detection System (LCDS) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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.
Measured: Through study completion, an average of 1 year
Secondary outcomes (2)
Area Under the ROC Curve (AUC) for LCDS Performance
The area under the receiver operating characteristic (ROC) curve comparing AI classifications with the radiologist-defined ground truth for nodule detection.
Measured: Through study completion, an average of 1 year
False Positive Rate per Case
The average number of false positive nodule detections made by the Lung Cancer Detection System (LCDS) per LDCT scan. A false positive is defined as a nodule detected by the AI system that was not confirmed by the radiologist-established ground truth.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jul 2025
- Study start26 Oct 2023
- Primary completion5 May 2024
- Study completion5 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Cancer ScreeningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Genesis Medical AI
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Arnon Makori, MD, Principal investigator, Assuta Medical CenterShay Cohen, MBA, Study director, Genesis Medical AI
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07052773
- Sponsor's protocol ID
- 0077-23-ASMC
- Other IDs
- privately funded
- Acronym
- GenMedAILCSD1
- Status last verified
- Jul 2025
- First submitted
- 22 Jun 2025
- Last updated
- 7 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IPD will not be shared due to the following reasons: * Medical imaging data sensitivity: The study involves low-dose CT (LDCT) chest scans containing highly sensitive medical imaging data that, despite de-identification efforts, may retain identifying characteristics inherent to medical images * Institutional privacy policies: Assuta Medical Center's data governance policies and patient consent frameworks do not permit external data sharing beyond the original study scope * Re-identification risk: Advanced imaging analysis techniques could potentially re-identify patients from anatomical features visible in CT scans, even after standard de-identification procedures Israeli privacy legislation: Compliance with Israeli Privacy Protection Law and healthcare data regulations that restrict sharing of patient-derived medical data Ethics Committee limitations: The original ethics approval and informed consent did not include provisions for data sharing.
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Blinded Retrospective Study to Clinically Validate the Accuracy of the Lung Cancer Detection System (LCDS) AI-based Decision Support Tool for Lung Cancer Low-Dose CT
- ClinicalTrials.gov ID
- NCT07052773
- Acronym / sponsor ID
- GenMedAILCSD1 / 0077-23-ASMC
- Conditions
- Lung Cancer Screening
- Keywords
- Lung Cancer Screening, Artificial Intelligence, Low-Dose Computed Tomography, Pulmonary Nodule Detection
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Genesis Medical AIIndustry (pharma, biotech, devices)
- Study start
- 26 Oct 2023 (actual)
- Primary completion
- 5 May 2024 (actual)
- Study completion
- 5 May 2024 (actual)
- First posted
- 7 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retrospective LDCT Scan Cohort | – | Device: Lung Cancer Detection System (LCDS)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). |
Who may qualify
- Ages
- 50 to 79 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | Retrospective LDCT Scan Cohort |
Primary outcome
Sensitivity of LCDS for Detection of Solid Pulmonary Nodules
Time frame: Through study completion, an average of 1 year
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).
Specificity of LCDS for Detection of Solid Pulmonary Nodules
Time frame: Through study completion, an average of 1 year
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.
2 secondary outcomes
Area Under the ROC Curve (AUC) for LCDS Performance
Time frame: Through study completion, an average of 1 year
False Positive Rate per Case
Time frame: Through study completion, an average of 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2025.
- Individual participant dataSharing planned: No. IPD will not be shared due to the following reasons: * Medical imaging data sensitivity: The study involves low-dose CT (LDCT) chest scans containing highly sensitive medical imaging data that, despite de-identification efforts, may retain identifying characteristics inherent to medical images * Institutional privacy policies: Assuta Medical Center's data governance policies and patient consent frameworks do not permit external data sharing beyond the original study scope * Re-identification risk: Advanced imaging analysis techniques could potentially re-identify patients from anatomical features visible in CT scans, even after standard de-identification procedures Israeli privacy legislation: Compliance with Israeli Privacy Protection Law and healthcare data regulations that restrict sharing of patient-derived medical data Ethics Committee limitations: The original ethics approval and informed consent did not include provisions for data sharing.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Arnon Makori, MD principal investigator
Assuta Medical Center
Shay Cohen, MBA study director
Genesis Medical AI
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer Screening, run by Genesis Medical AI at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 7 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of LCDS for Detection of Solid Pulmonary Nodules (time frame: Through study completion, an average of 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2025.