Clinical Evaluation of the Lung Cancer AI-based Decision Support Tool in Low-Dose Lung CT
Key facts
- Where
- Tel Aviv, IzraelSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 50 and 79 years old?
Where in Izrael
- Assuta Medical CenterTel Aviv, IzraelNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Genesis Medical AI
- Trial code
- NCT07052773
- Record updated
- 2025. július 7.
- Original description
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 full original descriptionShow less
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:
* 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.
Inclusion: 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: 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.