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 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.

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.

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.