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 Type
OBSERVATIONAL
Enrollment
100
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.
Assuta Medical Center
Tel Aviv, Israel
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).
Time frame: 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.
Time frame: Through study completion, an average of 1 year
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.
Time frame: 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.
Time frame: Through study completion, an average of 1 year
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