An observational study in Pulmonary Nodules, Lung Neoplasms and Lung Diseases, sponsored by Shanghai East Hospital. Active, not recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-26.
Sponsored by Shanghai East Hospital · Observational
This is a retrospective, multicenter observational study evaluating the clinical value of combining high-resolution (1024×1024 matrix) targeted CT scans with an artificial intelligence (AI) deep learning system to distinguish between benign and malignant pulmonary nodules. By analyzing existing medical records and CT images of adult patients (aged 18 and older) with pulmonary nodules (3-30 mm) who have confirmed diagnoses (via postoperative pathology）, this study aims to assess the diagnostic accuracy and efficiency of the AI system. The study plans to enroll approximately 10,000 cases from three hospitals in Shanghai. The primary goal is to evaluate the AI system's diagnostic performance (such as sensitivity, specificity, and Area Under the ROC Curve) and compare it with the performance of radiologists, ultimately exploring whether this combined approach can provide more accurate clinical diagnoses and reduce unnecessary procedures.
193 studies on the registry are indexed under Multiple Pulmonary Nodules; 60 are open to participants now.
This study's planned enrollment of 10,000 is above the median of 300 across 90 observational studies indexed under Multiple Pulmonary Nodules.
Browse Multiple Pulmonary Nodules studies →Shanghai East Hospital is the lead sponsor of 95 studies on the registry; 50 are open to participants now.
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This study population consists of adult patients (aged ≥ 18 years) who underwent chest high-resolution targeted CT scans at Shanghai East Hospital (lead site), Shanghai Renji Hospital, or Shanghai Chest Hospital between January 1, 2015, and January 1, 2025. Participants were retrospectively identified via the hospital PACS system using keywords such as "pulmonary nodule" and "targeted CT scan." Eligible patients had pulmonary nodules (diameter ≥ 3 mm and ≤ 30 mm) with complete imaging data, including scan slice thickness 1 mm, and images with a 1024×1024 scan matrix. The diagnosis of benign or malignant status was confirmed by surgical resection pathology (gold standard) or at least 12 months of clinical follow-up (stability/shrinkage for benign; progression/growth for malignant). The planned enrollment is approximately 10,000 cases, comprising roughly 7,000 malignant and 3,000 benign nodules, with a 1:1 ratio estimation targeted for statistical power.
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Exclusion Criteria:
This is a retrospective observational study. No intervention was administered. The AI-assisted diagnostic system was used as an observational assessment tool.
The Area Under the Receiver Operating Characteristic Curve (AUC) for AI Model Diagnosis of Pulmonary Nodules
Using pathological results as the gold standard, calculate the area under the operating characteristic curve (AUC) of the AI model for diagnosing pulmonary nodules at the nodule level, and report the 95% confidence interval. If there are multiple nodules in the same patient, adjust according to patient clustering.
Time frame: Baseline to 1 month after obtaining pathological results
Sensitivity of AI model in diagnosing pulmonary nodules
Using pathological results as the gold standard, calculate the sensitivity of the AI model in diagnosing pulmonary nodules, defined as the true positive number/(true positive number+false negative number), and summarize it in proportion and 95% confidence interval.
Time frame: Baseline to 1 month after obtaining pathological results
Specificity of artificial intelligence models in diagnosing pulmonary nodules
Using pathological results as the gold standard, calculate the specificity of the AI model in diagnosing pulmonary nodules, defined as the true negative number/(true negative number+false positive number), and summarize it in proportion and 95% confidence interval.
Time frame: Baseline to 1 month after obtaining pathological results
Consistency between AI and Radiologists (Kappa)
Agreement in diagnosing benign and malignant pulmonary nodules between the AI and radiologists, measured by Kappa statistics.
Time frame: Assessed during the data analysis phase (September 2026 to December 2026)
Diagnostic Time
The time taken by radiologists to review the CT images and provide a diagnosis.
Time frame: Assessed during the data analysis phase (September 2026 to December 2026)
Plan to share: No — Given the retrospective observational nature of this study, the ethics committee approved a waiver of informed consent. To strictly protect patient privacy and comply with institutional data security policies, individual participant data will not be shared with external researchers.
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is active, not recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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Shanghai East Hospital