A Multicenter Cohort Study of AI-Based Methods for Pulmonary Nodule Diagnosis and Follow-up

This is an observational, multicentre study. The primary objective of this study was to evaluate the diagnostic performance and clinical applicability of artificial intelligence models for pulmonary nodule segmentation, benign-malignant risk stratification, and follow-up management. We will collect CT images and medical data from participants at several hospitals. Participants will not receive any drugs or medical interventions.

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Key information

Conditions

Pulmonary Nodules Artificial Intelligence (AI) Lung Diseases Lung Neoplasms Multiple Pulmonary Nodules Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

(1) Age ≥18 years; (2) At least one non-calcified pulmonary nodule (diameter ≥3 mm and ≤30 mm) detected on chest CT; (3) Agreement to participate in the study and provision of written informed consent.

Exclusion criteria

Poor CT image quality with severe artifacts that preclude AI-based analysis;
Expected survival <12 months or inability to complete follow-up;
History of prior ipsilateral lung malignancy or local treatment. -

Treatment and study plan

Primary outcomes

Diagnostic performance of the AI model for pulmonary nodule malignancy

Time frame: Up to 24 months after enrollment

Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for classifying benign and malignant nodules, using pathology results or longitudinal stability as the reference standard.

Secondary outcomes

Risk stratification accuracy across different nodule sizes

Time frame: Up to 24 months

The ability of the AI model to correctly stratify nodules into low, intermediate, and high-risk categories compared to clinical judgement.

Sponsors and collaborators

Lead sponsor

Shanghai East Hospital

Other

Collaborators

RenJi Hospital
Shanghai Chest Hospital of Shanghai Jiao Tong University

Registry information

Official study title

A Prospective, Multicentre Cohort Study of Artificial Intelligence-based Methods for Pulmonary Nodule Segmentation, Benign-Malignant Risk Stratification, and Follow-up

Important dates

Study start: 2026
Primary completion: 2028
Study completion: 2029
First posted: Sep 11, 2026
Registry last updated: Sep 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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