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

NCT07815483 | Not Yet Recruiting

• 5 other identifiers: 2026YS-265No. DRFC2023015No. PW2024D-01No. 202340212No. 24SF1903903
• Study Type: observational
• Target: 16,880
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Pulmonary Nodules; Lung Neoplasms; Artificial Intelligence (AI)

Keywords

Pulmonary Nodules; Lung Neoplasms; Diagnostic Imaging; Artificial Intelligence; Deep Learning

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of the AI model for pulmonary nodule malignancy

  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.

  Up to 24 months after enrollment

Secondary Outcomes (1)

• Risk stratification accuracy across different nodule sizes

  Up to 24 months

Study Arms (2)

Benign Pulmonary Nodule Group

Participants with pulmonary nodules diagnosed as benign, confirmed by either pathological examination or stability on follow-up imaging. No intervention is applied.

Malignant Pulmonary Nodule Group

Participants with pulmonary nodules diagnosed as malignant, confirmed by pathological examination. No intervention is applied.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Consecutively enrolled, representative patients (aged ≥18 years, with a maximum nodule diameter of ≥3 mm and ≤30 mm) who underwent chest CT for routine clinical care and were found to have pulmonary nodules, including solid, part-solid, and pure ground-glass nodules.

You may qualify if:

• (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.

You may not qualify if:

• 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. -

Sponsors & Collaborators

• Shanghai East Hospital: lead
• Shanghai Chest Hospital of Shanghai Jiao Tong University: collaborator
• RenJi Hospital: collaborator

MeSH Terms

Conditions

Multiple Pulmonary Nodules; Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 12 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 8, 2026
• First Posted: September 11, 2026
• Study Start: October 1, 2026
• Primary Completion (Estimated): October 1, 2028
• Study Completion (Estimated): October 1, 2029
• Last Updated: September 11, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share