AI-Assisted Management of Pulmonary Nodules Found on Low-Dose CT in Health Screening

N/ANot Yet RecruitingNCT07841600

The First Affiliated Hospital of Guangzhou Medical University2,000 enrolled

Overview

Pulmonary nodules are frequently found during low-dose computed tomography (LDCT) health screening. The main challenge is not only detecting nodules, but also recommending the appropriate next step, such as routine follow-up, short-interval imaging follow-up, or specialist evaluation. This multicenter cluster-randomized trial will evaluate whether an artificial intelligence (AI)-assisted reporting workflow improves the appropriateness of pulmonary nodule management decisions in health examination settings without increasing under-management or missed referrals. Participating health examination branches, rather than individual participants, will be randomly assigned in a 1:1 ratio to a conventional reporting workflow or an AI-assisted reporting workflow. All final reports will be reviewed and signed by qualified physicians. An independent expert endpoint committee, blinded to study assignment and AI output, will determine the acceptable management range for each case. Approximately 2,000 adults with pulmonary nodules detected on LDCT will be included across at branches.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

DOUBLE

Enrollment

2,000

Conditions

Pulmonary Nodule Clinical Decision Support

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Age 18 years or older. * Undergoing chest low-dose computed tomography at a participating health examination branch during the study recruitment period. * At least one pulmonary nodule is identified on the index LDCT and requires risk stratification and a management decision regarding follow-up, repeat imaging, or specialist evaluation. * Thin-section reconstructed images are of sufficient quality for clinical interpretation and, where applicable, AI analysis. * Required clinical risk information is available, including age, sex, smoking history, history of malignancy, family history of lung cancer, and available prior chest imaging. * Included under the ethics committee-approved consent, simplified notification, waiver, and/or opt-out process, with no documented refusal of research data use. Exclusion Criteria: * Previously diagnosed lung cancer or currently receiving lung cancer-related treatment. * Known pulmonary metastasis from another malignancy. * Imaging findings that clearly require immediate entry into a lung cancer specialty diagnostic or treatment pathway and are not appropriate for routine pulmonary nodule risk-stratified management. * An urgent thoracic condition requiring immediate management, such as pneumothorax, large pleural effusion, or acute pulmonary embolism. * Severe imaging artifact, incompatible slice thickness or reconstruction, or a lesion type, imaging parameter, or disease extent outside the prespecified locked scope of the AI system. * Previous enrollment in this study. * Explicit refusal of research data use, or another reason judged by the investigator to make inclusion inappropriate.

Outcomes

Primary Outcomes

Proportion of Participants With an Appropriate Final Management Recommendation

Percentage of evaluable participants whose final physician recommendation is within the acceptable management range determined by the blinded independent expert endpoint committee. Recommendations are classified as routine or annual follow-up, short-interval imaging follow-up, or specialist evaluation/referral. The expert committee will complete adjudication using index data within approximately 30 days, but the participant-level outcome is the recommendation made at Day 0.