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

NCT07841600 | Not Yet Recruiting

• 1 other identifier: AI-NC-2026-01
• Study Type: interventional
• Target: 2,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Pulmonary Nodule; Clinical Decision Support

Keywords

Artificial Intelligence; Low-Dose Computed Tomography; Pulmonary Nodule Management; Lung Cancer Screening; Human-AI Collaboration

Outcome Measures

Primary Outcomes (1)

• 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.

  At the index LDCT report (Day 0)

Secondary Outcomes (8)

• Proportion of Participants With Under-Management

  At the index LDCT report (Day 0)

• Proportion of Expert-Defined Referral Cases Missed by the Final Report

  At the index LDCT report (Day 0)

• Proportion of Participants With Inappropriate Management Escalation

  At the index LDCT report (Day 0)

• Change in Decision Appropriateness After AI Review in the AI-Assisted Arm

  At the index LDCT report (Day 0)

• Physician Response to the AI Recommendation

  During the index LDCT reporting session (Day 0)

Study Arms (2)

AI-Assisted Pulmonary Nodule Reporting Workflow

EXPERIMENTAL

Physicians first record and lock an initial pulmonary nodule management decision without viewing AI results. They then review locked-version AI outputs, including nodule characteristics, estimated malignancy risk, and a management recommendation, and issue the final physician-signed report. Physicians may accept, modify, or reject the AI recommendation. The AI cannot automatically sign reports or directly instruct participants.

Other: AI-Assisted Pulmonary Nodule Reporting Workflow

Conventional Pulmonary Nodule Reporting Workflow

ACTIVE COMPARATOR

Physicians interpret LDCT examinations and issue pulmonary nodule management recommendations using the participating branch's conventional clinical reporting workflow. Study AI output is not displayed.

Other: Conventional Pulmonary Nodule Reporting Workflow

Interventions

AI-Assisted Pulmonary Nodule Reporting Workflow OTHER

A locked-version artificial intelligence decision-support workflow applied after the physician records an initial assessment. The system displays pulmonary nodule location, size, density, morphologic features, estimated malignancy risk, and a suggested management category. The physician retains final responsibility. If the system fails, times out, or produces an abnormal output, the case returns to manual reporting. No online learning or automatic model updating is allowed during the trial.

AI-Assisted Pulmonary Nodule Reporting Workflow

Conventional Pulmonary Nodule Reporting Workflow OTHER

Standard manual LDCT interpretation and pulmonary nodule management recommendation by qualified physicians using the participating branch's routine reporting process, without access to study AI output.

Conventional Pulmonary Nodule Reporting Workflow

Eligibility Criteria

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

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Affiliated Hospital of Guangzhou Medical University: lead

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD

Study Record Dates

• First Submitted: September 20, 2026
• First Posted: September 25, 2026
• Study Start: October 1, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: September 25, 2026

Record last verified: 2026-09