Clinical Utility of Artificial Intelligence-Assisted Management Decisions for Pulmonary Nodules Detected by Low-Dose Computed Tomography in Health Screening: A Multicenter, Prospective, Cluster-Randomized Controlled Trial
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 2,000
- 主要终点
- Proportion of Participants With an Appropriate Final Management Recommendation
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Screening
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
AI-Assisted Pulmonary Nodule Reporting Workflow
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.
干预措施: AI-Assisted Pulmonary Nodule Reporting Workflow (Other)
Conventional Pulmonary Nodule Reporting Workflow
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.
干预措施: Conventional Pulmonary Nodule Reporting Workflow (Other)
结局指标
主要结局
Proportion of Participants With an Appropriate Final Management Recommendation
时间窗: At the index LDCT report (Day 0)
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.
次要结局
- 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))
- Proportion of AI-Assisted Cases With System Failure(During the index LDCT reporting session (Day 0))
- Proportion of Participants With a Pulmonary Nodule-Related Management Action Within 90 Days(90 days after the index LDCT examination (allowable window, ±14 days))
- Proportion of Participants With a Reporting-Workflow-Related Management or Data Security Adverse Event(From the index LDCT report through 90 days after the index examination (allowable window, ±14 days))
研究者
Jianxing He
PhD
The First Affiliated Hospital of Guangzhou Medical University