AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 10,333
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy of the AI System for malignancy diagnosis
研究概览
简要总结
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.
详细描述
This study aims to evaluate the effectiveness of AI-human collaboration in liver tumor diagnosis by embedding real-time AI analysis into conventional multiphasic contrast-enhanced CT (CE-CT) workflows. Specifically, this prospective validation trial will assess diagnostic performance in detecting and characterizing hepatic lesions, particularly malignancies, evaluate the feasibility and efficiency of workflow integration, and determine the potential clinical impact on treatment decision-making and patient management.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age range 18 years and above
- •Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage
- •Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional
- •Complete imaging data that meet AI system analysis requirements.
排除标准
- •History of recent upper-abdominal surgery (within 30 days) or major hepatobiliary-pancreatic surgery affecting liver evaluation (e.g., liver transplantation or Whipple procedure); patients with prior simple cholecystectomy or single-lesion interventional procedures are not excluded
- •History of recent hepatic trauma (within 30 days)
- •Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)
- •Missing required imaging phases (required at least non-contrast, arterial, and venous phases) or inadequate scan range (e.g., lower-abdomen CT such as pelvic or rectal scans not covering the liver)
结局指标
主要结局
Diagnostic accuracy of the AI System for malignancy diagnosis
时间窗: Up to 90 days
Measures the patient-level diagnostic accuracy of the AI system for differentiating malignant vs. non-malignant lesions. The primary metric is the Area under the Receiver Operating Characteristic Curve (AUC). The primary analysis will test the one-sided superiority hypothesis H1: AUC \> 0.90 against H0: AUC \<= 0.90. The trial will be considered successful if the lower bound of the 95% Confidence Interval (CI) for the AUC is greater than 0.90.
次要结局
- Secondary diagnostic performance(Up to 90 days)
- Lesion screening performance(Up to 90 days)
- Detection discordance(Up to 90 days)
- Amended radiological report(Up to 90 days)
研究者
Yu Shi
Deputy director of department of radiology
Shengjing Hospital