Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 2,500
- 试验地点
- 2
- 主要终点
- Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI)
研究概览
简要总结
This study aims to assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer that may be missed by routine clinical workflow.
详细描述
This prospective real-world trial will be conducted at FAHZU, a high-volume tertiary medical center in mainland China.
LEAF will be deployed within the hospital information system through the DAMO Intelligent Medical Imaging interface, allowing it to flag potential liver lesions in real time. Approximately 2500 consecutive patients undergoing non-contrast CT examinations will be enrolled starting in July 2026. All incoming non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation.
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 (Participant)
入排标准
- 年龄范围
- 18 Years 至 90 Years（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 non-contrast chest or abdominal CT examination with liver coverage;
- •Patients with an established diagnosis of cirrhosis;
- •Patients with an established diagnosis of extrahepatic cancer.
排除标准
- •Patients who have been diagnosed with malignant liver tumor;
- •Patients who underwent liver transplantation;
- •Low quality image, severe artifacts and noise.
研究组 & 干预措施
LEAF
Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be enrolled within three weeks. Non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice.
干预措施: LEAF(Liver tumor dEtection And classiFication AI) (Device)
结局指标
主要结局
Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI)
时间窗: Within 1 weeks after enrollment
Sensitivity、Specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)
时间窗: Within 4 weeks after enrollment
Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
Detection efficiency in liver tumor assisted by LEAF(Liver tumor dEtection And classiFication AI)
时间窗: Complete the statistics within six months after the patient is fully enrolled, and it is expected to take 2 years from the start of the study
Sensitivity、Specificity、PPV
次要结局
- TNM stage(1 day (evaluate through CT imaging before surgery))
- OS(From diagnosis of liver cancer to 5 years later)
- AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification(Within 1 weeks after enrollment)
- Clinical utility: number of AI-detected primary overlooked liver malignancy lesions,(Within 1 weeks after enrollment)
- AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification(Within 4 weeks after enrollment)
- Clinical utility: number of AI-detected and originally overlooked liver malignant lesions(Within 4 weeks after enrollment)
研究者
TingBo Liang
Professor
Zhejiang University