LEAF (Liver Tumor dEtection And classiFication AI)

Официальное название

Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy

Обзор

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.

Подробное описание

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.

Вмешательства

• Устройство LEAF(Liver tumor dEtection And classiFication AI)
  The LEAF (Liver tumor dEtection And classiFication AI) model will assist in image interpretation. Patients with positive results for liver malignancy while not reported in standard-of-care CT report will be reviewed by a prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case and decide whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the

Первичные конечные точки

• Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI) [Срок оценки: Within 4 weeks after enrollment]

Вторичные конечные точки (2)

• AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification [Срок оценки: Within 4 weeks after enrollment]
• Clinical utility: number of AI-detected and originally overlooked liver malignant lesions [Срок оценки: Within 4 weeks after enrollment]

Критерии участия

Критерии включения

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.

Критерии исключения

Patients who have been diagnosed with malignant liver tumor;

Patients who underwent liver transplantation;

Low quality image, severe artifacts and noise.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Китай · 1 центр

• the First Affiliated Hospital, School of Medicine, Zhejiang University — Ханчжоу

Идентификаторы

NCT: NCT06859840 · LEAF

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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