LEAF (Liver Tumor dEtection And classiFication AI) (LEAF)

September 12, 2026 updated by: TingBo Liang, Zhejiang University

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

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Estimated)

2500

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Zhejiang
      • Hangzhou, Zhejiang, China, 310009
        • the First Affiliated Hospital, School of Medicine, Zhejiang University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

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.

Exclusion criteria:

Patients who have been diagnosed with malignant liver tumor;

Patients who underwent liver transplantation;

Low quality image, severe artifacts and noise.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: 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.
Device: 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 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, while remaining blinded to the LEAF results. 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)
Time Frame: Within 4 weeks after enrollment
Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
Within 4 weeks after enrollment

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification
Time Frame: Within 4 weeks after enrollment
Within 4 weeks after enrollment
Clinical utility: number of AI-detected and originally overlooked liver malignant lesions
Time Frame: Within 4 weeks after enrollment
recalled and pathologically confirmed
Within 4 weeks after enrollment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Zhejiang University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 17, 2026

Primary Completion (Actual)

August 12, 2026

Study Completion (Estimated)

November 10, 2026

Study Registration Dates

First Submitted

February 28, 2025

First Submitted That Met QC Criteria

March 4, 2025

First Posted (Actual)

March 5, 2025

Study Record Updates

Last Update Posted (Actual)

September 15, 2026

Last Update Submitted That Met QC Criteria

September 12, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LEAF

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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