LEAF (Liver Tumor dEtection And classiFication AI)

NCT06859840 | Recruiting

• 1 other identifier: LEAF
• Study Type: interventional
• Target: 2,500
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Liver Malignancy

Keywords

Artificial Intelligence; liver malignancy

Outcome Measures

Primary Outcomes (1)

• Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)

  Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)

  Within 4 weeks after enrollment

Secondary Outcomes (2)

• 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

Study Arms (1)

LEAF

EXPERIMENTAL

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)

Interventions

LEAF(Liver tumor dEtection And classiFication AI) DEVICE

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.

LEAF

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• Patients who have been diagnosed with malignant liver tumor;
• Patients who underwent liver transplantation;
• Low quality image, severe artifacts and noise.

Sponsors & Collaborators

• Zhejiang University: lead

Study Sites (1)

the First Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310009, China

RECRUITING

Central Study Contacts

Qi Zhang

CONTACT

13819137113; qi.zhang@zju.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: LEAF is a deep learning-based model that takes non-contrast CT scans as input and performs both tri-class diagnosis (benign, malignant, or non-tumor) and potential lesion localization for liver tumors.

Study Record Dates

• First Submitted: February 28, 2025
• First Posted: March 5, 2025
• Study Start: July 17, 2026
• Primary Completion (Estimated): August 10, 2026
• Study Completion (Estimated): November 10, 2026
• Last Updated: July 17, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)