LEAF(Liver Tumor dEtection And classiFication AI)

NCT06859840 | Active Not Recruiting

• 1 other identifier: LEAF
• Study Type: interventional
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study plans to utilize multiphase contrast-enhanced and non-contrast CT（Computed Tomography） images from 10000 pathologically confirmed liver tumor patients at our hospital. An AI（artificial intelligence） model will be used to outline the 3D contours of liver masses, which will then be refined by radiologists and hepatobiliary-pancreatic surgeons to enhance model accuracy. By incorporating more imaging data, the model's recognition capabilities will be improved, laying the groundwork for prospective clinical trials and aiming to establish a superior AI model for early liver cancer screening based on CT imaging.

Conditions

Hepatocellular Carcinoma

Keywords

Artificial Intelligence; Hepatocellular Carcinoma

Outcome Measures

Primary Outcomes (3)

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

  Sensitivity、Specificity、PPV

  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

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

  PPV、NPV

  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

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

  AUC

  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

Secondary Outcomes (2)

• TNM stage

  1 day (evaluate through CT imaging before surgery)

• OS

  From diagnosis of liver cancer to 5 years later

Study Arms (1)

LEAF

EXPERIMENTAL

Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be consecutively included, those who have already received treatment for hepatic malignancy and those with poor-quality CT images will be excluded.

Device: LEAF(Liver tumor dEtection And classiFication AI)

Interventions

LEAF(Liver tumor dEtection And classiFication AI) DEVICE

Using the LEAF(Liver tumor dEtection And classiFication AI)model to assist in image interpretation, patients with positive results are recalled for further examination based on the LEAF output information and the original image interpretation, to obtain pathological results and long-term follow-up.

LEAF

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• From 2019 to 2030, our hospital has collected non-contrast and contrast-enhanced CT images from patients with a full spectrum of liver tumors (such as HCC, ICC, META, etc.), all confirmed by the pathological gold standard

You may not qualify if:

• Patients who have undergone upper abdominal surgery. Examples include post-ERCP (Endoscopic Retrograde Cholangiopancreatography) for the pancreas, post-external drainage surgery, esophageal surgery, and gastrectomy, among others.
• Patients who have received systemic treatments such as chemotherapy or traditional Chinese medicine. Examples include chemotherapy for lymphoma, chemotherapy for leukemia, chemotherapy for lung cancer, and comprehensive treatment for liver cancer, etc.
• Patients with poor-quality CT images. Examples include convolution artifacts caused by the inability to place hands on the sides of the body and respiratory artifacts due to poor breath-holding, etc.

Sponsors & Collaborators

• Zhejiang University: lead

Study Sites (1)

the First Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310009, China

Location

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

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: The AI model will be used to identify imaging findings suggestive of liver space-occupying lesions. Senior liver specialists from our hospital's hepatobiliary-pancreatic surgery and radiology departments, who have expertise in AI research and clinical application, will delineate these liver lesions in conjunction with pathological results to develop and refine the model. After model establishment, external multicenter validation will be conducted to assess the model's stability in detecting focal liver lesions across diverse populations. For cases where the model indicates malignancy without clear evidence from medical history or other data, follow-up will be performed to confirm the true value through pathological results. The primary focus will be to evaluate whether the model can improve the detection rate of focal liver lesions requiring intervention in various complex real-world scenarios.

Study Record Dates

• First Submitted: February 28, 2025
• First Posted: March 5, 2025
• Study Start: July 15, 2025
• Primary Completion: October 31, 2025
• Study Completion (Estimated): September 15, 2030
• Last Updated: May 4, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)