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

Clinical Research on the Use of Abdominal CT Combined With AI for Early Screening

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Hepatocellular Carcinoma
• Intervention / Treatment: Device: LEAF(Liver tumor dEtection And classiFication AI)

Detailed Description

This research project intends to utilize multiphase contrast-enhanced and non-contrast CT images from 10000 patients with a full spectrum of liver tumors (such as HCC（hepatocellular carcinoma）, ICC（intrahepatic cholangiocarcinoma ）, META（Metastasis）, etc.), confirmed by the pathological gold standard at our hospital. Through a pre-established AI model, the 3D contours of various liver masses will be delineated. In collaboration with senior physicians from our hospital's radiology department and hepatobiliary pancreatic surgery department, the AI-drawn contours will be refined to obtain more accurate 3D mass models, thereby enhancing the validation efficacy of the model. By incorporating more radiological data, the precision of the model will be improved, boosting its recognition capabilities and laying a solid foundation for subsequent prospective clinical trials: the research will be conducted over a period of two weeks. 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. for cases where the model indicates malignancy and CT report malignancy clinical Soc procedure will be followed, and all patients with none malignany reported by model or CT report will be follow up via telephone every three mouths. The ultimate goal is to establish a superior AI model for early screening of liver cancer based on CT imaging.

• Study Type: Interventional
• Enrollment (Estimated): 10000
• Phase: Not Applicable

Contacts and Locations

Study Locations

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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Design Details

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

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: LEAF; 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); 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
TNM stage	Staging of liver cancer	1 day (evaluate through CT imaging before surgery)
OS	overall survival	From diagnosis of liver cancer to 5 years later

Collaborators and Investigators

• Sponsor: Zhejiang University

Study record dates

Study Major Dates

• Study Start (Actual): July 15, 2025
• Primary Completion (Actual): October 31, 2025
• Study Completion (Estimated): September 15, 2030

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): May 4, 2026
• Last Update Submitted That Met QC Criteria: April 27, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Hepatocellular Carcinoma; Artificial Intelligence
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Neoplasms by Histologic Type; Digestive System Neoplasms; Digestive System Diseases; Liver Diseases; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Liver Neoplasms; Carcinoma; Carcinoma, Hepatocellular
• 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