Active — Not RecruitingNot Applicable

LEAF(Liver Tumor dEtection And classiFication AI)

China10,000 participantsStarted 2025-07-15

Plain-language 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.

Who can participate

Age range

18 Years – 80 Years

Sex

ALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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.

Questions worth asking your doctor

Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.

1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?

Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.

Questions for the trial coordinator

The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.

1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?

A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.

What they're measuring

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

Timeframe: 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)

Timeframe: 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)

Timeframe: 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

Trial details

NCT IDNCT06859840

SponsorZhejiang University

Sponsor typeOTHER

Study typeINTERVENTIONAL

Primary completion2025-10-31

View on ClinicalTrials.gov More Hepatocellular Carcinoma trials

Plain-language summary

Who can participate

Age range

18 Years – 80 Years

Sex

ALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

What they're measuring

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

Timeframe: 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)

Timeframe: 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)

Timeframe: 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