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 range18 Years – 80 Years

SexALL

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.

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 range18 Years – 80 Years

SexALL

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