NCT06859840

LEAF(Liver Tumor dEtection And classiFication AI)

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

• Status: Active Not Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 10,000 (estimated)
• Sponsor: Zhejiang University · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Accepted

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.

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: 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 ultimate goal is to establish a superior AI model for early screening of liver cancer based on CTimaging.

Conditions

• Hepatocellular Carcinoma

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2025-07-15
• Primary completion: 2026-03-01
• Completion: 2030-09-15
• First posted: 2025-03-05
• Last updated: 2026-03-10

Locations

1 site across 1 country: China