Risk prediction for HBV-related liver cancer
Research on Risk Assessment and Intervention of HBV-Related Liver Cancer Based on Multimodal Data Fusion
Sun Yat-sen University · NCT07160946
This project tests an AI-based follow-up system to try to improve early detection of liver cancer in people with chronic hepatitis B and related risk factors.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 6000 (estimated) |
| Ages | 30 Years to 75 Years |
| Sex | All |
| Sponsor | Sun Yat-sen University (other) |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT07160946 on ClinicalTrials.gov |
What this trial studies
This is a prospective, multicenter cohort enrolling people positive for hepatitis B surface antigen who meet cirrhosis or other risk criteria and following them with an AI-driven, multimodal data fusion follow-up system. The program combines clinical information, imaging, elastography, pathology where available, and other markers to stratify patients by liver cancer risk and guide surveillance intervals. Enrolled participants receive structured, risk-based management and scheduled follow-up aimed at earlier detection of hepatocellular carcinoma. The primary intent is to increase the early diagnosis rate of liver cancer through targeted surveillance in a large sample across participating centers.
Who should consider this trial
Good fit: Adults positive for hepatitis B surface antigen who also have liver cirrhosis or at least one risk factor such as type 2 diabetes, family history of liver cirrhosis/liver cancer, long-term heavy alcohol use, Metavir fibrosis score F3 or above, or Fibroscan (LSM) ≥ 8.0 kPa.
Not a fit: People already diagnosed with malignant tumors, those post-liver transplantation, individuals with HIV infection, patients with expected survival under one year, or those with marked organ dysfunction or severe psychiatric illness are excluded and unlikely to benefit.
Why it matters
Potential benefit: If successful, the AI-driven follow-up could identify liver cancer earlier in high-risk hepatitis B patients, allowing timelier treatment and improved outcomes.
How similar studies have performed: Similar approaches using risk models and AI for hepatocellular carcinoma surveillance have shown promising early results but are not yet widely validated in large prospective multicenter cohorts.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: must meet (a) and any one of (b) to (g). * (a).Positive for Hepatitis B surface antigen; (b).Ultrasound/CT/MR indicates liver cirrhosis; (c). Type II diabetes; (d). Has family history of liver cirrhosis/ liver cancer; (e). Long term alcohol consumption history (\>5 years), equivalent to alcohol consumption of ≥ 40g/d for males and ≥ 20g/d for females; (f). Liver histology Metavir fibrosis score F3 or above; (g). Fibroscan value (LSM) ≥ 8.0kPa. Exclusion Criteria: * (a).Diagnosed with malignant tumors; (b). Post liver transplantation; (c). Infected with HIV; (d). Expected survival time\<1 year; (e). With marked organ dysfunctions(heart, brain, kidney, lung, endocrine system, blood, etc.) or psychiatric patients.
Where this trial is running
Guangzhou, Guangdong
- Third Affiliated Hospital of Sun Yat-sen University — Guangzhou, Guangdong, China (RECRUITING)
Study contacts
- Study coordinator: Bingliang Prof. Lin
- Email: linbingl@mail.sysu.edu.cn
- Phone: +86-20-85252081
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: HBV-related Liver Cirrhosis