Hepatocellular Carcinoma (HCC)
Conditions
Keywords
HCC, Risk Prediction Model, Multimodal Data, Artificial Intelligence, Chronic Hepatitis B
Brief summary
The investigators propose to construct and validate an early HCC risk prediction model in a multicenter retrospective cohort of hepatitis B-related fibrosis/cirrhosis patients, using multimodal data encompassing longitudinal clinical data, serum glycomics profiles, and liver biopsy histopathological images.
Detailed description
The investigators propose to construct and validate an early HCC risk prediction model in a multicenter retrospective cohort of hepatitis B-related fibrosis/cirrhosis patients, using multimodal data encompassing longitudinal clinical data, serum glycomics profiles, and liver biopsy histopathological images. Approximately 2,000 participants (200 HCC and 1,800 non-HCC) will be included. Model performance will be assessed by AUROC, sensitivity, and specificity through training and validation procedures, with the goal of developing a scalable HCC risk prediction method and software tool for clinical application.
Interventions
OTHERNo intervention (observational study)
No intervention (observational study)
Sponsors
Beijing Friendship Hospital
The Second Hospital & Clinical Medical School, Lanzhou University
Beijing Ditan Hospital
Yuncheng Central Hospital
Guangzhou Eighth People's Hospital, Guangzhou Medical University.
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Aged ≥18 years, both sexes eligible. 2. Diagnosed with hepatitis B-related liver fibrosis or cirrhosis based on clinical assessment or liver histopathology. 3. Undergoing antiviral treatment. 4. Follow-up duration between 0.5- 5 years. 5. Availability of baseline data and at least two longitudinal follow-up visits. 6. Clinical outcomes during the follow-up period are traceable.
Exclusion criteria
1. Diagnosis of Hepatocellular carcinoma (HCC) or liver transplantation at baseline or within 6 months after baseline/enrollment. 2. Coexisting other chronic liver diseases, such as genetic/metabolic liver diseases, drug-induced liver injury, or severe steatotic liver disease. 3. Coexisting other malignancies (except those considered cured).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To evaluate the predictive performance of an early-stage HCC risk prediction model integrating longitudinal clinical data and serum glycomics biomarkers in patients with hepatitis B-related fibrosis | Aug, 2026 - Aug, 2027 | Evaluation metrics include AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and calibration. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To evaluate the predictive performance of an early HCC risk prediction model integrating liver histopathological imaging with longitudinal clinical and serum glycomics data in patients with hepatitis B-related fibrosis | Aug, 2026 - Aug, 2028 | Evaluation metrics include AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and calibration. |
Countries
China
Contacts
CONTACTJialing Zhou
Outcome results
None listed