HCC Risk Prediction Model Based on Multimodal Data in Chronic Hepatitis B

Development and Application of an HCC Risk Prediction Model Based on Multimodal Data in Chronic Hepatitis B

We 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.

We 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.

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).