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.
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.
Study Type
OBSERVATIONAL
Enrollment
2,000
No intervention (observational study)
Beijing Friendship Hospital, Capital Medical University
Beijing, Xicheng, China
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
Evaluation metrics include AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and calibration.
Time frame: Aug, 2026 - Aug, 2027
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
Evaluation metrics include AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and calibration.
Time frame: Aug, 2026 - Aug, 2028
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