Liver elastography-based risk score for predicting hepatocellular carcinoma risk.

Area of Science:

• Hepatology
• Oncology
• Medical Diagnostics

Background:

• Liver stiffness measurement (LSM) via vibration-controlled transient elastography is a key method for assessing liver fibrosis.
• Hepatocellular carcinoma (HCC) is a significant concern in patients with chronic hepatitis.
• Accurate prediction of HCC development is crucial for effective patient management and surveillance.

Purpose of the Study:

• To develop and validate a universal risk score for predicting HCC development.
• To identify routinely available predictors of HCC in patients with chronic hepatitis.
• To create an accessible online tool for HCC risk assessment.

Main Methods:

• A risk prediction model (HCC-LSM) was developed in a hepatitis B virus (HBV) training cohort (n=2251).
• The model was validated in an independent HBV cohort (n=1191) and a non-viral chronic liver disease (CLD) cohort (n=1189).
• An online risk evaluation tool, Liver Elastography-Based Hepatocellular Carcinoma Risk Score (LEBER), was developed using ChatGPT4.0.

Main Results:

• Eight predictors were identified, with LSM showing a significant dose-response relationship with HCC incidence.
• The HCC-LSM model demonstrated excellent predictive performance in HBV cohorts (C-index=0.866 and 0.852) and good performance in the CLD cohort (C-index=0.769).
• The model outperformed six previous models and an online tool (LEBER) was created for practical application.

Conclusions:

• The developed HCC-LSM risk score, based on LSM, accurately predicts HCC development in chronic hepatitis patients.
• This accessible and reliable risk assessment tool can enhance HCC surveillance strategies.
• The LEBER online tool facilitates the practical application of the HCC-LSM score for risk evaluation.