Liver Elastography-based Risk Score for Predicting Hepatocellular Carcinoma Risk
Menée auprès de 2 251 patients infectés par le virus de l'hépatite B (durée médiane de suivi : 3,2 ans) puis validée sur 1 191 patients supplémentaires infectés également par le virus VHB (durée médiane de suivi : 5,7 ans) et 1 189 patients présentant une maladie hépatique chronique d'origine non virale (durée médiane de suivi : 3,3 ans), cette étude évalue la performance d'un score, basé sur 7 facteurs cliniques et le niveau de rigidité hépatique mesuré par élastographie transitoire contrôlée par vibration, pour prédire le risque de carcinome hépatocellulaire
Background & Aims : Liver stiffness measurement (LSM) via vibration-controlled transient elastography (VCTE) accurately assesses fibrosis. We aimed to develop a universal risk score for predicting hepatocellular carcinoma (HCC) development in patients with chronic hepatitis.
Methods : We systematically selected predictors and developed the risk prediction model (HCC-LSM) in the HBV training cohort (n = 2,251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1,191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1,189, median follow-up of 3.3 years). A HCC risk score was then constructed based on a nomogram. An online risk evaluation tool (LEBER) was developed using ChatGPT4.0.
Results : Eight routinely available predictors were identified, with LSM levels showing a significant dose-response relationship with HCC incidence (P < .001 by log-rank test). The HCC-LSM model exhibited excellent predictive performance in the HBV training cohort (C-index = 0.866) and the HBV validation cohort (C-index = 0.852), with good performance in the extrapolation CLD cohort (C-index = 0.769). The model demonstrated significantly superior discrimination compared to six previous models across the three cohorts. Cut-off values of 87.2 and 121.1 for the HCC-LSM score categorized participants into low-, medium-, and high-risk groups. An online public risk evaluation tool (LEBER; http://ccra.njmu.edu.cn/LEBER669.html) was developed to facilitate the use of HCC-LSM.
Conclusion : The accessible, reliable risk score based on LSM accurately predicted HCC development in patients with chronic hepatitis, providing an effective risk assessment tool for HCC surveillance strategies.
Journal of the National Cancer Institute , résumé, 2023