Use of liver stiffness measurement for HCC risk stratification in metabolic dysfunction-associated steatotic liver

Binu V John1,2, Dustin R Bastaich1,3, Yangyang Deng1,4

  • 1Division of Gastroenterology and Hepatology, Department of Medicine, Miami VA Medical System, Miami, Florida, USA.

PubMed

Insights

Liver stiffness measurement (LSM) predicts hepatocellular carcinoma (HCC) risk in metabolic dysfunction-associated steatotic liver disease (MASLD). Consider HCC surveillance for non-cirrhotic MASLD patients with diabetes and LSM ≥10 kPa.

Area of Science:

  • Hepatology
  • Oncology
  • Radiology

Background:

  • Metabolic dysfunction-associated steatotic liver disease (MASLD) is a rapidly growing cause of hepatocellular carcinoma (HCC).
  • A significant portion of MASLD-HCC cases occur without cirrhosis, highlighting a need for predictive tools.
  • Current methods for predicting HCC risk in MASLD are insufficient.

Purpose of the Study:

  • To investigate the association between liver stiffness measurement (LSM) and HCC risk in patients with MASLD.
  • To determine if specific LSM thresholds can predict HCC development.
  • To identify patients with MASLD who would benefit from cost-effective HCC surveillance.

Main Methods:

  • Retrospective analysis of the Veterans Analysis of Liver Disease (VALID) cohort.
  • Inclusion of patients with MASLD who underwent transient elastography.
  • Utilized multivariable Cox proportional models to assess the relationship between LSM and HCC risk.

Main Results:

  • Increased LSM was significantly associated with higher HCC risk in MASLD patients.
  • HCC risk rose by 18% for every 5 kPa increase in LSM.
  • Annual HCC incidence per 100 person-years increased with higher LSM values, particularly in patients with diabetes and LSM ≥10 kPa.

Conclusions:

  • Liver stiffness measurement (LSM) is a valuable predictor of HCC risk in MASLD.
  • HCC surveillance is recommended for non-cirrhotic MASLD patients who have diabetes and an LSM of ≥10 kPa.
  • These findings aid in optimizing HCC surveillance strategies for high-risk MASLD populations.
Abstract