FIB‑4 index and NAFLD fibrosis score are useful indicators for screening high‑risk groups of non‑viral hepatocellular carcinoma

Risk-stratified hepatocellular carcinoma surveillance in non-cirrhotic patients with MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly emerging as the leading global liver disorder and is poised to become the primary cause of hepatocellular carcinoma (HCC). Research indicates that nearly 50% of HCC cases in MASLD patients occur without cirrhosis, often presenting with more advanced and larger tumors. Despite this, current guidelines primarily focus on HCC screening in cirrhotic patients, with limited guidance for non-cirrhotic MASLD individuals. This narrative review seeks to identify key risk factors for HCC development, consolidate available screening methods, and propose a practical, risk-stratified algorithm for HCC surveillance in non-cirrhotic MASLD patients. We conducted a comprehensive review of studies published between 2017 and 2023 using PubMed, Embase, and CNKI, focusing on HCC risk factors and emerging screening strategies for non-cirrhotic MASLD cohorts. Key risk factors for HCC development in these patients include male sex, age over 65, hypertension, diabetes, mild alcohol consumption, smoking, dyslipidemia, elevated alanine aminotransferase levels, and a platelet count ≤ 150 × 109/L. Among the screening methods evaluated, circulating free DNA, alpha-fetoprotein (AFP) combined with protein induced by vitamin K absence or antagonist-II (PIVKA-II), and the GALAD score (incorporating Glypican-3, AFP, alpha-1-Antitrypsin, and des-gamma-carboxy prothrombin) demonstrated the highest performance. Based on these findings, we proposed a risk-stratified HCC surveillance algorithm that integrates GALAD and PIVKA-II into the existing sonography and AFP screening protocols. This review aims to provide clinicians with actionable recommendations for HCC screening in non-cirrhotic MASLD patients.

Modified FIB-4 Index in Type 2 Diabetes Mellitus with Steatosis: A Non-Linear Predictive Model for Advanced Hepatic Fibrosis

Background: The Fibrosis-4 (FIB-4) index is widely recommended as a first-tier method for screening advanced hepatic fibrosis; however, its diagnostic performance is known to be suboptimal in patients with Type 2 diabetes mellitus (T2DM). We aim to propose a modified FIB-4, using the parameters of the existing FIB-4, tailored specifically for diabetic patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: A total of 1503 patients who underwent liver biopsy were divided into T2DM (n = 517) and non-T2DM (n = 986) groups. The model was developed using multiple regression analysis in the derivation cohort and validated in the validation cohort. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic (AUC) curves. Results: Among the 1503 individuals, those with T2DM were older, more likely to be male, and had a higher prevalence of advanced hepatic fibrosis (≥F3) compared to non-T2DM individuals. Independent risk factors for advanced fibrosis in T2DM included age, AST, AST/ALT ratio, albumin, triglycerides, and platelet count. The optimized FIB-4 model for T2DM with MASLD (Diabetes Fibrosis Index) demonstrated superior diagnostic accuracy (AUC 0.771) compared to the FIB-4 (AUC 0.735, p = 0.012). The model showed a higher negative predictive value than the original FIB-4 across all age groups in the diabetic group. Conclusions: The newly optimized FIB-4 model for T2DM with MASLD (Diabetes Fibrosis Index), incorporating a non-linear predictive model, improves diagnostic performance (AUC) and the negative predictive value in MASLD with T2DM.

Lysyl oxidase-like 2 as a predictor of hepatocellular carcinoma in patients with hepatitis C virus after sustained virological response

Lysyl oxidase-like 2 (LOXL2) mediates the crosslinking of extracellular collagen, reflecting qualitative changes in liver fibrosis. This study aimed to validate the utility of serum LOXL2 levels as a predictive biomarker for the development of hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV) infection who achieved a sustained virological response (SVR). This retrospective study included 137 patients with chronic HCV infection without history of HCC development and who achieved SVR via direct-acting antiviral therapy. Median LOXL2 levels decreased significantly after SVR achievement (pre-Tx, 2.33 ng/mL; post-Tx, 1.31 ng/mL, p < 0.001). Post-Tx LOXL2 levels, fibrosis-4 index, platelet counts, Wisteria floribunda agglutinin-positive human Mac-2 binding protein levels, and alpha-fetoprotein (AFP) levels were identified as independent predictive factors for post-SVR HCC development in the univariate analysis. The incidence of post-SVR HCC development was significantly higher in patients with post-Tx LOXL2 levels ≥ 2.08 ng/mL and AFP levels ≥ 5.0 ng/mL than in patients with elevated levels of either marker or with lower marker levels. Serum LOXL2 levels can serve as a predictive biomarker for HCC development after achieving SVR. The combination of serum LOXL2 and AFP levels provides robust risk stratification for HCC development after SVR, suggesting an enhanced surveillance strategy.

Evolutive Models, Algorithms and Predictive Parameters for the Progression of Hepatic Steatosis

The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings.