aMAP Score and Its Combination With Liver Stiffness Measurement Accurately Assess Liver Fibrosis in Chronic Hepatitis B Patients

Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM

BACKGROUND

Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associated with an unfavourable prognosis. Therefore, timely diagnosis of LC in CHB patients is crucial.

OBJECTIVE

This study aimed to enhance the diagnostic accuracy of LC in CHB patients by integrating liver stiffness measurement (LSM) with traditional indicators.

METHODS

The study participants were randomly divided into training and internal validation sets. Employing the least absolute shrinkage and selection operator (LASSO) and random forest-recursive feature elimination (RF-RFE) for feature selection, we developed both traditional logistic regression and five machine learning models (k-nearest neighbors, random forest (RF), artificial neural network, support vector machine and eXtreme Gradient Boosting). Performance evaluation included receiver operating characteristic curves, calibration curves and decision curve analysis. Shapley additive explanations (SHAP) was employed to improve the interpretability of the optimal model.

RESULTS

We retrospectively included 1609 patients with CHB, among whom 470 were diagnosed with cirrhosis. Cirrhosis was diagnosed based on histological confirmation or clinical assessment, supported by characteristic findings on abdominal ultrasound and corroborative evidence such as thrombocytopenia, varices or imaging from CT/MRI. In the internal validation, the RF model achieved an accuracy above 0.80 and an AUC above 0.80, with outstanding calibration ability and clinical net benefit. Additionally, the model exhibited excellent predictive performance in an independent external validation set. The SHAP analysis indicated that LSM contributed the most to the model. The model still showed strong discriminative power when using only LSM or traditional indicators alone.

CONCLUSIONS

Machine learning models, especially the RF model, can effectively identify LC in CHB patients. Integrating LSM with traditional indicators can enhance diagnostic performance.

Identification of abdominal MRI features associated with histopathological severity and treatment response in autoimmune hepatitis

To identify abdominal contrast magnetic resonance imaging (MRI) features associated with histopathological severity, and treatment response in autoimmune hepatitis (AIH).

PATIENTS AND METHODS

AIH patients who had abdominal contrast MRI within 3 months of liver biopsy were retrospectively enrolled. Histopathological severity, liver volume, MRI features, laboratory tests, and treatment response were collected. MRI and serum models were constructed through stepwise univariate and multivariate logistic regression for diagnosing severe histopathology and predicting insufficient response (IR).

RESULTS

One hundred AIH patients were included (median age: 57.0 years, 79.0% female). For diagnosing severe portal inflammation, reticular fibrosis and volume ratio of segment V-VIII to total liver (SV-SVIII/TLV) achieved an area under the receiver operating characteristic curve (AUROC) of 0.765 (95% CI 0.670-0.860). Severe confluent necrosis was modeled using hepatic fissure widening, reticular fibrosis, and volume ratio of segment I-III to segments IV-VIII, achieving an AUROC of 0.796 (95% CI 0.708-0.885). Severe histological activity was modeled using ascites, and SV-SVIII/TLV achieved an AUROC of 0.748 (95% CI 0.649-0.847). To diagnose cirrhosis, ascites, reticular fibrosis, and the volume ratio of segment I to the total liver were employed, yielding an AUROC of 0.833 (95% CI 0.716-0.949); IR (transaminases and/or immunoglobulin G remaining unnormal after 6 months of immunosuppressive treatment) was modeled using ascites, gallbladder wall edema, and transient hepatic attenuation difference, achieving an AUROC of 0.796 (95% CI 0.691-0.902).

CONCLUSION

The MRI models demonstrated relatively good performance in evaluating histopathological severity and treatment response. Combining MRI and serum models could enhance diagnostic and prognostic efficacy.

KEY POINTS

Question Abdominal contrast MRI may help clinicians better evaluate the histopathological severity and treatment response of autoimmune hepatitis (AIH), but there is currently limited research. Findings Models based on MRI features perform well in diagnosing severe portal inflammation, confluent necrosis, histological activity, and cirrhosis, as well as predicting insufficient response. Clinical relevance Abdominal contrast MRI, combined with serological parameters, provides a new and stronger noninvasive method for clinically assessing AIH progression and treatment.

Liver elastography-based risk score for predicting hepatocellular carcinoma risk

BACKGROUND

Liver stiffness measurement (LSM) via vibration-controlled transient elastography 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 hepatitis B virus (HBV) training cohort (n = 2251, median follow-up of 3.2 years). The HCC-LSM model was validated in an independent HBV validation cohort (n = 1191, median follow-up of 5.7 years) and a non-viral chronic liver disease (CLD) extrapolation cohort (n = 1189, median follow-up of 3.3 years). An HCC risk score was then constructed based on a nomogram. An online risk evaluation tool Liver Elastography-Based Hepatocellular Carcinoma Risk Score (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 6 previous models across the 3 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.

Tenofovir amibufenamide: A potential alternative for chronic hepatitis B treatment

Tenofovir amibufenamide (TMF) is a novel prodrug of tenofovir that demonstrates a promising safety and efficacy profile. A recent study by Peng et al compared TMF with tenofovir alafenamide in the treatment of chronic hepatitis B. The findings indicated that both medications offer similar efficacy in terms of viral response and alanine aminotransferase normalization. Notably, TMF showed potential advantages in lipid management, as it did not significantly affect cholesterol levels, unlike tenofovir alafenamide. This correspondence highlights the need for further research to evaluate the long-term safety and efficacy of TMF, its impact on cardiovascular risk, and its use in specific patient populations.

Serum ECM1 is a promising biomarker for staging and monitoring fibrosis in patients with chronic hepatitis B

It is critical to assess the extent and progression of liver fibrosis for patients to receive suitable treatments, but its diagnostic methods remain unmet. Extracellular matrix protein 1 (ECM1) has previously been reported to be a key factor in the induction and progression of liver fibrosis. However, little is known about the use of ECM1 as a biomarker to evaluate fibrosis. In a CCl4-induced mouse model of liver fibrosis, the present study demonstrated that ECM1 decreased with gradually increasing fibrosis. Using biopsy as a reference, the serum ECM1 levels decreased with increasing fibrosis stage in 247 patients with liver fibrosis, but there were no significant changes between fibrosis stage 2 and stage 0-1. To improve the performance of ECM1, age, platelet count, and ECM1 concentration were combined to calculate an EPA (ECM1-platelet-age) score (ranging from 0 to 10). The areas under the receiver operating characteristic curve of the EPA scores for the detection of F⩾2, F⩾3, and F4 were 0.6801, 0.7377, and 0.8083, respectively, which showed a comparable or significantly greater diagnostic performance for assessing fibrosis than that of the AST/ALT ratio, APRI score, or FIB-4 score. In HBV patients following antiviral treatment, the dynamics of the EPA score depended on the status of liver fibrosis development. The accuracy of the EPA score in predicting fibrosis regression and progression was 66.00% and 71.43%, respectively, while that of the LSM, another useful method for monitoring hepatic fibrosis changes during treatment, was only 52.00% and 7.14%, respectively. Compared with healthy controls, there were lower levels of serum ECM1 in HBV patients and individuals with HCV infection, MAFLD, ALD, PBC, and DILI. These findings suggested that individuals with reduced ECM1 levels may have a risk of developing liver injury, and further examinations or medical care are needed. In conclusion, the ECM1-containing EPA score is a valuable noninvasive test for staging fibrosis and predicting the progression of liver fibrosis. Additionally, ECM1 alone is an indicator for distinguishing patients with liver injury from healthy controls.

Staging Chronic Hepatitis B Related Liver Fibrosis with Diffusion-weighted MRI-based Virtual Elastography: Comparisons with Serum Fibrosis Indexes

AIM

To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI)-based virtual magnetic resonance (MR) elastography and serum fibrosis indexes for staging liver fibrosis in patients with chronic hepatitis B (CHB).

MATERIALS AND METHODS

This retrospective study included 145 patients with CHB. Virtual shear modulus (μDiff) was derived from DWI acquisition with b values of 200 and 1500/mm2. Aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 index (FIB-4), S index, Fibro Q and gamma-glutamyl transpeptidase to platelet ratio (GPR) were calculated. The diagnostic efficacies of μDiff and serum indexes for staging liver fibrosis were compared.

RESULTS

μDiff, APRI, FIB-4, S index, Fibro Q, and GPR increased as the hepatic fibrosis progressed (r=0.23-0.52, P<0.05). Areas under the curves (AUCs) of μDiff were 0.725, 0.817 and 0.764 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively. μDiff had greater AUC over FIB-4 (0.686) and Fibro Q (0.638) for advanced fibrosis (P<0.05). The AUCs of combination of μDiff with APRI, FIB-4, S index, Fibro Q, and GPR individually were 0.779, 0.772, 0.763, 0.728, and 0.756 for significant fibrosis, 0.856, 0.842, 0.834, 0.831, and 0.834 for advanced fibrosis, 0.811, 0.818, 0.784, 0.835, and 0.788 for cirrhosis, respectively. The AUCs of the combinations were significantly higher than individual serum index for advanced fibrosis and cirrhosis (all P<0.05).

CONCLUSION

DWI-based virtual MR elastography could estimate liver fibrosis stages in patients with CHB, which outperformed FIB-4 and Fibro Q for advanced fibrosis. The combination of μDiff with each serum index appears superior to individual serum index for advanced fibrosis and cirrhosis.

Diagnostic Value of the Color Doppler Ultrasound Standardized Semiquantitative Score Combined With Sound Touch Elastography in Liver Fibrosis in Patients With Chronic Hepatitis B: A Retrospective Cohort Study

PURPOSE

This study aims to evaluate the diagnostic value of standardized semiquantitative scoring of color Doppler ultrasound combined with liver stiffness measurement (LSM) of sound touch elastography (STE) in chronic hepatitis B (CHB) patients, providing a reference for the liver fibrosis diagnosis.

METHODS

We performed ultrasound and STE on CHB patients, with liver biopsies as the benchmark. We compared the differences in ultrasound standardized semiquantitative scoring and LSM among patients with different stages of liver fibrosis, and evaluated the diagnostic efficacy of significant liver fibrosis using receiver operating characteristic (ROC) curves and the area under the ROC curve alone or in combination.

RESULTS

The total scores of ultrasound semiquantitative scoring and LSM showed statistically significant differences among patients with different stages of liver fibrosis (P < 0.05). There was no statistically significant difference in the total scores of S0 and S1 stages or in the LSM values (P > 0.05). However, the total scores and LSM values for patients at stages S2 and S3 were both higher than those at stage S0, and increased with the severity of fibrosis staging, with statistically significant differences (P < 0.05). The results of the ROC curve analysis showed that the combined diagnosis of significant liver fibrosis with ultrasound standardized semiquantitative scoring and STE had an area under the curve of 0.807, which was significantly greater than using ultrasound standardized semiquantitative scoring (0.694, P < 0.05) or shear wave elastography alone (0.706, P < 0.05).

CONCLUSIONS

Color Doppler ultrasound with standardized semiquantitative scoring combined with STE examination can detect significant liver fibrosis (≥S2) in CHB patients.

WFUMB Guideline/Guidance on Liver Multiparametric Ultrasound: Part 1. Update to 2018 Guidelines on Liver Ultrasound Elastography

The World Federation for Ultrasound in Medicine and Biology (WFUMB) endorsed the development of this document on multiparametric ultrasound. Part 1 is an update to the WFUMB Liver Elastography Guidelines Update released in 2018 and provides new evidence on the role of ultrasound elastography in chronic liver disease. The recommendations in this update were made and graded using the Oxford classification, including level of evidence (LoE), grade of recommendation (GoR) and proportion of agreement (Oxford Centre for Evidence-Based Medicine [OCEBM] 2009). The guidelines are clinically oriented, and the role of shear wave elastography in both fibrosis staging and prognostication in different etiologies of liver disease is discussed, highlighting advantages and limitations. A comprehensive section is devoted to the assessment of portal hypertension, with specific recommendations for the interpretation of liver and spleen stiffness measurements in this setting.

Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis

BACKGROUND & AIMS

aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance.

METHODS

A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125).

RESULTS

The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).

CONCLUSIONS

The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.

Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients

The aim of this review is to provide updated information on the clinical use of non-invasive serum and imaging-based tests for fibrosis assessment in chronic hepatitis B (CHB) virus infection. In recent years, non-invasive tests (NIT) have been increasingly used to determine eligibility for treatment. Liver biopsy is still considered the gold standard for assessing inflammatory activity and fibrosis staging, but it is an invasive procedure with inherent limitations. Simple serum markers such as APRI and FIB-4 are limited by indeterminate results but remain useful initial tests for fibrosis severity if imaging elastography is not available. Point-of-care US-based elastography techniques, such as vibration-controlled transient elastography or 2D shear wave elastography, are increasingly available and have better accuracy than simple serum tests for advanced fibrosis or cirrhosis, although stiffness cut-offs are variable based on E-antigen status and inflammatory activity. Current NITs have poor diagnostic performance for following changes in fibrosis with antiviral therapy. However, NITs may have greater clinical utility for determining prognosis in patients with CHB that have advanced disease, especially for the development of hepatocellular carcinoma and/or liver decompensation. Algorithms combining serum and imaging NITs appear promising for advanced fibrosis and prognostic risk stratification.

An Autologous Macrophage-Based Phenotypic Transformation-Collagen Degradation System Treating Advanced Liver Fibrosis

In advanced liver fibrosis (LF), macrophages maintain the inflammatory environment in the liver and accelerate LF deterioration by secreting proinflammatory cytokines. However, there is still no effective strategy to regulate macrophages because of the difficulty and complexity of macrophage inflammatory phenotypic modulation and the insufficient therapeutic efficacy caused by the extracellular matrix (ECM) barrier. Here, AC73 and siUSP1 dual drug-loaded lipid nanoparticle is designed to carry milk fat globule epidermal growth factor 8 (MFG-E8) (named MUA/Y) to effectively inhibit macrophage proinflammatory signals and degrade the ECM barrier. MFG-E8 is released in response to the high reactive oxygen species (ROS) environment in LF, transforming macrophages from a proinflammatory (M1) to an anti-inflammatory (M2) phenotype and inducing macrophages to phagocytose collagen. Collagen ablation increases AC73 and siUSP1 accumulation in hepatic stellate cells (HSCs) and inhibits HSCs overactivation. Interestingly, complete resolution of liver inflammation, significant collagen degradation, and HSCs deactivation are observed in methionine choline deficiency (MCD) and CCl4 models after tail vein injection of MUA/Y. Overall, this work reveals a macrophage-focused regulatory treatment strategy to eliminate LF progression at the source, providing a new perspective for the clinical treatment of advanced LF.

eALT-F: A New Non-Invasive Staging Method to Identify Medium to High-Risk Patients with HCC from Ultra-High HBV Viral Load Population - China, 2010-2023

Background

The objective of this study was to examine the clinical characteristics of individuals with ultra-high hepatitis B virus (HBV) viral load and develop a novel staging method for chronic hepatitis B (CHB) that can more effectively identify patients with medium to high hepatocellular carcinoma (HCC) risk.

Methods

A total of 2,118 patients with HBV DNA >1×107 IU/mL who visited Peking University People's Hospital between January 2010 and March 2023 were enrolled retrospectively. Clinical data from the first visit were obtained and analyzed. The traditional phases and new 'eALT-F' stages were compared to evaluate the risk of HCC.

Results

In the overall patients, more than one-third of the patients were under 30 years old. Additionally, a small proportion of older people (>60 years) also had ultra-high HBV viral load (4.3%). 9.1% and 6.7% of individuals with ultra-high HBV viral load showed FIB-4>3.25 and aMAP≥50, respectively. In the traditional stages of CHB, which are based on HBeAg and alanine aminotransferase (ALT) [the upper limit of normal (ULN) ALT level at 40 IU/L for both men and women], regardless of phase, a certain proportion of patients were at risk of developing HCC (4.1%, 6.4%, 25.0%, and 20.3%). However, in the new 'eALT-F' stages, which are based on HBeAg, ALT (the ULN of ALT level at 30 IU/L for men and 19 IU/L for women), and/or FIB-4 levels (>1.45), aMAP≥50 was only observed in chronic hepatitis patients with positive or negative HBeAg (6.4% and 22.1%, respectively).

Conclusions

The 'eALT-F' staging method, based on HBeAg, ALT (males: the ULN of ALT was 30 IU/L, females: 19 IU/L) and/or FIB-4 levels, was more effective in identifying medium to high-risk patients with HCC from patients with ultra-high HBV viral load than the traditional staging methods.