Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for effectively managing and preventing the possibility of liver dysfunction. Detecting steatosis helps identify individuals at risk of liver-related diseases, such as inflammation (Non-Alcoholic SteatoHepatitis, NASH) and fibrosis. Fibrosis involves the formation of scar tissue in the liver due to chronic inflammation and injury. Early recognition of fibrosis helps categorize patients based on their risk of progression to advanced liver disease. Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) leads to many outcomes, including Metabolic dysfunction-Associated Steatohepatitis (MASH), fibrosis, and cirrhosis. We aim to show that routine clinical tests supported by machine learning offer sufficient information to predict these endpoints.

The research focused on applying various operational research methods such as Linear Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, and Naive Bayes.

The proposed method - FibrAIm - allows the identification of patients at risk of complications related to the conditions analyzed based on inconclusive test results. It can also identify the risk of fibrosis in those whose results appear correct.

Given the results obtained during the trials, FibrAIm could become a valuable tool for diagnosing patients at risk of liver early steatosis and fibrosis by identifying cases based on standardized screening tests.

Liver imaging and transient elastography (TE) are both tools used to assess liver fibrosis and steatosis among people with hepatitis C virus (HCV) infection. However, the diagnostic accuracy of conventional imaging in detecting fibrosis and steatosis in this patient population remains unclear.

To investigate the correlation between steatosis and fibrosis and abnormal findings on liver imaging in patients with HCV.

We conducted a retrospective cross-sectional analysis of patients with HCV at Grady Liver Clinic who had TE exams between 2018-2019. We analyzed the correlation of controlled attenuation parameter and liver stiffness measurement on TE and abnormal findings on liver imaging. Liver imaging findings (hepatic steatosis, increased echogenicity, cirrhosis, and chronic liver disease) were further evaluated for their diagnostic performance in detecting fibrosis (≥ F2, ≥ F3, ≥ F4) and steatosis (≥ S1, ≥ S2, ≥ S3).

Of 959 HCV patients who underwent TE, 651 had liver imaging. Higher controlled attenuation parameter scores were observed in patients with abnormal liver findings (P = 0.0050), hepatic steatosis (P < 0.0001), and increased echogenicity (P < 0.0001). Higher liver stiffness measurement values were also noted in those with abnormal liver (P < 0.0001) and increased echogenicity (P = 0.0026). Steatosis severity correlated with hepatic steatosis (r = 0.195, P < 0.001) and increased echogenicity (r = 0.209, P < 0.001). For fibrosis detection, abnormal liver imaging had moderate sensitivity (81.7%) and specificity (70.4%) for cirrhosis (≥ F4), while cirrhosis on imaging had high specificity (99.2%) but low sensitivity (18.3%). Increased echogenicity showed high specificity (92.8%) but low sensitivity (20.9%) for steatosis detection.

Liver imaging detects advanced fibrosis and steatosis but lacks early-stage sensitivity. Integrating TE with imaging may improve evaluation in patients with HCV.

Non-invasive tests (aspartate aminotransferase-to-platelet ratio index [APRI] and transient elastography [FibroScan]) were recommended in the 2015 WHO guidelines to guide treatment decisions in people with chronic hepatitis B. We updated the systematic review and meta-analysis that informed the 2015 guidelines to inform new cutoffs for non-invasive tests for the diagnosis of significant fibrosis and cirrhosis for the 2024 WHO guidelines for chronic hepatitis B.

We searched PubMed (MEDLINE), Embase, and Science Citation Index Expanded (Web of Science) for studies published in any language between Jan 1, 2014, and Feb 15, 2023. We included all studies that reported cross-sectional data on the staging of fibrosis or cirrhosis with APRI, Fibrosis-4 (FIB-4), and FibroScan compared with liver biopsy as the reference standard in people with chronic hepatitis B. We excluded studies in which the maximum interval between liver biopsy and non-invasive fibrosis test was more than 6 months; that reported on fewer than ten patients with advanced fibrosis or cirrhosis; that were done exclusively in children; and did not report diagnostic accuracy across our prespecified ranges of test cutoffs. The results of this updated search were collated with the meta-analysis that informed the 2015 guidelines. Outcomes of interest were the sensitivity and specificity of non-invasive tests using defined index test cutoffs for detecting significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (F4) based on the METAVIR staging system. We performed meta-analyses using a bivariate random-effects model.

Of 19 933 records identified by our search strategy, 195 were eligible for our systematic review and combined with the 69 studies from the previous meta-analysis to total 264. Two studies were at low risk of bias, 31 studies had unclear risk of bias, and 231 studies had a high risk of bias. Of these 264, 211 studies with 61 665 patients were used in the meta-analysis. For the diagnosis of significant fibrosis (≥F2), sensitivity and specificity were 72·9% (95% CI 70·2-75·5) and 64·7% (95% CI 61·0-68·2) for the APRI low cutoff (>0·3 to 0·7), 30·5% (23·7-38·3) and 92·3% (89·3-94·6) for the APRI high cutoff (>1·3 to 1·7), and 75·1% (72·2-77·7) and 79·3% (76·2-82·2) for FibroScan (>6·0 to 8·0 kPa), respectively. For the diagnosis of cirrhosis (F4), sensitivity and specificity were 59·4% (53·2-65·2) and 73·9% (70·1-77·4) for the APRI low cutoff (>0·8 to 1·2), 30·2% (24·2-36·9) and 88·2% (85·4-90·6) for the APRI high cutoff (>1·8 to 2·2), and 82·6% (77·8-86·5) and 89·0% (86·3-91·2) for FibroScan (>11·0 to 14·0 kPa), respectively. Using a hypothetical population of 1000 unselected patients with chronic hepatitis B with a 25% prevalence of significant fibrosis (≥F2), the APRI low cutoff for significant fibrosis (≥F2) would result in 262 (26·2%) false positives but only 68 (6·8%) false negatives. The FibroScan cutoff would result in 158 (15·8%) false positives and 63 (6·3%) false negatives. In a population with a 5% prevalence of cirrhosis (F4), the APRI low cutoff for cirrhosis (F4) would result in 247 (24·7%) false positives and 21 (2·1%) false negatives and the FibroScan cutoff would result in 105 (10·5%) false positives and nine (0·9%) false negatives.

These findings have informed new thresholds of APRI and FibroScan for diagnosis of significant fibrosis and cirrhosis in the 2024 WHO guidelines on chronic hepatitis B, with an APRI score greater than 0·5 or a FibroScan value greater than 7·0 kPa considered to identify most adults with significant fibrosis (≥F2) and an APRI score greater than 1·0 or a FibroScan value greater than 12·5 kPa to identify most adults with cirrhosis (F4). These patients are a priority for antiviral treatment.

WHO.

Biopsy is the gold standard method for diagnosing liver fibrosis. FibroScan is a non-invasive method of diagnosing liver fibrosis, but it still faces some limitations. This study aimed to establish a nomogram model and identify patients at high risk of advanced liver fibrosis associated with hepatitis B infection.

Data were collected from 375 patients with hepatitis B who underwent liver biopsy. Patients were divided randomly into the training (n = 263) and validation sets (n = 112). Their demographic and clinical characteristics were analyzed using the least absolute shrinkage and selection operator regression (LASSO). A nomogram model was established to predict the fibrosis stage, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) and was compared with other recognized models.

In total, 209 patients with non-advanced fibrosis (S0-1) and 166 patients with advanced fibrosis (S ≥ 2) were included. Hyaluronic acid (HA), laminin, total cholesterol (TC), platelet, and age were entered into the nomogram model based on the LASSO analysis. The nomogram model for predicting advanced fibrosis exhibited a relatively high AUC in the training set. Compared with FIB4 and APRI, the nomogram model showed a better agreement between the actual status and predicted status based on the calibration curve. The nomogram model showed an AUC similar to FibroScan in the validation cohort, and showed high clinical net benefits in the training and validation sets.

Our nomogram model can help identify patients with hepatitis B and advanced liver fibrosis.

To explore the impact of hepatic steatosis measured by MRI-proton density fat fraction (MRI-PDFF) on liver stiffness measurement (LSM) value and its diagnostic performance for staging liver fibrosis in patients with chronic hepatitis B (CHB).

A total of 914 patients with CHB who underwent liver biopsy and MRI-PDFF were retrospectively reviewed. The influence of MRI-PDFF on LSM value was assessed using univariate and multivariate linear analyses. To assess the influence of liver steatosis on the diagnostic performance of LSM, a series of ROC analyses were performed and compared by stratifying patients into non-steatosis (PDFF < 5%) and steatosis (PDFF ≥ 5%) groups according to MRI-PDFF values. The effects of different LSM cut-off values on the false-positive rate in the steatosis cohort were compared using McNemar's test.

LSM values were significantly affected by MRI-PDFF in the entire cohort (B-coefficient: 0.003, p < 0.001), F1 cohort (B-coefficient: 0.005, p < 0.001), and F2 cohort (B-coefficient: 0.003, p = 0.002). Hepatic steatosis was not observed to have a significant influence on the ROC curve of LSM for staging liver fibrosis. Compared with using the cut-off values for the CHB cohort, using relatively higher cut-off values for hepatic steatosis significantly improved the false-positive rate of LSM in the steatosis cohort.

Steatosis significantly influenced LSM, with a higher value in the early stage of liver fibrosis but did not affect the diagnostic efficiency of LSM for staging liver fibrosis. Moreover, using relatively high cut-off values significantly improved the false-positive rate of LSM in CHB patients with steatosis.

The identified correlation between MRI-PDFF and VCTE-measured LSM is not clinically relevant since the diagnostic performance of LSM in staging liver fibrosis is not affected by steatosis. A higher cut-off should be applied in CHB patients with steatosis to improve the false-positive rate.

Steatosis can affect liver stiff measurement (LSM) values in the early stage of liver fibrosis. The diagnostic performance of LSM in staging liver fibrosis is not affected by steatosis. LSM's cutoffs should be increased in patients with steatosis to improve the false-positive rate.

FibroScan, initially designed for assessing cheese maturity, has evolved into a crucial medical tool for liver fibrosis diagnosis. This systematic review explores its development history, functionality, and pros and cons compared to traditional liver biopsy. Precision in various clinical settings is scrutinised, emphasising FibroScan's accuracy in conditions like NAFLD and viral-induced liver disease. The article also delves into its potential in paediatrics, its relevance in monitoring COVID-19-related liver complications, and its role in predicting hepatocellular carcinoma risk, Technical aspects, including transducers, imaging integration, and portability, are examined. Various methods for evaluating liver fibrosis are discussed, highlighting FibroScan's suitability for advanced stages, contrasting with the gold standard of liver biopsy for early stages. The impact of FibroScan on long-term liver conditions is emphasised, focusing on early detection, progression monitoring, reduced invasive biopsies, and hepatocellular carcinoma risk prediction. This systematic review underscores FibroScan's transformative potential in liver disease treatment and predicts ongoing research to enhance early detection, disease monitoring, and explore new clinical applications. Anticipated advances include FibroScan-guided liver biopsy, artificial intelligence data analysis, and point-of-care device development, promising a further revolution in liver disease management. The article concludes with optimistic prospects for FibroScan's future.

Hepatic inflammation in patients with hepatocellular carcinoma (HCC) remains unclear. This study aimed to construct a clinically expedient predictive model to grade hepatic inflammation in HCC patients.

This is a two-center retrospective cohort study of HCC patients comprising Derivation cohort and External Validation cohort of 1201 and 505 patients, respectively. Variables of liver inflammation identified through uni- and multi-variate logistic regression analyses were incorporated into predictive nomograms and applied to Derivation cohort, subject to internal and external validation.

Liver fibrosis severity score, portal hypertension severity, and model for end-stage liver disease-sodium independently predicted hepatic inflammation grade. Performance for distinguishing G1 and non-G1 (≥ G2) patients was good with C-index of 0.810 and 0.817 in Derivation and External Validation cohort, respectively. The nomogram performed poorly to predict grade G2, G3 and G2 + G3, but performed well to predict G4.

Our nomogram exhibited good performance for scaling hepatic inflammation (G1 and G4) in HCC, and could be employed as adjunctive diagnostic tools to guide HCC management strategy.

Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr-/-.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.

Metabolic associated fatty liver disease (MAFLD) is a significant risk factor for the development of hepatocellular carcinoma (HCC). Hepatic stellate cells (HSCs), as key mediators in liver injury response, are believed to play a crucial role in the repair process of liver injury. However, in MAFLD patients, the normal metabolic and immunoregulatory mechanisms of HSCs become disrupted, leading to disturbances in the local microenvironment. Abnormally activated HSCs are heavily involved in the initiation and progression of HCC. The metabolic disorders and abnormal activation of HSCs not only initiate liver fibrosis but also contribute to carcinogenesis. In this review, we provide an overview of recent research progress on the relationship between the abnormal metabolism of HSCs and the local immune system in the liver, elucidating the mechanisms of immune imbalance caused by abnormally activated HSCs in MAFLD patients. Based on this understanding, we discuss the potential and challenges of metabolic-based and immunology-based mechanisms in the treatment of MAFLD-related HCC, with a specific focus on the role of HSCs in HCC progression and their potential as targets for anti-cancer therapy. This review aims to enhance researchers' understanding of the importance of HSCs in maintaining normal liver function and highlights the significance of HSCs in the progression of MAFLD-related HCC.

To determine whether liver stiffness measurement (LSM) indicates liver inflammation in chronic hepatitis B (CHB) with different upper limits of normal (ULNs) for alanine aminotransferase (ALT).

We grouped 439 CHB patients using different ULNs for ALT: cohort I, ≤40 U/L (439 subjects); cohort II, ≤35/25 U/L (males/females; 330 subjects); and cohort III, ≤30/19 U/L (males/females; 231 subjects). Furthermore, 84 and 96 CHB patients with normal ALT (≤40 U/L) formed the external and prospective validation groups, respectively. We evaluated the correlation between LSM and biopsy-confirmed liver inflammation, and determined diagnostic accuracy using area under the curve (AUC). A noninvasive LSM-based model was developed using multivariate logistic regression.

Fibrosis-adjusted LSM values significantly increased with increasing inflammation. The AUCs of LSM in cohorts I, II, and III were 0.799, 0.796, and 0.814, respectively, for significant inflammation (A≥2) and 0.779, 0.767, and 0.770, respectively, for severe inflammation (A=3). Cutoff LSM values in all cohorts for A≥2 and A=3 were 6.3 and 7.5 kPa, respectively. Internal, external, and prospective validations showed high diagnostic accuracy of LSM for A≥2 and A=3, and no significant differences in AUCs among the four groups. LSM and globulin independently predicted A≥2. The AUC of an LSM-globulin model for A≥2 exceeded those of globulin, ALT, and AST, but was similar to that of LSM.

LSM predicted liver inflammation and guided the indication of antiviral therapy for CHB in patients with normal ALT.