Liver Fibrosis Stage Research Articles

Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.

Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.

How effective are experienced hepatologists at staging fibrosis using non-invasive fibrosis tests in patients with metabolic dysfunction-associated steatotic liver disease?

Sequential use of non-invasive fibrosis tests (NITs) to identify patients with advanced hepatic fibrosis is recommended. However, it remains unclear how reliable clinicians are staging liver fibrosis using combinations of NITs. Our aim was to assess concordance between NIT-based 'clinician fibrosis assessment (CFA)' and histology in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and compare this with established algorithmic approaches. Six experienced hepatologists independently staged 230 MASLD patients for advanced fibrosis (F0-2 vs F3-4) using FIB-4, FIB-4+ELF, FIB-4+ vibration controlled transient elastography (VCTE; Fibroscan™) and FIB-4+ELF+VTCE. Concordance between histology and CFA or algorithmic approaches were assessed. A total of 230 patients were included (median age 54 [22-78] years; 55% female; median FIB-4 1.21 [IQR: 0.78-1.91]; ELF 9.3 [IQR: 8.6-10.2]; VCTE 9.4 [IQR: 6.3-14.3]; 41% F0-1, 22% F2, 21% F3 and 16% F4). Overall, area under the receiver operator curves for histologic F3-4 for the raw tests were 0.84 for FIB-4, 0.86 for ELF and 0.86 for VCTE. Concordance between the hepatologists was good (FIB4, κ = 0.64; FIB-4+ELF, κ = 0.70; FIB-4+VCTE, κ = 0.69; FIB-4+ELF+VCTE, κ = 0.70). Concordance between individual CFA and histology was variable, which was reflected in variability in sensitivity (44%-84%) and specificity (76%-94%). Concordance with histology was better when clinicians used NIT combinations. Purely algorithmic approaches, particularly sequential use of FIB-4 then VCTE, tended to perform better than the CFA. Adhering to the recommended algorithmic approaches using NITs to stage fibrosis tended to perform more accurately than less-structured clinician NIT-based assessments conducted by experienced hepatologists.

Clinical experience of using Polysorb in complex pathogenetic therapy of chronic hepatitis C with severe liver fibrosis

Introduction. Violation of metabolic processes in the gastrointestinal tract in patients with chronic hepatitis C (HCV) leads to the accumulation of toxic metabolic products in the intestine, negatively affecting both the balance of the microbiota and the functional state of hepatocytes. The sorption of toxins released by opportunistic anaerobic bacteria contributes to the restoration of the population of bifidobacteria and lactobacilli, which has a beneficial effect on the functional state of the liver.Aim. To evaluate the clinical and laboratory efficacy and safety of Polysorb as part of complex pathogenetic therapy in patients with HCV with severe liver fibrosis.Materials and methods. The study included 62 patients with HCV in the stage of severe liver fibrosis (F3 according to METAVIR) of both sexes aged 18 to 65 years who were not receiving antiviral therapy. In the study group, pathogenetic therapy was supplemented with Polysorb. Clinical and laboratory parameters were evaluated before and after the course of treatment. The study of the intestinal microbiota was carried out by determining the concentration of volatile fatty acids (VFA) in the intestinal contents: acetic, propionic, butyric, the total content of isoC4 + isoC5 + isoC6 and the value of the anaerobic index by gas-liquid chromatography (chromatograph “Tsvet 100”, Russia).Results and discussion. Complex pathogenetic therapy of patients with HCV (F3), including the enterosorbent Polysorb, increases the effectiveness of treatment for clinical syndromes: right hypochondrium by 35.8%, asthenovegetative – by 13.6%, dyspeptic – from 8 to 22.5% (according to individual symptoms), cholestatic – by 8%, and also improves the biochemical parameters of liver function: p = 0.060; GGTP, p = 0.014 and it helps to stabilize the composition of the microbiota, increasing the total level of LVH (p < 0.05), mainly due to normalization of acetic acid values, improvement of the anaerobic index: before treatment -0.858 ± 0.152, after -0.601 ± 0.163 (p < 0.05).Conclusion. The inclusion of Polysorb in the pathogenetic therapy of patients with HCV (F3) helps to stabilize the composition of the microbiota, while no side effects or adverse events have been recorded.

Reply: Imaging-based noninvasive liver disease assessment for staging liver fibrosis in chronic liver disease: A systematic review supporting the AASLD Practice Guideline.

Reply: Imaging-based noninvasive liver disease assessment for staging liver fibrosis in chronic liver disease: A systematic review supporting the AASLD Practice Guideline.

The Prevalence of Depression and Its Potential Link to Liver Fibrosis in Patients Diagnosed With Chronic Hepatitis C Virus Infection Prior to the Initiation of Direct-Acting Antiviral Treatment.

Introduction Chronic hepatitis C virus (HCV) infection is associated with various extrahepatic manifestations, including depression. This study aimed to determine the prevalence of depression in treatment-naive HCV patients and explore its potential association with liver fibrosis severity. Methodology A consecutive cohort of 50 treatment-naive HCV patients without coinfections was enrolled over six months. Depression was assessed using the Hamilton Depression Rating Scale (HAM-D), and the liver fibrosis stage was evaluated using Fibroscan elastography. Results The cohort comprised 62% females (n=31) and 38% males (n=19), with ages ranging from 27 to 76 years. HAM-D scores indicated mild depression in 78% (n=39) and moderate depression in 16% (n=8) of patients. Notably, patients with mild depression displayed varying degrees of liver fibrosis (F0, F1, and F2), while all patients with moderate depression had advanced fibrosis (F3).Based on the multiple regression model, fibrosis was a statistically significant independent predictor with an unstandardized regression coefficient(B) of3.115 (p=0.007). Conclusions Our findings point to a high prevalence of depression in treatment-naive HCV patients. Interestingly, there might be a link between depression severity and the stage of liver fibrosis, with advanced fibrosis potentially associated with more severe depression.

Effect of phospholipid curcumin Meriva on liver histology and kidney disease in nonalcoholic steatohepatitis: A randomized, double-blind, placebo-controlled trial.

NASH confers an increased liver-related and kidney morbidity. Phospholipid curcumin (Meriva) is a phospholipid formulation with ameliorated systemic curcumin absorption and delivery. We assessed the safety and efficacy of Meriva in NASH. In this double-blind trial, 52 patients with biopsy-proven NASH (71% with stage ≥F2 fibrosis, 58% with stage A2-G2/A2-G3a chronic kidney disease) were randomized 1:1 to receive Meriva 2g/d or placebo for 72 weeks. The primary endpoint was NASH resolution with no worsening of fibrosis. The secondary endpoints included a ≥1 stage liver fibrosis improvement with no NASH worsening; regression of significant (ie, stage ≥F2) fibrosis and CKD; and improvement in renal, glucose, lipid, and inflammatory parameters. We also explored the treatment effect on hepatic activation of NF-kB, a key proinflammatory transcription factor and a major target of curcumin. Fifty-one patients (26 on Meriva and 25 on placebo) completed the trial. Sixteen (62%) patients on Meriva versus 3 (12%) patients on placebo had NASH resolution (RR = 5.33 [95% CI = 1.76-12.13]; p = 0.003). Thirteen (50%) patients on Meriva versus 2 (8%) patients on placebo had ≥1 stage fibrosis improvement (RR = 6.50 [1.63-21.20]; p = 0.008). Eleven (42%) patients on Meriva versus 0 (0%) on placebo had regression of significant liver fibrosis (RR = 18.01 [1.43-36.07]; p = 0.02). Hepatic NF-kB inhibition predicted NASH resolution (AUC = 0.90, 95% CI = 0.84-0.95) and fibrosis improvement (AUC = 0.89, 95% CI = 0.82-0.96). Thirteen (50%) patients on Meriva versus 0 (0%) on placebo had chronic kidney disease regression (RR = 10.71 [1.94-17.99)]; p = 0.004). Compared with placebo, Meriva improved eGFR (difference in adjusted eGFR change: +3.59 [2.96-4.11] mL/min/1.73m 2 /y, p = 0.009), fasting glucose(-17mg/dL; 95% CI = -22, -12), HbA1c (-0.62%; 95% CI = -0.87%, -0.37%), LDL-C (-39mg/dL; 95% CI = -45, -33), triglycerides (-36mg/dL, 95% CI = -46, -26), HDL-C (+10mg/dL; 95% CI = +8, +11), and inflammatory markers. Adverse events were rare, mild, and evenly distributed. In patients with NASH, Meriva administration for 72 weeks was safe, well-tolerated, and improved liver histology, possibly through NF-kB inhibition, kidney disease, and metabolic profile.

A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients

Early assessment and accurate staging of liver fibrosis may be of great help for clinical diagnosis and treatment in patients with chronic hepatitis B (CHB). We aimed to identify serum markers and construct a machine learning (ML) model to reliably predict the stage of fibrosis in CHB patients. The clinical data of 618 CHB patients between February 2017 and September 2021 from Zhejiang Provincial People's Hospital were retrospectively analyzed, and these data as a training cohort to build the model. Six ML models were constructed based on logistic regression, support vector machine, Bayes, K-nearest neighbor, decision tree (DT) and random forest by using the maximum relevance minimum redundancy (mRMR) and gradient boosting decision tree (GBDT) dimensionality reduction selected features on the training cohort. Then, the resampling method was used to select the optimal ML model. In addition, a total of 571 patients from another hospital were used as an external validation cohort to verify the performance of the model. The DT model constructed based on five serological biomarkers included HBV-DNA, platelet, thrombin time, international normalized ratio and albumin, with the area under curve (AUC) values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the training cohort were 0.898, 0.891, 0.907 and 0.944, respectively. The AUC values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the external validation cohort were 0.906, 0.876, 0.931 and 0.933, respectively. The simulated risk classification based on the cutoff value showed that the classification performance of the DT model in distinguishing hepatic fibrosis stages can be accurately matched with pathological diagnosis results. ML model of five serum markers allows for accurate diagnosis of hepatic fibrosis stages, and beneficial for the clinical monitoring and treatment of CHB patients.

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471×10-3, 1.653×10-3, 9.487×10-3, and>30×10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P=0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC=0.876, P=0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC=0.894, P=0.002). The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Liver fibrosis automatic diagnosis utilizing dense-fusion attention contrastive learning network.

Liver fibrosis poses a significant public health challenge given its elevated incidence and associated mortality rates. Diffusion-Weighted Imaging (DWI) serves as a non-invasive diagnostic tool for supporting the identification of liver fibrosis. Deep learning, as a computer-aided diagnostic technology, can assist in recognizing the stage of liver fibrosis by extracting abstract features from DWI images. However, gathering samples is often challenging, posing a common dilemma in previous research. Moreover, previous studies frequently overlooked the cross-comparison information and latent connections among different DWI parameters. Thus, it is becoming a challenge to identify effective DWI parameters and dig potential features from multiple categories in a dataset with limited samples. A self-defined Multi-view Contrastive Learning Network is developed to automatically classify multi-parameter DWI images and explore synergies between different DWI parameters. A Dense-fusion Attention Contrastive Learning Network (DACLN) is designed and used to recognize DWI images. Concretely, a multi-view contrastive learning framework is constructed to train and extract features from raw multi-parameter DWI. Besides, a Dense-fusion module is designed to integrate feature and output predicted labels. We evaluated the performance of the proposed model on a set of real clinical data and analyzed the interpretability by Grad-CAM and annotation analysis, achieving average scores of 0.8825, 0.8702, 0.8933, 0.8727, and 0.8779 for accuracy, precision, recall, specificity and F-1 score. Of note, the experimental results revealed that IVIM-f, CTRW-β, and MONO-ADC exhibited significant recognition ability and complementarity. Our method achieves competitive accuracy in liver fibrosis diagnosis using the limited multi-parameter DWI dataset and finds three types of DWI parameters with high sensitivity for diagnosing liver fibrosis, which suggests potential directions for future research.

Non-invasive Serological Markers of Hepatic Fibrosis – Mini Review

Aim: This study examines the pathological outcomes of chronic liver injuries, with a focus on liver fibrosis. It emphasizes understanding the structural changes within the liver that may lead to cirrhosis and functional impairments, crucial for developing targeted antifibrotic therapies. Methods: Our approach reviews existing literature detailing the use of traditional diagnostic methods—biochemical and serological tests alongside liver biopsies. Additionally, we evaluate the reliability and efficacy of non-invasive techniques such as serological test panels and imaging examinations. These methods are compared to understand their viability as supplementary or alternative diagnostic tools to liver biopsy. Significance: Liver fibrosis, if unmanaged, can progress to severe conditions such as cirrhosis and hepatocellular carcinoma, making it vital to understand its progression and treatment options. This study underscores the need for precise and non-invasive diagnostic tools in the clinical management of liver fibrosis, providing insight into the progression of chronic liver diseases and potential therapeutic targets. Conclusion and future perspectives: The research confirms that while liver biopsy remains the definitive method for staging liver fibrosis, its risks and limitations necessitate the use of enhanced non-invasive diagnostic techniques. These methods have shown promising results in accuracy and are critical for broadening clinical applications and patient safety. It is recommended that the scientific community continue to develop and validate non-invasive diagnostic tools. Enhancing the accuracy and reliability of these tools can provide a cost-effective, accessible, and safer alternative for large-scale screening and management of liver fibrosis in asymptomatic populations. Additionally, integrating advancements in radiologic and serological markers can further refine these diagnostic methods, improving overall patient outcomes.

Staging of Liver Fibrosis Based on Energy Valley Optimization Multiple Stacking (EVO-MS) Model.

Currently, staging the degree of liver fibrosis predominantly relies on liver biopsy, a method fraught with potential risks, such as bleeding and infection. With the rapid development of medical imaging devices, quantification of liver fibrosis through image processing technology has become feasible. Stacking technology is one of the effective ensemble techniques for potential usage, but precise tuning to find the optimal configuration manually is challenging. Therefore, this paper proposes a novel EVO-MS model-a multiple stacking ensemble learning model optimized by the energy valley optimization (EVO) algorithm to select most informatic features for fibrosis quantification. Liver contours are profiled from 415 biopsied proven CT cases, from which 10 shape features are calculated and inputted into a Support Vector Machine (SVM) classifier to generate the accurate predictions, then the EVO algorithm is applied to find the optimal parameter combination to fuse six base models: K-Nearest Neighbors (KNNs), Decision Tree (DT), Naive Bayes (NB), Extreme Gradient Boosting (XGB), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF), to create a well-performing ensemble model. Experimental results indicate that selecting 3-5 feature parameters yields satisfactory results in classification, with features such as the contour roundness non-uniformity (Rmax), maximum peak height of contour (Rp), and maximum valley depth of contour (Rm) significantly influencing classification accuracy. The improved EVO algorithm, combined with a multiple stacking model, achieves an accuracy of 0.864, a precision of 0.813, a sensitivity of 0.912, a specificity of 0.824, and an F1-score of 0.860, which demonstrates the effectiveness of our EVO-MS model in staging the degree of liver fibrosis.

Inflammation activity affects liver stiffness measurement by magnetic resonance elastography in MASLD

BackgroundMagnetic resonance elastography (MRE) is recognized as the most precise imaging technology for assessing liver fibrosis in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to investigate the clinical factors and pathological characteristics that may impact LSM in MASLD patients. MethodsThis cross-sectional study recruited 124 patients who concurrently performed MRE, MRI-PDFF, and biopsy-proven MASLD. Linear regression models, Spearman's correlation, and subgroup analysis were employed to identify the variables affecting LSM. ResultsThe AUROC (95 % CI) of MRE for diagnosing fibrosis stage ≥ 1, 2, 3, and 4 was 0.80 (0.70–0.90), 0.76 (0.66–0.85), 0.92 (0.86–0.99), and 0.99 (0.99–1.00), with corresponding cutoffs of 2.56, 2.88, 3.35, and 4.76 kPa, respectively. Multivariate analyses revealed that AST was the only independent clinical variable significantly correlated with LSM. Furthermore, LSM exhibited a notable association with the grade of lobular inflammation and hepatocellular ballooning. Subgroup analysis showed that when AST ≥ 2 ULN or inflammation grade ≥ 2, LSM of patients with early fibrosis stages showed a slight but significant increase. ConclusionMRE demonstrates significant diagnostic accuracy in predicting liver fibrosis stages for MASLD patients, especially for advanced liver fibrosis and cirrhosis. However, elevated AST and the severity of liver inflammation may impact its accuracy in staging early liver fibrosis.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients. We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

Precision and Test-Retest Repeatability of Stiffness Measurement with MR Elastography: A Multicenter Phantom Study.

Background MR elastography (MRE) has been shown to have excellent performance for noninvasive liver fibrosis staging. However, there is limited knowledge regarding the precision and test-retest repeatability of stiffness measurement with MRE in the multicenter setting. Purpose To determine the precision and test-retest repeatability of stiffness measurement with MRE across multiple centers using the same phantoms. Materials and Methods In this study, three cylindrical phantoms made of polyvinyl chloride gel mimicking different degrees of liver stiffness in humans (phantoms 1-3: soft, medium, and hard stiffness, respectively) were evaluated. Between January 2021 and January 2022, phantoms were circulated between five different centers and scanned with 10 MRE-equipped clinical 1.5-T and 3-T systems from three major vendors, using two-dimensional (2D) gradient-recalled echo (GRE) imaging and/or 2D spin-echo (SE) echo-planar imaging (EPI). Similar MRE acquisition parameters, hardware, and reconstruction algorithms were used at each center. Mean stiffness was measured by a single observer for each phantom and acquisition on a single section. Stiffness measurement precision and same-session test-retest repeatability were assessed using the coefficient of variation (CV) and the repeatability coefficient (RC), respectively. Results The mean precision represented by the CV was 5.8% (95% CI: 3.8, 7.7) for all phantoms and both sequences combined. For all phantoms, 2D GRE achieved a CV of 4.5% (95% CI: 3.3, 5.7) whereas 2D SE EPI achieved a CV of 7.8% (95% CI: 3.1, 12.6). The mean RC of stiffness measurement was 5.8% (95% CI: 3.7, 7.8) for all phantoms and both sequences combined, 4.9% (95% CI: 2.7, 7.0) for 2D GRE, and 7.0% (95% CI: 2.9, 11.2) for 2D SE EPI (all phantoms). Conclusion MRE had excellent in vitro precision and same-session test-retest repeatability in the multicenter setting when similar imaging protocols, hardware, and reconstruction algorithms were used. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tang in this issue.

Identification of Early Stage Liver Fibrosis by Modifications in the Interstitial Space Diffusive Microenvironment Using Fluorescent Single-Walled Carbon Nanotubes.

During liver fibrosis, recurrent hepatic injuries lead to the accumulation of collagen and other extracellular matrix components in the interstitial space, ultimately disrupting liver functions. Early stages of liver fibrosis may be reversible, but opportunities for diagnosis at these stages are currently limited. Here, we show that the alterations of the interstitial space associated with fibrosis can be probed by tracking individual fluorescent single-walled carbon nanotubes (SWCNTs) diffusing in that space. In a mouse model of early liver fibrosis, we find that nanotubes generally explore elongated areas, whose lengths decrease as the disease progresses, even in regions where histopathological examination does not reveal fibrosis yet. Furthermore, this decrease in nanotube mobility is a purely geometrical effect as the instantaneous nanotube diffusivity stays unmodified. This work establishes the promise of SWCNTs both for diagnosing liver fibrosis at an early stage and for more in-depth studies of the biophysical effects of the disease.

COVID-19 severity prediction in patients with chronic hepatitis C depending on the liver fibrosis stage

The aim of the study was to develop a method for assessing the risk of severity of the novel coronavirus infection COVID-19 in patients with chronic hepatitis C depending on the severity of liver fibrosis.Object and methods. The results of laboratory data of patients with COVID-19 and chronic hepatitis C (n = 147) were analyzed. According to the severity of liver fibrosis, patients with COVID-19 with chronic hepati­tis C were divided into 3 subgroups: mild fibrosis (F1), moderate fibrosis (F2), severe fibrosis (F3). In blood serum, the parameters of total and biochemical (C-reactive protein, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transpeptidase, gamma-globulins, ferritin) blood tests, lipid profile (high-density lipoprotein cholesterol, triglycerides, apolipoprotein A1, apolipoprotein B), hemostasis (fibrinogen, activated partial thromboplastin time), cytokines (interleukin-2, tumor necrosis factor-а). All parameters were assessed in the acute period, depending on the severity of COVID-19 and the stage of liver fibrosis.Outcomes. Computer program for predicting the severi­ty of COVID-19 in patients with chronic hepatitis C, depending on the stage of liver fibrosis, can be introduced into clinical practice for managing patients in the certain group.

Classifying Liver Fibrosis through the Utilization of Transfer Learning and FCNET Classifiers

Abdominal Radiology is used for monitoring patients with liver diseases and to measure severity of liver fibrosis by using Ultrasound (US) images which is obtained by scanning. Scanning uses high frequency sound waves to take images of the affected human organs. Since liver is situated deep into the body the signal get weaker each time it passes through other organs which makes it difficult for diagnosis to overcome this difficult transfer learning technique is proposed in this system. The first step is to remove unwanted disturbances from the US images which is accomplished by pre-processing steps. To ensure the quality of the input images they undergo resizing of resolution, adjusting variations in brightness and contrast. The goal of pre-processing technique is to enhance the quality, uniformity, and relevance of the input images, making them suitable for feature extraction and subsequent classification of liver fibrosis stages. The next step is to apply the Expectation Maximum (EM) algorithm based on Region of Interest (ROI), which is a clustering technique used for image segmentation. The EM algorithm iteratively assigns pixels to different clusters based on their intensities and probabilistic models, aiming to identify distinct regions. The feature extraction process is carried out by Fully Connected Convolutional Neural Network (FCNET) models which enable extracting high level representations from original images and transfer learning classification is used for classification of fibrosis stages with improved accuracy.

Combining T1rho and advanced diffusion MRI for noninvasively staging liver fibrosis: an experimental study in rats

PurposeTo investigate the value of imaging parameters derived from T1 relaxation times in the rotating frame (T1ρ or T1rho), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in assessment of liver fibrosis in rats and propose an optimal diagnostic model based on multiparametric MRI.MethodsThirty rats were divided into one control group and four fibrosis experimental groups (n = 6 for each group). Liver fibrosis was induced by administering thioacetamide (TAA) for 2, 4, 6, and 8 weeks. T1ρ, mean kurtosis (MK), mean diffusivity (MD), perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were measured and compared among different fibrosis stages. An optimal diagnostic model was established and the diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.ResultsThe mean AUC values, sensitivity, and specificity of T1ρ and MD derived from DKI across all liver fibrosis stages were comparable but much higher than those of other imaging parameters (0.954, 92.46, 91.85 for T1ρ; 0.949, 92.52, 91.24 for MD). The model combining T1ρ and MD exhibited better diagnostic performance with higher AUC values than any individual method for staging liver fibrosis (≥ F1: 1.000 (0.884–1.000); ≥ F2: 0.935 (0.782–0.992); ≥ F3: 0.982 (0.852–1.000); F4: 0.986 (0.859–1.000)).ConclusionAmong the evaluated imaging parameters, T1ρ and MD were superior for differentiating varying liver fibrosis stages. The model combining T1ρ and MD was promising to be a credible diagnostic biomarker to detect and accurately stage liver fibrosis.Graphical abstract

Role of radiomics in staging liver fibrosis: a meta-analysis

BackgroundFibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis.MethodAfter literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology.ResultsFifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation.ConclusionAlthough radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.

Indicators of cellular immunity and lymphocyte metabolism in children with type 1 autoimmune hepatitis at different stages of liver fibrosis

Autoimmune hepatitis (AIH) is a progressive hepatocellular inflammation of unknown etiology, accompanied by the process of liver fibrosis. Damage in the liver is associated with the formation of highly reactive molecules that contain unpaired electrons (free radicals), which in turn induce lipid peroxidation, leading to the development of mitochondrial dysfunction. Mitochondrial DAMPs released from damaged hepatocyte mitochondria (with mtDNA as the main active component) directly activate hepatic stellate cells and cause liver scarring. Assessing cellular metabolism can be done by examining the activity of succinate dehydrogenase (SDH), a key enzyme involved in both the tricarboxylic acid cycle and OXPHOS. Aim. to examine indicators of cellular immunity and succinate dehydrogenase activity in lymphocyte populations at different stages of liver fibrosis in children with type I AIH.. Materials and methods. During hospitalization, 69 children with AIH type I, aged 12 to 18 years, Me=15.5 [12.8;16.9], were examined. 62 healthy children made up the comparison group. The stage of liver fibrosis was assessed using the FibroScan F502. The subpopulation composition of lymphocytes and the activity of succinate dehydrogenase in them were assessed using flow cytometry. The following lymphocyte populations were evaluated: T-lymphocytes, activated T-cells, B-lymphocytes, NK cells, CD16/56+CD3+ lymphocytes, cytotoxic T-lymphocytes, T-helpers, including small populations: activated T-helpers, regulatory T-cells, Th17 lymphocytes. Statistica 10.0 (USA) program was used for statistical data processing. Results. The study included 47 girls and 22 boys. Children with AIH were on standard therapy with glucocorticosteroids for 2.6 [1.1;5.4] years. In 53 children, AIH occurred in combination with gastroduodenitis (76.8% of observations), in 12 children – with gastroesophageal reflux disease (17% of observations). 7 children had comorbid autoimmune diseases or a combination of them: 5 patients had autoimmune thyroiditis (7% of cases), 2 children had vitiligo (3% of cases), as well as type 1 diabetes mellitus, juvenile rheumatoid arthritis and celiac disease in one case (1.5%), 3 children (5% of cases) had multiple autoimmune syndrome. In children with AIH, an increase in the relative content of T cells and T helper cells was detected. The relative number of NK cells and B cells was reduced in all stages of AF relative to the comparison group. A significant increase in the absolute number of Thact and Tregs was obtained at all stages of fibrosis relative to the comparison group, while the relative number of Thact (% CD4+) was significantly higher only at stages F0, F3 and F4, and the relative number of Tregs (% CD4+) was significantly higher only at stages F0 and F4. The proportion of Th17 lymphocytes (% CD4+) was significantly reduced relative to the comparison group at stages F0 and F2. From the F0 to F3 stage, the relative number of B cells decreased, and then significantly increased by the F4 stage. The proportion of T helper cells, Tregs and Thact in most patients was higher than the comparison group, however, no significant differences were found between different stages. The proportion of Th17 lymphocytes increased with increasing stage of liver fibrosis, the maximum values were detected at stage F4. A study of SDH revealed a significant decrease in the activity of this enzyme in all populations of lymphocytes. The greatest decrease in activity was detected in double negative CD3+CD4-CD8- cells by 18.5% and NK cells by 17.5% relative to the comparison group. In the populations of Th17 and Treg lymphocytes, SDH activity was significantly lower in the state of exacerbation of the disease, relative to in the state of remission. Analysis of SDH activity indicators depending on the stage of liver fibrosis revealed a nonlinear relationship: in the populations of T-helpers, T-cytotoxic and Thact – an increase to stage F2-3, and then a decrease to stage F4. SDH activity in the population of Th17 lymphocytes increased maximally at the F1 stage and subsequently decreased to the F4 stage. With increasing stage of liver fibrosis, SDH activity significantly increased in the Treg population, but did not reach normative values. Children with autoimmune hepatitis need clinical observation and strict monitoring of the development of the disease. Conclusion. The subpopulation composition of peripheral blood lymphocytes and the activity of succinate dehydrogenase in lymphocyte populations objectively reflect the severity of the patient’s condition at the time of examination and can be used for additional laboratory assessment of the progression of liver fibrosis. The identified signs of mitochondrial dysfunction justify the use of correction of metabolic disorders as a therapy that improves the basic treatment of patients with type 1 AIH.