Quantification of liver fat: A comprehensive review

Transcriptome Profiling of the Liver in Nellore Cattle Phenotypically Divergent for RFI in Two Genetic Groups

The identification and selection of genetically superior animals for residual feed intake (RFI) could enhance productivity and minimize environmental impacts. The aim of this study was to use RNA-seq data to identify the differentially expressed genes (DEGs), known non-coding RNAs (ncRNAs), specific biomarkers and enriched biological processes associated with RFI of the liver in Nellore cattle in two genetic groups. In genetic group 1 (G1), 24 extreme RFI animals (12 low RFI (LRFI) versus 12 high RFI (HRFI)) were selected from a population of 60 Nellore bulls. The RNA-seq of the samples from their liver tissues was performed using an Illumina HiSeq 2000. In genetic group 2 (G2), 20 samples of liver tissue of Nellore bulls divergent for RFI (LRFI, n = 10 versus HRFI, n = 10) were selected from 83 animals. The raw data of the G2 were chosen from the ENA repository. A total of 1811 DEGs were found for the G1 and 2054 for the G2 (p-value ≤ 0.05). We detected 88 common genes in both genetic groups, of which 33 were involved in the immune response and in blocking oxidative stress. In addition, seven (B2M, ADSS, SNX2, TUBA4A, ARHGAP18, MECR, and ABCF3) possible gene biomarkers were identified through a receiver operating characteristic analysis (ROC) considering an AUC > 0.70. The B2M gene was overexpressed in the LRFI group. This gene regulates the lipid metabolism protein turnover and inhibits cell death. We also found non-coding RNAs in both groups. MIR25 was up-regulated and SNORD16 was down-regulated in the LRFI for G1. For G2, up-regulated RNase_MRP and SCARNA10 were found. We highlight MIR25 as being able to act by blocking cytotoxicity and oxidative stress and RMRP as a blocker of mitochondrial damage. The biological pathways associated with RFI of the liver in Nellore cattle in the two genetic groups were for energy metabolism, protein turnover, redox homeostasis and the immune response. The common transcripts, biomarkers and metabolic pathways found in the two genetic groups make this unprecedented work even more relevant, since the results are valid for different herds raised in different ways. The results reinforce the biological importance of these known processes but also reveal new insights into the complexity of the liver tissue transcriptome of Nellore cattle.

Ovine RAP1GAP and rBAT gene polymorphisms and their association with tail fat deposition in Hu sheep

Excessive fat deposition in the tail of sheep will affect its feed efficiency, which will increase the feeding cost. The purpose of this study was to identify the single nucleotide polymorphisms (SNPs) of RAP1GAP and rBAT genes by PCR amplification and Sanger sequencing, the SNPs were genotyped by KASP genotyping assays to evaluate their association with tail fat deposition traits. The results showed that two intronic mutations of g.13561 G > A and g.1460 T > C were found in RAP1GAP and rBAT, respectively. There were three genotypes of GG, AG, AA and CC, CT and TT at these two loci, respectively. Association analysis showed that g.13561 G > A of RAP1GAP was associated with tail width, tail fat weight and relative tail fat weight (P < 0.05). The g.1460 T > C of rBAT was associated with tail width and tail fat weight (P < 0.05). Different combinations of genotypes also differed significantly with tail fat deposition traits. In the tail fat tissue, the expression levels of RAP1GAP gene was significantly higher in small-tailed sheep than in big-tailed sheep, and the expression levels of rBAT gene was significantly higher in big-tailed sheep than in small-tailed sheep. In the liver, the expression levels of RAP1GAP and rBAT gene was significantly higher at 6 months than at 0 and 3 months. In conclusion, RAP1GAP and rBAT polymorphisms can be used as a candidate molecular marker to reduce tail fat deposition in sheep.

Hepatic Positron Emission Tomography: Applications in Metabolism, Haemodynamics and Cancer

Evaluating in vivo the metabolic rates of the human liver has been a challenge due to its unique perfusion system. Positron emission tomography (PET) represents the current gold standard for assessing non-invasively tissue metabolic rates in vivo. Here, we review the existing literature on the assessment of hepatic metabolism, haemodynamics and cancer with PET. The tracer mainly used in metabolic studies has been [¹⁸F]2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG). Its application not only enables the evaluation of hepatic glucose uptake in a variety of metabolic conditions and interventions, but based on the kinetics of ¹⁸F-FDG, endogenous glucose production can also be assessed. 14(R,S)-[¹⁸F]fluoro-6-thia-Heptadecanoic acid (¹⁸F-FTHA), ¹¹C-Palmitate and ¹¹C-Acetate have also been applied for the assessment of hepatic fatty acid uptake rates (¹⁸F-FTHA and ¹¹C-Palmitate) and blood flow and oxidation (¹¹C-Acetate). Oxygen-15 labelled water (¹⁵O-H₂O) has been used for the quantification of hepatic perfusion. ¹⁸F-FDG is also the most common tracer used for hepatic cancer diagnostics, whereas ¹¹C-Acetate has also shown some promising applications in imaging liver malignancies. The modelling approaches used to analyse PET data and also the challenges in utilizing PET in the assessment of hepatic metabolism are presented.

Clinical evaluation of grayscale and linear scale hepatorenal indices for fatty liver quantification: a prospective study of a native Chinese population

BACKGROUND AND AIMS

Hepato-renal index (HRI) has been investigated extensively in various clinical studies. New linear scale HRI (LS-HRI) is proposed as an alternative to conventional grayscale HRI (GS-HRI) that suffers from lack of a widely accepted cut-off value for differentiation of fatty from normal livers. To investigate the diagnostic performance of conventional GS-HRI and new LS-HRI for a relatively large Chinese population with NAFLD using a well-established ultrasonographic fatty liver indicator (US-FLI) as the reference standard for steatosis grades.

MATERIALS AND METHODS

A total of 106 patients with various stages of NAFLD were prospectively enrolled. All ultrasound images for these patients were first acquired by a highly experienced ultrasound doctor and their US-FLI scores then obtained by the same doctor. Both GS-HRI and LS-HRI values were measured off-line by two additional ultrasound doctors. Four steatosis grades were determined from US-FLI scores for steatosis detection and staging.

RESULTS

Inter-observer agreements for both GS-HRI and LS-HRI were excellent with the respective concordance correlation coefficient (CCC) of 0.900 for GS-HRI and 0.822 for LS-HRI. A linear correlation to US-FLI for LS-HRI (R = 0.74) was substantially superior to that for GS-HRI (R = 0.46). LS-HRI had a sensitivity of 85.9% and a specificity of 96.3% to differentiate steatosis from the normal liver (AUROC: 95.5%) while GS-HRI had a sensitivity of 85.9% and a specificity of 92.6% to distinguish steatosis from the normal liver (AUROC: 94.7%).

CONCLUSIONS

Both GS-HRI and LS-HRI measurements are reproducible between two ultrasonographic clinicians and are evidently effective for steatosis detection.

Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound

Contrast-enhanced ultrasound (CEUS) is generally employed for focal liver lesions (FLLs) diagnosis. Among the FLLs, atypical hepatocellular carcinoma (HCC) is difficult to distinguish from focal nodular hyperplasia (FNH) in CEUS video. For this reason, we propose and evaluate a feature fusion method to resolve this problem. The proposed algorithm extracts a set of hand-crafted features and the deep features from the CEUS cine clip data. The hand-crafted features include the spatial-temporal feature based on a novel descriptor called Velocity-Similarity and Dissimilarity Matching Local Binary Pattern (V-SDMLBP), and the deep features from a 3-D convolution neural network (3D-CNN). Then the two types of features are fused. Finally, a classifier is employed to diagnose HCC or FNH. Several classifiers have achieved excellent performance, which demonstrates the superiority of the fused features. In addition, compared with general CNNs, the proposed fused features have better interpretability.

S2FLNet: Hepatic steatosis detection network with body shape

Fat accumulation in the liver cells can increase the risk of cardiac complications and cardiovascular disease mortality. Therefore, a way to quickly and accurately detect hepatic steatosis is critically important. However, current methods, e.g., liver biopsy, magnetic resonance imaging, and computerized tomography scan, are subject to high cost and/or medical complications. In this paper, we propose a deep neural network to estimate the degree of hepatic steatosis (low, mid, high) using only body shapes. The proposed network adopts dilated residual network blocks to extract refined features of input body shape maps by expanding the receptive field. Furthermore, to classify the degree of steatosis more accurately, we create a hybrid of the center loss and cross entropy loss to compact intra-class variations and separate inter-class differences. We performed extensive tests on the public medical dataset with various network parameters. Our experimental results show that the proposed network achieves a total accuracy of over 82% and offers an accurate and accessible assessment for hepatic steatosis.

Clusters of Ultrasound Scattering Parameters for the Classification of Steatotic and Normal Livers

The study of ultrasound tissue interactions in fatty livers has a long history with strong clinical potential for assessing steatosis. Recently we proposed alternative measures of first- and second-order statistics of echoes from soft tissues, namely, the H-scan, which is based on a matched filter approach, to quantify scattering transfer functions and the Burr distribution to model speckle patterns. Taken together, these approaches produce a multiparameter set that is directly related to the fundamentals of ultrasound propagation in tissue. To apply this approach to the problem of assessing steatotic livers, these analyses were applied to in vivo rat livers (N=21) under normal feeding conditions or after receiving a methionine- and choline-deficient diet that produces steatosis within a few weeks. Ultrasound data were acquired at baseline and again at weeks 2 and 6 before applying the H-scan and Burr analyses. Furthermore, a classification technique known as the support vector machine was then used to find clusters of the five parameters that are characteristic of the different steatotic liver conditions as confirmed by histologic processing of excised liver tissue samples. With the in vivo multiparametric ultrasound measurement approach and determination of clusters, steatotic can be discriminated from normal livers with 100% accuracy in a rat animal model.

Contemporary strategies to assess and manage liver donor steatosis: a review

PURPOSE OF REVIEW

Due to a persistent shortage of donor livers, attention has turned toward ways of utilizing marginal grafts, particularly those with steatosis, without incurring inferior outcomes. Here we review the evaluation and utilization of steatotic liver allografts, highlight recently published data, and discuss novel methods of graft rehabilitation.

RECENT FINDINGS

Although severe liver allograft (>60%) steatosis has been associated with inferior graft and recipient outcomes, mild (<30%) steatosis has not. There is ongoing debate regarding safe utilization of grafts with moderate (30-60%) steatosis. Presently, no established protocols for evaluating steatosis in donor candidates or utilizing such grafts exist. Liver biopsy is accepted as the gold standard technique, though noninvasive methods have shown promise in accurately predicting steatosis. More recently, machine perfusion has been shown to enhance ex situ liver function and reduce steatosis, emerging as a potential means of optimizing steatotic grafts prior to transplantation.

SUMMARY

Steatotic liver allografts constitute a large proportion of deceased donor organs. Further work is necessary to define safe upper limits for the acceptable degree of steatosis, develop standardized evaluation protocols, and establish utilization guidelines that prioritize safety. Machine perfusion has shown promise in rehabilitating steatotic grafts and offers the possibility of expanding the deceased donor pool.

Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides

BACKGROUND

The incidence of non-alcoholic fatty liver disease (NAFLD) and its progressive form, non-alcoholic steatohepatitis (NASH), has been increasing for decades. Since the mainstay is lifestyle modification in this mainly asymptomatic condition, there is a need for accurate diagnostic methods.

OBJECTIVES

This study proposes a method with a computer-aided diagnosis (CAD) system to predict NAFLD Activity score (NAS scores-steatosis, lobular inflammation, and ballooning) and fibrosis stage from histopathology slides.

METHODS

A total of 87 pathology slides pairs (H&E and Trichrome-stained) were used for the study. Ground-truth NAS scores and fibrosis stages were previously identified by a pathologist. Each slide was split into 224 × 224 patches and fed into a feature extraction network to generate local features. These local features were processed and aggregated to obtain a global feature to predict the slide's scores. The effects of different training strategies, as well as training data with different staining and magnifications were explored. Four-fold cross validation was performed due to the small data size. Area Under the Receiver Operating Curve (AUROC) was utilized to evaluate the prediction performance of the machine-learning algorithm.

RESULTS

Predictive accuracy for all subscores was high in comparison with pathologist assessment. There was no difference among the 3 magnifications (5x, 10x, 20x) for NAS-steatosis and fibrosis stage tasks. A larger magnification (20x) achieved better performance for NAS-lobular scores. Middle-level magnification was best for NAS-ballooning task. Trichrome slides are better for fibrosis stage prediction and NAS-ballooning score prediction task. NAS-steatosis prediction had the best performance (AUC 90.48%) in the model. A good performance was observed with fibrosis stage prediction (AUC 83.85%) as well as NAS-ballooning prediction (AUC 81.06%).

CONCLUSIONS

These results were robust. The method proposed proved to be effective in predicting NAFLD Activity score and fibrosis stage from histopathology slides. The algorithms are an aid in having an accurate and systematic diagnosis in a condition that affects hundreds of millions of patients globally.

Efficacy and Safety of Glucagon-like Peptide-1 Receptor Agonists in the Treatment of Metabolic Associated Fatty Liver Disease: A Systematic Review and Meta-analysis

BACKGROUND

Clinical trials examining the therapeutic benefits of glucagon-like peptide-1 receptor agonists (GLP-1RA) on patients with metabolic associated fatty liver disease (MAFLD) have reported inconsistent results. The aim of this meta-analysis was to verify the role of GLP-1RA in the treatment of MAFLD patients.

MATERIALS AND METHODS

We searched PubMed, Embase, Medline, and the Cochrane Library for randomized controlled trials published that compared GLP-1RA with the control treatment in patients with MAFLD till to July 30, 2020. The effects of GLP-1RA on liver histology, body mass index, waist circumference (WC), aspartate aminotransferase, total cholesterol, triglycerides (TG), low-density lipoprotein and high-density lipoprotein were evaluated.

RESULTS

Thirteen trials involving 704 patients were included in the meta-analysis. Compared with the control treatment, GLP-1RA treatment induced a greater resolution of steatohepatitis [RR=2.87; 95% confidence interval (CI): 0.89 to 9.23], delayed the progression of liver fibrosis (RR=3.83, 95% CI: 0.91 to 16.07) and reduced liver fat deposition (MD: -1.40; 95% CI: -2.75 to -0.05). In addition, it reduced the body mass index (MD: -1.15; 95% CI: -2.26 to -0.04), WC (MD: -3.33; 95% CI: -6.31 to -0.35) and improved serum aspartate aminotransferase (MD: -3.04; 95% CI: -5.93 to -0.16) and total cholesterol (MD: -0.20; 95% CI: -0.28 to -0.13).

CONCLUSION

GLP-1RA improves liver steatosis and fibrosis. It is also beneficial to metabolic syndrome as it reduces BMI, WC, and hyperlipidemia.

Magnetic Resonance Spectroscopy of Hepatic Fat from Fundamental to Clinical Applications

The number of individuals suffering from fatty liver is increasing worldwide, leading to interest in the noninvasive study of liver fat. Magnetic resonance spectroscopy (MRS) is a powerful tool that allows direct quantification of metabolites in tissue or areas of interest. MRS has been applied in both research and clinical studies to assess liver fat noninvasively in vivo. MRS has also demonstrated excellent performance in liver fat assessment with high sensitivity and specificity compared to biopsy and other imaging modalities. Because of these qualities, MRS has been generally accepted as the reference standard for the noninvasive measurement of liver steatosis. MRS is an evolving technique with high potential as a diagnostic tool in the clinical setting. This review aims to provide a brief overview of the MRS principle for liver fat assessment and its application, and to summarize the current state of MRS study in comparison to other techniques.

Dual-energy CT in diffuse liver disease: is there a role?

Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.

Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology

BACKGROUND AND OBJECTIVE

Hepatocellular ballooning is an important histological parameter in the diagnosis of nonalcoholic steatohepatitis (NASH), and it is considered to be a morphological pattern that indicates the severity and the progression to cirrhosis and liver-related deaths. There remains uncertainty about the pathological criteria for evaluating the spectrum of non-alcoholic fatty liver disease (NAFLD) in liver biopsies. We introduce persistence images as novel mathematical descriptors for the classification of ballooning degeneration in the pathological diagnosis.

METHODS

We implemented and tested a topological data analysis methodology combined with linear machine learning techniques and applied this to the classification of tissue images into NAFLD subtypes using Matteoni classification in liver biopsies.

RESULTS

Digital images of hematoxylin- and eosin-stained specimens with a pathologist's visual assessment were obtained from 79 patients who were clinically diagnosed with NAFLD. We obtained accuracy rates of more than 90% for the classification between NASH and non-NASH NAFLD groups. The highest area under the curve from the receiver operating characteristic analysis was 0.946 for the classification of NASH and NAFL2 (type 2 of Matteoni classification), when both 0- and 1-dimensional persistence images were used.

CONCLUSIONS

Our methodology using persistent homology provides quantitative measurements of the topological features in liver biopsies of NAFLD groups with considerable accuracy.

Triptolide enhances lipolysis of adipocytes by enhancing ATGL transcription via upregulation of p53

Lipolysis is an essential physiological activity of adipocytes. The Patatin Like Phospholipase Domain Containing 2 (PNPLA2) gene encodes the enzyme adipose triglyceride lipase (ATGL) responsible for triglyceride hydrolysis, the first step in lipolysis. In this study, we investigated the potential of triptolide (TP), a natural plant extract, to induce weight loss by examining its effect on ATGL expression. We found that long- and short-term TP administration reduced body weight and fat weight and increased heat production in brown adipose tissue in wild-type C57BL/6 mice. In 3T3-L1 fibroblasts and porcine adipocytes, TP treatment reduced the number of lipid droplets as determined by Oil Red O and BODIPY staining, with concomitant increases in free fatty acid and triglyceride levels in the culture medium. Combined treatment with TP and p53 inhibitor reversed these lipolytic effects. We next amplified the ATGL promoter region and identified conserved p53 binding sites in the sequence by in silico analysis. The results of the dual-luciferase reporter assay using a construct containing the ATGL promoter harboring the p53 binding site showed that p53 induces ATGL promoter activity and consequently, ATGL transcription. These results demonstrate that TP has therapeutic value as an anti-obesity agent and acts by promoting lipolysis via upregulation of p53 and ATGL transcription.

qFIBS: An Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis

BACKGROUND AND AIMS

Nonalcoholic steatohepatitis (NASH) is a common cause of chronic liver disease. Clinical trials use the NASH Clinical Research Network (CRN) system for semiquantitative histological assessment of disease severity. Interobserver variability may hamper histological assessment, and diagnostic consensus is not always achieved. We evaluate a second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) imaging-based tool to provide an automated quantitative assessment of histological features pertinent to NASH.

APPROACH AND RESULTS

Images were acquired by SHG/TPEF from 219 nonalcoholic fatty liver disease (NAFLD)/NASH liver biopsy samples from seven centers in Asia and Europe. These were used to develop and validate qFIBS, a computational algorithm that quantifies key histological features of NASH. qFIBS was developed based on in silico analysis of selected signature parameters for four cardinal histopathological features, that is, fibrosis (qFibrosis), inflammation (qInflammation), hepatocyte ballooning (qBallooning), and steatosis (qSteatosis), treating each as a continuous rather than categorical variable. Automated qFIBS analysis outputs showed strong correlation with each respective component of the NASH CRN scoring (P < 0.001; qFibrosis [r = 0.776], qInflammation [r = 0.557], qBallooning [r = 0.533], and qSteatosis [r = 0.802]) and high area under the receiver operating characteristic curve values (qFibrosis [0.870-0.951; 95% confidence interval {CI}, 0.787-1.000; P < 0.001], qInflammation [0.820-0.838; 95% CI, 0.726-0.933; P < 0.001), qBallooning [0.813-0.844; 95% CI, 0.708-0.957; P < 0.001], and qSteatosis [0.939-0.986; 95% CI, 0.867-1.000; P < 0.001]) and was able to distinguish differing grades/stages of histological disease. Performance of qFIBS was best when assessing degree of steatosis and fibrosis, but performed less well when distinguishing severe inflammation and higher ballooning grades.

CONCLUSIONS

qFIBS is an automated tool that accurately quantifies the critical components of NASH histological assessment. It offers a tool that could potentially aid reproducibility and standardization of liver biopsy assessments required for NASH therapeutic clinical trials.

Hepatic steatosis is associated with abnormal hepatic enzymes, visceral adiposity, altered myocardial glucose uptake measured by 18F-FDG PET/CT

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is a multisystem disease that affects the liver and a variety of extra-hepatic organ systems. This study aimed to investigate the relationship between hepatic steatosis and glucose metabolism in liver and extra-hepatic tissues and organs.

METHODS

The whole body ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) images of 191 asymptomatic tumor screening patients were retrospectively analyzed. Patients with the ratio of spleen/liver CT densities > 1.1 were defined to have NAFLD, and their clinical symptoms, laboratory markers, FDG uptake in a variety of tissues and organs including heart, mediastinal blood pool, liver, spleen, pancreas, and skeletal muscle, as well as abdominal adipose tissue volumes including visceral adipose tissue (VAT) volume and subcutaneous adipose tissue (SAT) volume were compared with those of the non-NAFLD patients and used to analyze the independent correlation factors of NAFLD.

RESULTS

Among the 191 patients, 33 (17.3%) were NAFLD, and 158 (82.7%) were non-NAFLD. There was no significant correlation between the mean standardized uptake value (SUVmean) and CT density of liver as well as the ratio of spleen/liver CT densities. Hepatic steatosis, but not FDG intake, was more significant in NAFLD patients with abnormal liver function than those with normal liver function. Compared with the non-NAFLD patients, NAFLD patients had significantly reduced myocardial glucose metabolism, but significantly increased mediastinal blood pool, spleen SUVmean and abdominal adipose tissue volumes (including VAT and SAT volumes) (P < 0.05). Multivariate regression analysis showed that elevated serum ALT, increased abdominal VAT volume, and decreased myocardial FDG uptake were independent correlation factors for NAFLD. Further studies showed that hepatic steatosis and myocardial FDG uptake were mildly linearly correlated (r = 0.366 with hepatic CT density and - 0.236 with the ratio of spleen/liver CT densities, P < 0.05).

CONCLUSIONS

NAFLD is a systemic disease that can lead to the change of glucose metabolism in some extra-hepatic tissues and organs, especially the myocardium.

Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.

Automated computerized image analysis for the user-independent evaluation of disease severity in preclinical models of NAFLD/NASH

Pathologists use a semiquantitative scoring system (NAS or SAF score) to facilitate the reporting of disease severity and evolution. Similar scores are applied for the same purposes in rodents. Histological scores have inherent inter- and intra-observer variability and yield discrete and not continuous values. Here we performed an automatic numerical quantification of NASH features on liver sections in common preclinical NAFLD/NASH models. High-fat diet-fed foz/foz mice (Foz HF) or wild-type mice (WT HF) known to develop progressive NASH or an uncomplicated steatosis, respectively, and C57Bl6 mice fed a choline-deficient high-fat diet (CDAA) to induce steatohepatitis were analyzed at various time points. Automated software image analysis of steatosis, inflammation, and fibrosis was performed on digital images from entire liver sections. Data obtained were compared with the NAS score, biochemical quantification, and gene expression. As histologically assessed, WT HF mice had normal liver up to week 34 when they harbor mild steatosis with if any, little inflammation. Foz HF mice exhibited grade 2 steatosis as early as week 4, grade 3 steatosis at week 12 up to week 34; inflammation and ballooning increased gradually with time. Automated measurement of steatosis (macrovesicular steatosis area) revealed a strong correlation with steatosis scores (r = 0.89), micro-CT liver density, liver lipid content (r = 0.89), and gene expression of CD36 (r = 0.87). Automatic assessment of the number of F4/80-immunolabelled crown-like structures strongly correlated with conventional inflammatory scores (r = 0.79). In Foz HF mice, collagen deposition, evident at week 20 and progressing at week 34, was automatically quantified on picrosirius red-stained entire liver sections. The automated procedure also faithfully captured and quantitated macrovesicular steatosis, mixed inflammation, and pericellular fibrosis in CDAA-induced steatohepatitis. In conclusion, the automatic numerical analysis represents a promising quantitative method to rapidly monitor NAFLD activity with high-throughput in large preclinical studies and for accurate monitoring of disease evolution.

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

Lipidomic biomarkers and mechanisms of lipotoxicity in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) represents the most common form of chronic liver disease worldwide (about 25% of the general population) and 3-5% of patients develop non-alcoholic steatohepatitis (NASH), characterized by hepatocytes damage, inflammation and fibrosis, which increase the risk of developing liver failure, cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD, particularly the mechanisms whereby a minority of patients develop a more severe phenotype, is still incompletely understood. In this review we examine the available literature on initial mechanisms of hepatocellular damage and inflammation, deriving from toxic effects of excess lipids. Accumulating data indicate that the total amount of triglycerides stored in the liver cells is not the main determinant of lipotoxicity and that specific lipid classes act as damaging agents. These lipotoxic species affect the cell behavior via multiple mechanisms, including activation of death receptors, endoplasmic reticulum stress, modification of mitochondrial function and oxidative stress. The gut microbiota, which provides signals through the intestine to the liver, is also reported to play a key role in lipotoxicity. Finally, we summarize the most recent lipidomic strategies utilized to explore the liver lipidome and its modifications in the course of NALFD. These include measures of lipid profiles in blood plasma and erythrocyte membranes that can surrogate to some extent lipid investigation in the liver.

Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this process is time-consuming, error-prone, and easily affected by the expertise of pathologists. Recently, computer-aided methods have been widely applied to medical image analysis; however, the current medical image analysis studies have not yet focused on the histopathological morphology of liver cancer due to its complex features and the insufficiency of training images with detailed annotations. This paper proposes a deep learning method for liver cancer histopathological image classification using only global labels. To compensate for the lack of detailed cancer region annotations in those images, patch features are extracted and fully utilized. Transfer learning is used to obtain the patch-level features and then combined with multiple-instance learning to acquire the image-level features for classification. The method proposed here solves the processing of large-scale images and training sample insufficiency in liver cancer histopathological images for image classification. The proposed method can distinguish and classify liver histopathological images as abnormal or normal with high accuracy, thus providing support for the early diagnosis of liver cancer.

Pterostilbene Reduces Liver Steatosis and Modifies Hepatic Fatty Acid Profile in Obese Rats

Excessive fat accumulation within the liver is known as “simple hepatic steatosis”, which is the most benign form of non-alcoholic fatty liver disease (NAFLD). The aim of the present study was to determine whether pterostilbene improves this hepatic alteration in Zucker (fa/fa) rats. Animals were distributed in two experimental groups (n = 10) and fed a standard laboratory diet. Rats in the pterostilbene group were given a dose of 30 mg/kg body weight/d for six weeks. After sacrifice, serum glucose, transaminase, and insulin concentrations were quantified and the liver triacylglycerol content and fatty acid profile was analyzed. Different pathways of triacylglycerol metabolism in liver were studied, including fatty acid synthesis and oxidation, triglyceride assembly, fatty acid uptake, and glucose uptake. With pterostilbene administration, a reduction in insulin concentrations (consequently in the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR)) and hepatic triacylglycerol content were observed. No effects were observed in pterostilbene-treated rats in the activity of de novo lipogenesis enzymes. An improvement in the fatty acid profile was observed in pterostilbene-treated rats. In conclusion, pterostilbene is a useful molecule to reduce liver steatosis. Its delipidating effect is due, at least in part, to reduced fatty acid availability and triacylglycerol synthesis, as well as to an increased very low-density lipoprotein assembly and fatty acid oxidation.

Assessment of Liver Graft Steatosis: Where Do We Stand?

The growing number of patients on waiting lists for liver transplantation and the shortage of organs have forced many centers to adopt extended criteria for graft selection, moving the limit of acceptance for marginal livers. Steatotic grafts that were, in the past, considered strictly unacceptable for transplantation because of the high risk of early nonfunction are now considered as a potential resource for organ implementation. Several methods to diagnose, measure, classify, and stage steatosis exist, but none can be considered qualitatively and quantitatively "the ideal method" to date. Clinical, biological, and imaging data can be very helpful to estimate graft steatosis, but histology still remains the gold standard. There is an increasing need for rapid and reliable tools to assess graft steatosis. Herein, we present a comprehensive review of the approaches that are currently used to quantify steatosis in liver grafts.

Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images

BACKGROUND

Clinical histological grading of hepatocellular carcinoma (HCC) differentiation is of great significance in clinical diagnoses, treatments, and prognoses. However, it is challenging for radiologists to evaluate HCC gradings from medical images.

PURPOSE

In this study, a novel deep neural network was developed by combining the squeeze-and-excitation networks (SENets) in a three-dimensional (3D) densely connected convolutional network (DenseNet), which is referred to as a 3D SE-DenseNet, for the classification of HCC grading using enhanced clinical magnetic resonance (MR) images obtained from two different clinical centers.

METHOD

In the proposed architecture, the SENet was added as an additional layer between the dense blocks of the 3D DenseNet, to mitigate the impact of feature redundancy. For the HCC grading task, the 3D SE-DenseNet was trained after data augmentation, and it outperformed the 3D DenseNet based on the clinical dataset.

RESULTS

The quantitative evaluations of the 3D SE-DenseNet on a two-class HCC grading task were conducted based on the dataset, which included 213 samples of the dynamic enhanced MR images. The proposed 3D SE-DenseNet demonstrated an accuracy of 83%, when compared with the 72% accuracy of the 3D DenseNet.

CONCLUSION

Owing to the advantage of useful automatic feature learning by the SE layer, the 3D SE-DenseNet can simultaneously handle useful feature enhancement and superfluous feature suppression. The quantitative experiments confirm the excellent performance of the 3D SE-DenseNet in the evaluation of the HCC grading.

Quantification of hepatic steatosis in histologic images by deep learning method

OBJECTIVE

To develop and test a novel method for automatic quantification of hepatic steatosis in histologic images based on the deep learning scheme designed to predict the fat ratio directly, which aims to improve accuracy in diagnosis of non-alcoholic fatty liver disease (NAFLD) with objective assessment of the severity of hepatic steatosis instead of subjective visual estimation.

MATERIALS AND METHODS

Thirty-six 8-week old New Zealand white rabbits of both sexes were fed with high-cholesterol, high-fat diet and sacrificed under deep anesthesia at various time points to obtain the pathological specimen. All rabbits were performed by multislice computed tomography for surveillance to measure density changes of liver parenchyma. A deep learning scheme using a convolutional neural network was developed to directly predict the liver fat ratio based on the pathological images. The average error value, standard deviation, and accuracy (error <5%) were evaluated and compared between the deep learning scheme and manual segmentation results. The Pearson's correlation coefficient was also calculated in this study.

RESULTS

The deep learning scheme performs successfully on rabbit liver histologic data, showing a high degree of accuracy and stability. The average error value, standard deviation, and accuracy (error <5%) were 3.21%, 4.02%, and 79.10% for the cropped images, 2.22%, 1.92%, and 88.34% for the original images, respectively. The strong positive correlation was also observed for cropped images (R = 0.9227) and original images (R = 0.9255) in comparison to labeled fat ratio.

CONCLUSIONS

This new deep learning scheme may aid in the quantification of steatosis in the liver and facilitate its treatment by providing an earlier clinical diagnosis.

Vitamin E-inspired multi-scale imaging agent

The production and use of multi-modal imaging agents is on the rise. The vast majority of these imaging agents are limited to a single length scale for the agent (e.g. tissues only), which is typically at the organ or tissue scale. This work explores the synthesis of such an imaging agent and discusses the applications of our vitamin E-inspired multi-modal and multi-length scale imaging agents TB-Toc ((S,E)-5,5-difluoro-7-(2-(5-((6-hydroxy-2,5,7,8-tetramethylchroman-2-yl) methyl) thiophen-2-yl) vinyl)-9-methyl-5H-dipyrrolo-[1,2-c:2',1'-f][1,3,2]diazaborinin-4-ium-5-uide). We investigate the toxicity of TB-Toc along with the starting materials and lipid based delivery vehicle in mouse myoblasts and fibroblasts. Further we investigate the uptake of TB-Toc delivered to cultured cells in both solvent and liposomes. TB-Toc has low toxicity, and no change in cell viability was observed up to concentrations of 10 mM. TB-Toc shows time-dependent cellular uptake that is complete in about 30 min. This work is the first step in demonstrating our vitamin E derivatives are viable multi-modal and length scale diagnostic tools.

The association between diabetes mellitus and reduction in myocardial glucose uptake: a population-based 18F-FDG PET/CT study

BACKGROUND

In diabetes, dysregulated substrate utilization and energy metabolism of myocardium can lead to heart failure. To examine the dynamic changes of myocardium, most of the previous studies conducted dynamic myocardial PET imaging following euglycemic-hyperinsulinemic clamp, which involves complicated procedures. In comparison, the whole-body 18F-FDG PET/CT scan is a simple and widely used method. Therefore, we hope to use this method to observe abnormal myocardial glucose metabolism in diabetes and determine the influencing factors.

METHODS

We retrospectively analyzed PET/CT images of 191 subjects from our medical examination center. The levels of FDG uptake in myocardium were visually divided into 4 grades (Grade 0-3, from low to high). The differences in clinical and metabolic parameters among diabetes mellitus (DM), impaired fasting glucose (IFG), and normal fasting glucose (NFG) groups were analyzed, as well as their associations with myocardial FDG uptake.

RESULTS

Compared with NFG and IFG groups, DM group had more cardiovascular-related risk factors. The degree of myocardial FDG uptake was significantly decreased in DM group; when myocardial FDG uptake ≤ Grade 1, the sensitivity of DM prediction was 84.0%, and the specificity was 58.4%. Univariate analysis showed that the myocardial FDG uptake was weakly and negatively correlated with multiple metabolic-related parameters (r = - 0.173~ - 0.365, P < 0.05). Multivariate logistic regression analysis showed that gender (male), HOMA-IR and nonalcoholic fatty liver disease (NAFLD) were independent risk factors for poor myocardial FDG uptake.

CONCLUSIONS

Diabetes is associated with decreased myocardial glucose metabolism, which is mediated by multiple metabolic abnormalities.

Simultaneous quantification of hepatic MRI-PDFF and R2* in a rabbit model with nonalcoholic fatty liver disease

Quantification of hepatic fat and iron content is important for early detection and monitoring of nonalcoholic fatty liver disease (NAFLD) patients. This study evaluated quantification efficiency of hepatic proton density fat fraction (PDFF) by MRI using NAFLD rabbits. R2* was also measured to investigate whether it correlates with fat levels in NAFLD. NAFLD rabbit model was successfully established by high fat and cholesterol diet. Rabbits underwent MRI examination for fat and iron analyses, compared with liver histological findings. MR examinations were performed on a 3.0T MR system using multi-echo 3D gradient recalled echo (GRE) sequence. MRI-PDFF showed significant differences between different steatosis grades with medians of 3.72% (normal), 5.43% (mild), 9.11% (moderate) and 11.17% (severe), whereas this was not observed in R2*. Close correlation between MRI-PDFF and histological steatosis was observed (r=0.78, P=0.000). Hepatic iron deposit was not found in any rabbits. There was no correlation between R2* and either liver MRI-PDFF or histological steatosis. MR measuring MRI-PDFF and R2* simultaneously provides promising quantification of steatosis and iron. Rabbit NAFLD model confirmed accuracy of MRI-PDFF for liver fat quantification. R2* measurement and relationship between fat and iron of NAFLD liver need further experimental investigation.

Automated quantification of steatosis: agreement with stereological point counting

BACKGROUND

Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist.

METHODS

The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability.

RESULTS

The new method showed the strongest agreement with the expert. At 20× resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10× resolution, it was more accurate than and twice as fast as all other methods at 20× resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer.

CONCLUSIONS

The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.

Deep scattering convolution network based features for ultrasonic fatty liver tissue characterization

Accumulation of excess fat in the liver tissue is the leading cause for dysfunction of liver, which can lead to the diseases from fibrosis to end stage cirrhosis. Hence, early detection of fatty liver becomes crucial in avoiding the liver from permanent failure. Depending on the concentration of fat in the tissue, the liver is classified as Normal, Grade 1, Grade 2 and Grade 3 respectively. The texture of liver tissue in ultrasound image is so specific to the concentration of fat, hence classifying the fatty liver is formulated as a texture discrimination problem. In this paper, we present an automated algorithm for grading the tissue of a fatty liver based on the features obtained from the invariant scattering convolution network (ISCN). ISCN, which involves cascade of modulus complex wavelet transforms and averaging operations results in scattering coefficients (SC), these coefficients will give stable invariant representations and also maps the texture of fatty liver image to a discriminative manifold giving good features for classification. SC are of high dimension and hence a compact representation feature is obtained by summing all the SC coefficients. Summed SC features along with cubic SVM classifier gave an accuracy of 96.6% in automatically categorizing the fatty content present in the tissue of a liver.

Imaging of liver transplantation

Liver transplantation (LT) is the treatment of choice for end-stage chronic liver disease, fulminant liver failure and early stage hepatocellular carcinoma. As discussed in this review, state-of-the-art imaging modalities including ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the selection of patients and donors, as well as in early detection of those complications at risk of impairing graft function and/or survival. We also illustrate main imaging findings related to the wide spectrum of clinical problems raised by LT.

Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique

PURPOSE

The diagnostic gold standard for nonalcoholic fatty liver disease is an invasive biopsy. Noninvasive Cartesian MRI fat quantification remains limited to a breath-hold (BH). In this work, a novel free-breathing 3D stack-of-radial (FB radial) liver fat quantification technique is developed and evaluated in a preliminary study.

METHODS

Phantoms and healthy subjects (n = 11) were imaged at 3 Tesla. The proton-density fat fraction (PDFF) determined using FB radial (with and without scan acceleration) was compared to BH single-voxel MR spectroscopy (SVS) and BH 3D Cartesian MRI using linear regression (correlation coefficient ρ and concordance coefficient ρ_c ) and Bland-Altman analysis.

RESULTS

In phantoms, PDFF showed significant correlation (ρ > 0.998, ρ_c  > 0.995) and absolute mean differences < 2.2% between FB radial and BH SVS, as well as significant correlation (ρ > 0.999, ρ_c  > 0.998) and absolute mean differences < 0.6% between FB radial and BH Cartesian. In the liver and abdomen, PDFF showed significant correlation (ρ > 0.986, ρ_c  > 0.985) and absolute mean differences < 1% between FB radial and BH SVS, as well as significant correlation (ρ > 0.996, ρ_c  > 0.995) and absolute mean differences < 0.9% between FB radial and BH Cartesian.

CONCLUSION

Accurate 3D liver fat quantification can be performed in 1 to 2 min using a novel FB radial technique. Magn Reson Med 79:370-382, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Initial Poor Function and Primary Nonfunction in Deceased-Donor Orthotopic Liver Transplantation Maintaining Short Cold Ischemic Time

BACKGROUND

Nations with emerging deceased-donor liver transplantation programs, such as India, face problems associated with poor donor maintenance. Cold ischemic time (CIT) is typically maintained short by matching donor organ recovery and recipient hepatectomy to achieve maximum favorable outcome. We analyzed different extended criteria donor factors including donor acidosis, which may act as a surrogate marker of poor donor maintenance, to quantify the risk of primary nonfunction (PNF) or initial poor function (IPF).

METHODS

A single-center retrospective outcome analysis of prospectively collected data of patients undergoing deceased-donor liver transplantation over 2 years to determine the impact of different extended criteria donor factors on IPF and PNF.

RESULTS

From March 2013 to February 2015, a total of 84 patients underwent deceased-donor liver transplantation. None developed PNF. Thirteen (15.5%) patients developed IPF. Graft macrosteatosis and donor acidosis were only related to IPF ( P = .002 and P = .032, respectively). Cold ischemic time was maintained short (81 cases ≤8 hours, maximum 11 hours) in all cases.

CONCLUSION

Poor donor maintenance as evidenced by donor acidosis and graft macrosteatosis had significant impact in developing IPF when CIT is kept short. Similar study with larger sample size is required to establish extended criteria cutoff values.