• Network meta-analysis
• Nonalcoholic fatty liver disease
• Shear wave elastography
• Transient elastography

• Elasticity Imaging Techniques/methods
• Humans
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Sensitivity and Specificity
• Network Meta-Analysis as Topic
• Liver Cirrhosis/diagnostic imaging
• Adult
• Liver/diagnostic imaging
• Reproducibility of Results

• Humans
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Non-alcoholic Fatty Liver Disease/drug therapy
• Magnetic Resonance Imaging/methods
• Middle Aged
• Male
• Female
• Liver/diagnostic imaging
• Liver/metabolism
• Adult
• Models, Biological
• Drug Therapy, Combination
• Aged
• Pyridines

• This research has been principally supported by grants from Genome Canada, Canada Foundation for Innovation, the University of Alberta Hospital Foundation, the Alberta Ministry of Advanced Education and Technology, the Mendez National Institute of Transplantation Foundation, and Industrial Research Assistance Program. Partial support was also provided by funding from a licensing agreement with the One Lambda division of Thermo Fisher. Dr. Halloran held a Canada Research Chair in Transplant Immunology until 2008 and currently holds the Muttart Chair in Clinical Immunology.

• liver biopsy
• liver diseases
• metabolic dysfunction-associated steatotic liver disease
• non-alcoholic associated liver disease

• Fondo Nacional de Desarrollo Científico y Tecnológico

• Humans
• Non-alcoholic Fatty Liver Disease/drug therapy
• Non-alcoholic Fatty Liver Disease/pathology
• Glucagon-Like Peptides/therapeutic use
• Glucagon-Like Peptides/administration & dosage
• Female
• Male
• Middle Aged
• Biopsy
• Liver/pathology
• Liver/drug effects
• Adult
• Artificial Intelligence
• Machine Learning
• Liver Cirrhosis/pathology
• Liver Cirrhosis/drug therapy

• biopsy
• genomics
• kidney
• machine learning
• molecular diagnostics
• transplant

• Humans
• Graft Rejection/diagnosis
• Organ Transplantation
• Oligonucleotide Array Sequence Analysis
• Gene Expression Profiling/methods
• Precision Medicine/methods
• Machine Learning
• Reproducibility of Results

• This research has been principally supported by grants from Genome Canada, Canada Foundation for Innovation, the University of Alberta Hospital Foundation, the Alberta Ministry of Advanced Education
• the Mendez National Institute of Transplantation Foundation, and Industrial Research Assistance Program. Partial support was also provided by funding from a licensing agreement with the One Lambda
• division of Thermo Fisher. Dr. Halloran held a Canada Research Chair in Transplant Immunology until 2008 and currently holds the Muttart Chair in Clinical Immunology.

• Humans
• Male
• Female
• Middle Aged
• Liver Cirrhosis/pathology
• Liver/pathology
• Adult
• Biopsy
• Non-alcoholic Fatty Liver Disease/pathology
• Non-alcoholic Fatty Liver Disease/complications
• Aged
• Metabolic Diseases/pathology
• Metabolic Diseases/complications
• Fatty Liver/pathology
• Cardiovascular Diseases/etiology

• Humans
• Multiparametric Magnetic Resonance Imaging/methods
• Liver/diagnostic imaging
• Liver/pathology
• Liver/metabolism
• Fatty Liver/diagnostic imaging
• Severity of Illness Index
• Biopsy
• Magnetic Resonance Imaging/methods
• Liver Cirrhosis/diagnostic imaging

• Humans
• Elasticity Imaging Techniques/methods
• Liver/diagnostic imaging
• Liver/pathology
• Liver Cirrhosis/pathology
• Magnetic Resonance Imaging/methods
• Non-alcoholic Fatty Liver Disease/complications
• Protons
• Prospective Studies

• R01 AA021171 NIAAA NIH HHS
• R37 EB001981 NIBIB NIH HHS
• R01 EB017197 NIBIB NIH HHS
• R37 AA021171 NIAAA NIH HHS
• P30 DK084567 NIDDK NIH HHS
• K23 DK115594 NIDDK NIH HHS

• Biochimie
• Diabetes
• Diabète
• Métabolomique
• NASH
• Pathophysiology
• Physiopathologie

• liver biopsy
• new drug development
• nonalcoholic steatohepatitis
• value and challenges

• Biopsy
• Carcinoma, Hepatocellular/drug therapy
• Carcinoma, Hepatocellular/pathology
• Drug Development
• Humans
• Liver/pathology
• Liver Cirrhosis/pathology
• Liver Neoplasms/pathology
• Non-alcoholic Fatty Liver Disease/drug therapy
• Non-alcoholic Fatty Liver Disease/pathology
• Reproducibility of Results

• Diagnosis
• Liver cirrhosis
• Nomogram
• Non-invasive

• Biomarkers
• Hepatitis B virus
• Hepatitis B, Chronic/complications
• Hepatitis B, Chronic/diagnosis
• Humans
• Liver/pathology
• Liver Cirrhosis/diagnosis
• Liver Cirrhosis/pathology
• ROC Curve
• Retrospective Studies

The reference diagnostic test of fibrosis, steatosis, and hepatic iron overload is liver biopsy, a clear invasive procedure. The main objective of this work was to propose HSA, or human serum albumin, as a biomarker for the assessment of fibrosis and to study non-invasive biomarkers for the assessment of steatosis and hepatic iron overload by means of an MR image acquisition protocol. It was performed on a set of eight subjects to determine fibrosis, steatosis, and hepatic iron overload with four different MRI sequences. We calibrated longitudinal relaxation times (T1 [ms]) with seven human serum albumin (HSA [%]) phantoms, and we studied the relationship between them as this protein is synthesized by the liver, and its concentration decreases in advanced fibrosis. Steatosis was calculated by means of the fat fraction (FF [%]) between fat and water liver signals in “fat-only images” (the subtraction of in-phase [IP] images and out-of-phase [OOP] images) and in “water-only images” (the addition of IP and OOP images). Liver iron concentration (LIC [µmol/g]) was obtained by the transverse relaxation time (T2* [ms]) using Gandon’s method with multiple echo times (TE) in T2-weighted IP and OOP images. The preliminary results showed that there is an inverse relationship (r = −0.9662) between the T1 relaxation times (ms) and HSA concentrations (%). Steatosis was determined with FF > 6.4% and when the liver signal was greater than the paravertebral muscles signal, and thus, the liver appeared hyperintense in fat-only images. Hepatic iron overload was detected with LIC > 36 µmol/g, and in these cases, the liver signal was smaller than the paravertebral muscles signal, and thus, the liver behaved as hypointense in IP images.

• biomarker
• fibrosis
• hepatic iron overload
• image analysis
• magnetic resonance protocol
• phantom
• steatosis

• medical imaging
• imaging

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Azo Compounds
• Biopsy
• Child
• Clinical Decision-Making
• Coloring Agents
• Decision Support Techniques
• Eosine Yellowish-(YS)
• Female
• Hematoxylin
• Humans
• Image Interpretation, Computer-Assisted
• Liver/pathology
• Liver Cirrhosis/diagnosis
• Liver Cirrhosis/pathology
• Male
• Methyl Green
• Microscopy
• Middle Aged
• Neural Networks, Computer
• Non-alcoholic Fatty Liver Disease/diagnosis
• Non-alcoholic Fatty Liver Disease/pathology
• Predictive Value of Tests
• Reproducibility of Results
• Retrospective Studies
• Severity of Illness Index
• Software
• Staining and Labeling
• Young Adult

• R01 CA216265 NCI NIH HHS
• R01 DE022772 NIDCR NIH HHS
• P20 GM104416 NIGMS NIH HHS
• U.S. Department of Health & Human Services | National Institutes of Health (NIH)

Non-invasive assessment of non-alcoholic steatohepatitis (NASH) is increasing in desirability due to the invasive nature and costs associated with the current form of assessment; liver biopsy. Quantitative multiparametric magnetic resonance imaging (mpMRI) to measure liver fat (proton density fat fraction) and fibroinflammatory disease [iron-corrected T1 (cT1)], as well as elastography techniques [vibration-controlled transient elastography (VCTE) liver stiffness measure], magnetic resonance elastography (MRE) and 2D Shear-Wave elastography (SWE) to measure stiffness and fat (controlled attenuated parameter, CAP) are emerging alternatives which could be utilised as safe surrogates to liver biopsy.

To evaluate the agreement of non-invasive imaging modalities with liver biopsy, and their subsequent diagnostic accuracy for identifying NASH patients.

From January 2019 to February 2020, Japanese patients suspected of NASH were recruited onto a prospective, observational study and were screened using non-invasive imaging techniques; mpMRI with LiverMultiScan^®, VCTE, MRE and 2D-SWE. Patients were subsequently biopsied, and samples were scored by three independent pathologists. The diagnostic performances of the non-invasive imaging modalities were assessed using area under receiver operating characteristic curve (AUC) with the median of the histology scores as the gold standard diagnoses. Concordance between all three independent pathologists was further explored using Krippendorff’s alpha (a) from weighted kappa statistics.

N = 145 patients with mean age of 60 (SD: 13 years.), 39% females, and 40% with body mass index ≥ 30 kg/m² were included in the analysis. For identifying patients with NASH, MR liver fat and cT1 were the strongest performing individual measures (AUC: 0.80 and 0.75 respectively), and the mpMRI metrics combined (cT1 and MR liver fat) were the overall best non-invasive test (AUC: 0.83). For identifying fibrosis ≥ 1, MRE performed best (AUC: 0.97), compared to VCTE-liver stiffness measure (AUC: 0.94) and 2D-SWE (AUC: 0.94). For assessment of steatosis ≥ 1, MR liver fat was the best performing non-invasive test (AUC: 0.92), compared to controlled attenuated parameter (AUC: 0.75). Assessment of the agreement between pathologists showed that concordance was best for steatosis (a = 0.58), moderate for ballooning (a = 0.40) and fibrosis (a = 0.40), and worst for lobular inflammation (a = 0.11).

Quantitative mpMRI is an effective alternative to liver biopsy for diagnosing NASH and non-alcoholic fatty liver, and thus may offer clinical utility in patient management.

• Corrected T1
• Fibro-inflammation
• Non-invasive imaging
• Non-alcoholic steatohepatitis
• Multiparametric magnetic resonance imaging
• Non-alcoholic fatty liver disease

• Biopsy
• Elasticity Imaging Techniques
• Female
• Humans
• Japan
• Liver/diagnostic imaging
• Liver/pathology
• Liver Cirrhosis/pathology
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Multiparametric Magnetic Resonance Imaging
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Non-alcoholic Fatty Liver Disease/pathology
• Prospective Studies

• Decellularization
• Extracellular matrix
• Liver engineering
• Organ engineering

• Artificial intelligence
• Canonical discriminant analysis
• Diffuse reflectance spectroscopy
• Endogenous fluorescence spectroscopy
• Quadratic Discriminant analysis
• Steatosis

• Algorithms
• Artificial Intelligence
• Discriminant Analysis
• Humans
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Spectrum Analysis

• Diabetes Mellitus
• Histology
• Insulin Resistance
• Non-alcoholic fatty liver disease
• Type 2
• Validation studies

Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies.

We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model.

The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set).

Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation.

Mid-America Transplant Society

• Biopsy
• Convolutional neural network
• Deep learning
• Digital pathology
• Image analysis
• Liver transplantation
• Steatosis

• basic (laboratory) research/science
• biopsy
• liver transplantation/hepatology
• microarray/gene array
• molecular biology: mRNA/mRNA expression
• rejection

Liver biopsy is the essential method to diagnose non-alcoholic steatohepatitis (NASH), but histological features of NASH are too subjective to achieve reproducible diagnoses in early stages of disease. We aimed to identify the key histological features of NASH and devise a scoring model for diagnosis.

Thirteen pathologists blindly assessed 12 histological factors and final histological diagnoses (‘not-NASH,’ ‘borderline,’ and ‘NASH’) of 31 liver biopsies that were diagnosed as non-alcoholic fatty liver disease (NAFLD) or NASH before and after consensus. The main histological parameters to diagnose NASH were selected based on histological diagnoses and the diagnostic accuracy and agreement of 12 scoring models were compared for final diagnosis and the NAFLD Activity Score (NAS) system.

Inter-observer agreement of final diagnosis was fair (κ = 0.25) before consensus and slightly improved after consensus (κ = 0.33). Steatosis at more than 5% was the essential parameter for diagnosis. Major diagnostic factors for diagnosis were fibrosis except 1C grade and presence of ballooned cells. Minor diagnostic factors were lobular inflammation (≥ 2 foci/ × 200 field), microgranuloma, and glycogenated nuclei. All 12 models showed higher inter-observer agreement rates than NAS and post-consensus diagnosis (κ = 0.52–0.69 vs. 0.33). Considering the reproducibility of factors and practicability of the model, summation of the scores of major (× 2) and minor factors may be used for the practical diagnosis of NASH.

A scoring system for the diagnosis of NAFLD would be helpful as guidelines for pathologists and clinicians by improving the reproducibility of histological diagnosis of NAFLD.

• Biopsy
• Consensus
• Non-alcoholic fatty liver disease
• Non-alcoholic steatohepatitis

• MRI
• NAFLD
• NASH
• imaging
• non-invasive biomarkers

• Biomarkers/analysis
• Cross-Sectional Studies
• Female
• Humans
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Non-alcoholic Fatty Liver Disease/pathology
• Protons
• Retrospective Studies

• SCD/20 Chief Scientist Office
• Department of Health

Non-alcoholic fatty liver disease (NAFLD) and the progressive form of non-alcoholic steatohepatitis (NASH) are diseases of major importance with a high unmet medical need. Efficacy studies on novel compounds to treat NAFLD/NASH using disease models are frequently evaluated using established histological feature scores on ballooning, inflammation, steatosis and fibrosis. These features are assessed by a trained pathologist using microscopy and assigned discrete scores. We demonstrate how to automate these scores with convolutional neural networks (CNNs). Whole slide images of stained liver sections are analyzed using two different scales with four CNNs, each specialized for one of four histopathological features. A continuous value is obtained to quantify the extent of each feature, which can be used directly to provide a high resolution readout. In addition, the continuous values can be mapped to obtain the established discrete pathologist-like scores. The automated deep learning-based scores show good agreement with the trainer - a human pathologist.

• LIC
• Liver iron
• Magnetic resonance imaging
• Pediatric
• R2

• Adolescent
• Child
• Child, Preschool
• Female
• Humans
• Image Interpretation, Computer-Assisted/methods
• Infant
• Iron Overload/diagnostic imaging
• Liver/diagnostic imaging
• Liver Diseases/diagnostic imaging
• Magnetic Resonance Imaging/methods
• Male
• Reproducibility of Results

Non-alcoholic fatty liver disease is a highly prevalent condition worldwide that increases the risk to develop liver fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, it is imperative to develop novel diagnostic tools that together with liver biopsy help to differentiate mild and advanced degrees of steatosis. Ex-vivo liver samples were collected from mice fed a methionine-choline deficient diet for two or eight weeks, and from a control group. The degree of hepatic steatosis was histologically evaluated, and fat content was assessed by Oil-Red O staining. On the other hand, fluorescence spectroscopy was used for the assessment of the steatosis progression. Fluorescence spectra were recorded at excitation wavelengths of 330, 365, 385, 405, and 415 nm by establishing surface contact of the fiber optic probe with the liver specimens. A multi-variate statistical approach based on principal component analysis followed by quadratic discriminant analysis was applied to spectral data to obtain classifiers able to distinguish mild and moderate stages of steatosis at the different excitation wavelengths. Receiver Operating Characteristic (ROC) curves were computed to compare classifier’s performances for each one of the five excitation wavelengths and steatosis stages. Optimal sensitivity and specificity were calculated from the corresponding ROC curves using the Youden index. Intensity in the endogenous fluorescence spectra at the given wavelengths progressively increased according to the time of exposure to diet. The area under the curve of the spectra was able to discriminate control liver samples from those with steatosis and differentiate among the time of exposure to the diet for most of the used excitation wavelengths. High specificities and sensitivities were obtained for every case; however, fluorescence spectra obtained by exciting with 405 nm yielded the best results distinguishing between the mentioned classes with a total classification error of 1.5% and optimal sensitivities and specificities better than 98.6% and 99.3%, respectively.

• endogenous fluorescence spectroscopy
• liver steatosis
• multi-variate analysis

• Adipose Tissue/chemistry
• Adipose Tissue/diagnostic imaging
• Adipose Tissue/pathology
• Animals
• Area Under Curve
• Choline Deficiency/diagnostic imaging
• Choline Deficiency/metabolism
• Choline Deficiency/pathology
• Discriminant Analysis
• Disease Models, Animal
• Disease Progression
• Humans
• Liver/chemistry
• Liver/diagnostic imaging
• Liver/pathology
• Male
• Methionine/deficiency
• Mice
• Mice, Inbred C57BL
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Non-alcoholic Fatty Liver Disease/metabolism
• Non-alcoholic Fatty Liver Disease/pathology
• Principal Component Analysis
• ROC Curve
• Sensitivity and Specificity
• Severity of Illness Index
• Spectrometry, Fluorescence/methods
• Spectrometry, Fluorescence/standards

Liver biopsy is the main method for grading and staging liver disorders, but the effects of clinical information and optimal biopsy specimen size on interpretation remain contentious. The aim of the study was to evaluate the impact of clinical information and quality of liver specimen on inter-observer agreement for liver disease.

A total of 289 consecutive biopsy specimens from 2010 to 2017 were re-evaluated by five pathologists using the modified Ishak and non-alcoholic fatty liver diseases (NAFLD) activity score (NAS) systems. Detailed clinical information was extracted from medical records of patients and the size of all liver biopsy samples was recorded.

Full agreement between primary diagnosis and final diagnosis was obtained in 214 cases (74%). The remaining cases, namely 22 (7.6%) and 53 (18.3%) biopsies had minor and major diagnostic discrepancies, respectively. The results showed that the overall agreement was significantly higher in cases with complete clinical information than patients without any clinical information and even with partial clinical information (P<0.001). Interestingly, no significant difference in inter-observer agreement was achieved with a length over 20 mm (P=0.181). However, the inter-observer variation significantly decreased when the number of portal tract was more than 10 (P=0.001).

This study identified the impact of clinical information and the number of portal tracts as the key factors to diagnosis. Therefore, request forms for liver biopsies should always be accompanied with the clinical history. Moreover, adequacy of biopsy specimens is very useful for accurate evaluation of samples by pathologists.

• Grading
• Inter-observer
• Liver biopsy
• Pathology
• Staging

• liver diseases
• hepatology

• Age Factors
• Aged
• Antigens, Neoplasm/blood
• Antigens, Neoplasm/metabolism
• Biomarkers/blood
• Biomarkers/metabolism
• Biopsy
• Elasticity Imaging Techniques
• Female
• Humans
• Liver Cirrhosis/diagnostic imaging
• Liver Cirrhosis/metabolism
• Liver Cirrhosis/pathology
• Male
• Membrane Glycoproteins/blood
• Membrane Glycoproteins/metabolism
• Middle Aged
• Non-alcoholic Fatty Liver Disease/metabolism
• Non-alcoholic Fatty Liver Disease/pathology
• Plant Lectins/metabolism
• ROC Curve
• Receptors, N-Acetylglucosamine/metabolism

• Japan Agency for Medical Research and Development (AMED)

• Elastography
• Magnetic resonance imaging
• Nonalcoholic fatty liver disease
• Nonalcoholic steatohepatitis

• Fibrosis
• GRE MRE
• Liver
• MR elastography
• Magnetic resonance imaging
• SE-EPI MRE

• Inter-reader agreement
• Liver
• Proton density fat fraction (PDFF)
• Quantitative imaging biomarker
• Region of interest
• Reproducibility

• CT
• Fat quantification
• Liver fibrosis
• MR elastography
• MR spectroscopy
• MRI
• Nonalcoholic fatty liver disease
• Ultrasound

• Elasticity Imaging Techniques
• Humans
• Liver/diagnostic imaging
• Liver Cirrhosis/diagnostic imaging
• Magnetic Resonance Imaging
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Tomography, X-Ray Computed
• Ultrasonography