Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease

Evaluation of MRI proton density fat fraction in hepatic steatosis: a systematic review and meta-analysis

BACKGROUND

Amidst the global rise of metabolic dysfunction-associated steatotic liver disease (MASLD), driven by increasing obesity rates, there is a pressing need for precise, non-invasive diagnostic tools. Our research aims to validate MRI Proton Density Fat Fraction (MRI-PDFF) utility, compared to liver biopsy, in grading hepatic steatosis in MASLD.

METHODS

A systematic search was conducted across Embase, PubMed/Medline, Scopus, and Web of Science until January 13, 2024, selecting studies that compare MRI-PDFF with liver biopsy for hepatic steatosis grading, defined as grades 0 (< 5% steatosis), 1 (5-33% steatosis), 2 (34-66% steatosis), and 3 (> 66% steatosis).

RESULTS

Twenty-two studies with 2844 patients were included. The analysis showed high accuracy of MRI-PDFF with AUCs of 0.97 (95% CI = 0.96-0.98) for grade 0 vs ≥ 1, 0.91 (95% CI = 0.88-0.93) for ≤ 1 vs ≥ 2, and 0.91 (95% CI = 0.88-0.93) for ≤ 2 vs 3, diagnostic odds ratio (DOR) from 98.74 (95% CI = 58.61-166.33) to 23.36 (95% CI = 13.76-39.68), sensitivity and specificity from 0.93 (95% CI = 0.88-0.96) to 0.76 (95% CI = 0.63-0.85) and 0.93 (95% CI = 0.88-0.96) to 0.89 (95% CI = 0.84-0.93), respectively. Likelihood ratio (LR) + ranged from 13.3 (95% CI = 7.4-24.0) to 7.2 (95% CI = 4.9-10.5), and LR - from 0.08 (95% CI = 0.05-0.13) to 0.27 (95% CI = 0.17-0.42). The proposed MRI-PDFF threshold of 5.7% for liver fat content emerges as a potential cut-off for the discrimination between grade 0 vs ≥ 1 (p = 0.075).

CONCLUSION

MRI-PDFF is a precise non-invasive technique for diagnosing and grading hepatic steatosis, warranting further studies to establish its diagnostic thresholds.

CLINICAL RELEVANCE STATEMENT

This study underscores the high diagnostic accuracy of MRI-PDFF for distinguishing between various grades of hepatic steatosis for early detection and management of MASLD, though further research is necessary for broader application.

KEY POINTS

MRI-PDFF offers precision in diagnosing and monitoring hepatic steatosis. The diagnostic accuracy of MRI-PDFF decreases as the grade of hepatic steatosis advances. A 5.7% MRI-PDFF threshold differentiates steatotic from non-steatotic livers.

An update on applications of digital pathology: primary diagnosis; telepathology, education and research

Digital Pathology or whole slide imaging (WSI) is a diagnostic evaluation technique that produces digital images of high quality from tissue fragments. These images are formed on glass slides and evaluated by pathologist with the aid of microscope. As the concept of digital pathology is introduced, these high quality images are digitized and produced on-screen whole slide images in the form of digital files. This has paved the way for pathologists to collaborate with other pathology professionals in case of any additional recommendations and also provides remote working opportunities. The application of digital pathology in clinical practice is glazed with several advantages and adopted by pathologists and researchers for clinical, educational and research purposes. Moreover, digital pathology system integration requires an intensive effort from multiple stakeholders. All pathology departments have different needs, case usage, and blueprints, even though the framework elements and variables for effective clinical integration can be applied to any institution aiming for digital transformation. This article reviews the background and developmental phases of digital pathology and its application in clinical services, educational and research activities.

The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases

Early detection, accurate diagnosis, and effective treatment of liver diseases are of paramount importance for improving patient survival rates. However, traditional methods are frequently influenced by subjective factors and technical limitations. With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. This article offers a comprehensive review of the current applications of AI in hepatology. It elaborates on how AI is utilized to predict the progression of liver diseases, diagnose various liver conditions, and assist in formulating personalized treatment plans. The article emphasizes key advancements, including the application of machine learning and deep learning algorithms. Simultaneously, it addresses the challenges and limitations within this domain. Moreover, the article pinpoints future research directions. It underscores the necessity for large-scale datasets, robust algorithms, and ethical considerations in clinical practice, which is crucial for facilitating the effective integration of AI technology and enhancing the diagnostic and therapeutic capabilities of liver diseases.

Identification of Candidates for MASLD Treatment With Indeterminate Vibration-Controlled Transient Elastography

BACKGROUND AND AIMS

A noteworthy proportion of patients with metabolic dysfunction-associated steatotic liver disease (MASLD) have an indeterminate vibration-controlled transient elastography (VCTE). Among these patients, we aimed to identify candidates for MASLD treatment by diagnosing significant fibrosis.

METHODS

This was a real-world prospective study including a large dataset of MASLD patients with paired VCTE and liver biopsy from 6 centers. A total of 1196 patients were recruited and divided in training (3 centers, Spain), internal validation (2 centers, Spain), and external validation (1 center, United States) cohorts. In patients with indeterminate liver stiffness measurement (LSM) (8-12 kPa), a diagnostic algorithm was developed to identify significant fibrosis, defined as histological stage ≥F2. Statistical analysis was performed using Gaussian mixture model (GMM) and k-means unsupervised clusterization.

RESULTS

From the eligible population, 33%, 29%, and 31% had indeterminate VCTE in the training, internal and external validation samples, respectively. The controlled attenuation parameter allowed the differentiation of GMM clusters with a cutoff of 280 dB/m (area under the curve, 0.89; 95% confidence interval, 0.86-0.97). Within patients with <280 dB/m, a LSM between 8.0-9.0 kPa showed a 93% sensitivity and a 91% negative predictive value to exclude significant fibrosis. Among patients with ≥280 dB/m, a LSM between 10.3 and 12.0 kPa diagnosed significant fibrosis with a 91% specificity. Applying this algorithm to the validation cohorts, 36% of the indeterminate VCTE were reallocated. The reallocated high-risk group showed a prevalence of 86% significant fibrosis, opening the therapeutic window for MASLD patients.

CONCLUSIONS

To identify candidates for MASLD treatment among indeterminate VCTE, an algorithm-based on the sequential combination of LSM and controlled attenuation parameter thresholds can optimize the diagnosis of moderate-to-advanced fibrosis.

Hydroxycitrate delays early mortality in mice and promotes muscle regeneration while inducing a rich hepatic energetic status

ATP citrate lyase (ACLY) inhibitors have the potential of modulating central processes in protein, carbohydrate, and lipid metabolism, which can have relevant physiological consequences in aging and age-related diseases. Here, we show that hepatic phospho-active ACLY correlates with overweight and Model for End-stage Liver Disease score in humans. Wild-type mice treated chronically with the ACLY inhibitor potassium hydroxycitrate exhibited delayed early mortality. In AML12 hepatocyte cultures, the ACLY inhibitors potassium hydroxycitrate, SB-204990, and bempedoic acid fostered lipid accumulation, which was also observed in the liver of healthy-fed mice treated with potassium hydroxycitrate. Analysis of soleus tissue indicated that potassium hydroxycitrate produced the modulation of wound healing processes. In vivo, potassium hydroxycitrate modulated locomotor function toward increased wire hang performance and reduced rotarod performance in healthy-fed mice, and improved locomotion in mice exposed to cardiotoxin-induced muscle atrophy. Our findings implicate ACLY and ACLY inhibitors in different aspects of aging and muscle regeneration.

Liver fibrosis analysis using digital pathology

Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3 s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™..　In this review, we provide an overview of role of digital pathology on the evaluation of fibrosis in liver disease and the characteristics of recent methods to assess liver fibrosis.

OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification

Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images.

PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various components across multiple levels, including regions (cortex, medulla), functional units (glomeruli, tubules), and cells (podocytes, mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research, we introduce a novel universal proposition learning approach, called panoramic renal pathology segmentation (PrPSeg), designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper, we propose (1) the design of a comprehensive universal proposition matrix for renal pathology, facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture, with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function, quantifying the inter-object relationships across the kidney.

Hepatic Fibrosis and Cancer: The Silent Threats of Metabolic Syndrome

Metabolic dysfunction-associated steatotic (fatty) liver disease (MASLD), previously termed non-alcoholic fatty liver disease, is a worldwide epidemic that can lead to hepatic inflammation, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The disease is typically a component of the metabolic syndrome that accompanies obesity, and is often overlooked because the liver manifestations are clinically silent until late-stage disease is present (i.e., cirrhosis). Moreover, Asian populations, including Koreans, have a higher fraction of patients who are lean, yet their illness has the same prognosis or worse than those who are obese. Nonetheless, ongoing injury can lead to hepatic inflammation and ballooning of hepatocytes as classic features. Over time, fibrosis develops following activation of hepatic stellate cells, the liver's main fibrogenic cell type. The disease is usually more advanced in patients with type 2 diabetes mellitus, indicating that all diabetic patients should be screened for liver disease. Although there has been substantial progress in clarifying pathways of injury and fibrosis, there no approved therapies yet, but current research seeks to uncover the pathways driving hepatic inflammation and fibrosis, in hopes of identifying new therapeutic targets. Emerging molecular methods, especially single cell sequencing technologies, are revolutionizing our ability to clarify mechanisms underlying MASLD-associated fibrosis and HCC.

Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions

The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.

Liver fibrosis phenotyping and severity scoring by quantitative image analysis of biopsy slides

BACKGROUND & AIMS

Digital pathology image analysis can phenotype liver fibrosis using histological traits that reflect collagen content, morphometry and architecture. Here, we aimed to calculate fibrosis severity scores to quantify these traits.

METHODS

Liver biopsy slides were categorised by Ishak stage and aetiology. We used a digital pathology technique to calculate four fibrosis severity scores: Architecture Composite Score (ACS), Collagen Composite Score (CCS), Morphometric Composite Score (MCS) and Phenotypic Fibrosis Composite Score (PH-FCS). We compared how these scores varied according to disease stage and aetiology.

RESULTS

We included 80 patients (40% female, mean age 59.0 years, mean collagen proportionate area 17.1%) with mild (F0-2, n = 28), moderate (F3-4, n = 17) or severe (F5-6, n = 35) fibrosis. All four aetiology independent scores corelated with collagen proportionate area (ACS: rp  = .512, CCS: rp  = .727, MCS: rp  = .777, PFCS: r = .772, p < .01 for all) with significant differences between moderate and severe fibrosis (p < .05). ACS increased primarily between moderate and severe fibrosis (by 95% to 226% depending on underlying aetiology), whereas MCS and CCS accumulation was more varied. We used 28 qFTs that distinguished between autoimmune- and alcohol-related liver disease to generate an MCS that significantly differed between mild and severe fibrosis for these aetiologies (p < .05).

CONCLUSIONS

We describe four aetiology-dependent and -independent severity scores that quantify fibrosis architecture, collagen content and fibre morphometry. This approach provides additional insight into how progression of architectural changes and accumulation of collagen may differ depending on underlying disease aetiology.

Pancreatic steatosis and iron overload increases cardiovascular risk in non-alcoholic fatty liver disease

Objective

To assess the prevalence of pancreatic steatosis and iron overload in non-alcoholic fatty liver disease (NAFLD) and their correlation with liver histology severity and the risk of cardiometabolic diseases.

Method

A prospective, multicenter study including NAFLD patients with biopsy and paired Magnetic Resonance Imaging (MRI) was performed. Liver biopsies were evaluated according to NASH Clinical Research Network, hepatic iron storages were scored, and digital pathology quantified the tissue proportionate areas of fat and iron. MRI-biomarkers of fat fraction (PDFF) and iron accumulation (R2*) were obtained from the liver and pancreas. Different metabolic traits were evaluated, cardiovascular disease (CVD) risk was estimated with the atherosclerotic CVD score, and the severity of iron metabolism alteration was determined by grading metabolic hiperferritinemia (MHF). Associations between CVD, histology and MRI were investigated.

Results

In total, 324 patients were included. MRI-determined pancreatic iron overload and moderate-to severe steatosis were present in 45% and 25%, respectively. Liver and pancreatic MRI-biomarkers showed a weak correlation (r=0.32 for PDFF, r=0.17 for R2*). Pancreatic PDFF increased with hepatic histologic steatosis grades and NASH diagnosis (p<0.001). Prevalence of pancreatic steatosis and iron overload increased with the number of metabolic traits (p<0.001). Liver R2* significantly correlated with MHF (AUC=0.77 [0.72-0.82]). MRI-determined pancreatic steatosis (OR=3.15 [1.63-6.09]), and iron overload (OR=2.39 [1.32-4.37]) were independently associated with high-risk CVD. Histologic diagnosis of NASH and advanced fibrosis were also associated with high-risk CVD.

Conclusion

Pancreatic steatosis and iron overload could be of utility in clinical decision-making and prognostication of NAFLD.

Surveillance of the progression and assessment of treatment endpoints for nonalcoholic steatohepatitis

Nonalcoholic steatohepatitis (NASH) is an aggressive form of nonalcoholic fatty liver disease (NAFLD) characterized by steatosis-associated inflammation and liver injury. Without effective treatment or management, NASH can have life-threatening outcomes. Evaluation and identification of NASH patients at risk for adverse outcomes are therefore important. Key issues in screening NASH patients are the assessment of advanced fibrosis, differentiation of NASH from simple steatosis, and monitoring of dynamic changes during follow-up and treatment. Currently, NASH staging and evaluation of the effectiveness for drugs still rely on pathological diagnosis, despite sample error issues and the subjectivity associated with liver biopsy. Optimizing the pathological assessment of liver biopsy samples and developing noninvasive surrogate methods for accessible, accurate, and safe evaluation are therefore critical. Although noninvasive methods including elastography, serum soluble biomarkers, and combined models have been implemented in the last decade, noninvasive diagnostic measurements are not widely applied in clinical practice. More work remains to be done in establishing cost-effective strategies both for screening for at-risk NASH patients and identifying changes in disease severity. In this review, we summarize the current state of noninvasive methods for detecting steatosis, steatohepatitis, and fibrosis in patients with NASH, and discuss noninvasive assessments for screening at-risk patients with a focus on the characteristics that should be monitored at follow-up.

Subclinical versus advanced forms of alcohol-related liver disease: Need for early detection

Alcohol-related liver disease (ALD) consists of a wide spectrum of clinical manifestations and pathological features, ranging from asymptomatic patients to decompensated cirrhosis and hepatocellular carcinoma. Patients with heavy alcohol intake and advanced fibrosis often develop a subacute form of liver failure called alcohol-induced hepatitis (AH). Globally, most patients with ALD are identified at late stages of the disease, limiting therapeutic interventions. Thus, there is a need for early detection of ALD patients, which is lacking in most countries. The identification of alcohol misuse is hampered by the existence of alcohol underreporting by many patients. There are useful biomarkers that can detect recent alcohol use. Moreover, there are several non-invasive techniques to assess the presence of advanced fibrosis among patients with alcohol misuse, which could identify patients at high risk of liver related events or early death. In this review, we discuss differences between early stages of ALD and AH as the cornerstone of advanced forms. A global overview of epidemiological, anthropometric, clinical, analytical, histological, and molecular differences is summarized in this article. We propose that campaigns aimed at identifying patients with subclinical forms can prevent the development of life-threatening forms.

Digital Pathology: New Initiative in Pathology

Digital pathology (DP) is an emerging field of pathology that manages information generated from digitized specimen slides [...].

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.

Current status and challenges in the drug treatment for fibrotic nonalcoholic steatohepatitis

Currently, nonalcoholic steatohepatitis (NASH) is one of the most common forms of chronic hepatitis, increasing the burden of health care worldwide. In patients with NASH, the fibrosis stage is the most predictive factor of long-term events. However, there are still no drugs approved by the Food and Drug Administration of the United States for treating biopsy-proven NASH with fibrosis or cirrhosis. Although some novel drugs have shown promise in preclinical studies and led to improvement in terms of hepatic fat content and steatohepatitis, a considerable proportion of them have failed to achieve histological endpoints of fibrosis improvement. Due to the large number of NASH patients and adverse clinical outcomes, the search for novel drugs is necessary. In this review, we discuss current definitions for the evaluation of treatment efficacy in fibrosis improvement for NASH patients, and we summarize novel agents in the pipeline from different mechanisms and phases of trial. We also critically review the challenges we face in the development of novel agents for fibrotic NASH and NASH cirrhosis.

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

Steady progress over four decades toward understanding the pathogenesis and clinical consequences of hepatic fibrosis has led to the expectation of effective antifibrotic drugs, yet none has been approved. Thus, an assessment of the field is timely, to clarify priorities and accelerate progress. Here, we highlight the successes to date but, more importantly, identify gaps and unmet needs, both experimentally and clinically. These include the need to better define cell-cell interactions and etiology-specific elements of fibrogenesis and their link to disease-specific drivers of portal hypertension. Success in treating viral hepatitis has revealed the remarkable capacity of the liver to degrade scar in reversing fibrosis, yet we know little of the mechanisms underlying this response. Thus, there is an exigent need to clarify the cellular and molecular mechanisms of fibrosis regression in order for therapeutics to mimic the liver's endogenous capacity. Better refined and more predictive in vitro and animal models will hasten drug development. From a clinical perspective, current diagnostics are improving but not always biologically plausible or sufficiently accurate to supplant biopsy. More urgently, digital pathology methods that leverage machine learning and artificial intelligence must be validated in order to capture more prognostic information from liver biopsies and better quantify the response to therapies. For more refined treatment of NASH, orthogonal approaches that integrate genetic, clinical, and pathological data sets may yield treatments for specific subphenotypes of the disease. Collectively, these and other advances will strengthen and streamline clinical trials and better link histologic responses to clinical outcomes.

Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-score: proportionate area of staining intensities clusters) measurements were independently performed. Our data show that I-scores and C-scores increase with inflammation grades (p < 0.001). High correlation was seen for CH (ρ = 0.85-0.88), but only moderate for NAFLD (ρ = 0.5-0.53). I-score (p = 0.008) and C-score (p = 0.002) were higher for CH than NAFLD. Our MATLAB algorithm performed better than QuPath software for the diagnosis of low-moderate inflammation (p < 0.05). C-score AUC for classifying NASH was 0.75 (95%CI, 0.65-0.84) and for moderate/severe CH was 0.99 (95%CI, 0.97-1.00). Digital pathology measurements increased with fibrosis stages (p < 0.001). In conclusion, quantitative and morphometric metrics of inflammatory burden obtained by digital pathology correlate well with pathologists' scores, showing a higher accuracy for the evaluation of CH than NAFLD.

The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics

Histology remains essential for the diagnosis and management of many disorders affecting the liver. However, the biopsy procedure itself is associated with a low risk of harm to the patient and cost to the health services; samples may not be adequate and are subject to sampling variation. Furthermore, interpretation often depends on the skill of the pathologist. Increasingly, new techniques are becoming available that are altering the indications for liver biopsy. Many diseases of the liver can be diagnosed and managed using serological and radiological techniques; the degree of fibrosis and fat can often be assessed by serological or imaging techniques and the nature of space occupying lesions defined by serology, imaging and use of liquid biopsy. However, these techniques, too, are subject to limitations: sensitivity and specificity is not always adequate for diagnosis or management; some techniques are expensive and often also require expert interpretation. Although there may be less need for liver biopsy today, histology remains the gold standard as well as an essential tool for the diagnosis and management of many conditions, especially where there are multiple pathologies, or where a diagnosis cannot or has not been made by alternative approaches. Until less invasive techniques become more reliable and accessible, liver histology will remain a key investigation.