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

Liver fibrosis progression analyzed with AI predicts renal decline

Background & Aims

The relationship between biopsy-proven liver fibrosis progression and renal function decline in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) has not been fully elucidated. We used an automated quantitative liver fibrosis assessment (qFibrosis) technique to investigate the temporal changes in regional liver fibrosis.

Methods

This retrospective longitudinal study included 68 MASLD patients and their paired formalin-fixed sections of liver biopsies. One hundred eighty-four fibrosis parameters were quantified in five different hepatic regions, including portal tract, peri-portal, zone 2, peri-central and central vein regions, and qFibrosis continuous values were calculated for all samples based on 10 fibrosis parameters using qFibrosis assessment. Liver fibrosis progression (QLF+, n = 18) and regression (QLF-, n = 23) was defined as at least a 20% relative change in qFibrosis over a 23-month follow-up. Renal function decline was assessed by estimated glomerular filtration rate (eGFR) changes.

Results

The eGFR decline was greater in the QLF+ group (106.53 ± 13.71 ml/min/1.73 m2 vs. 105.28 ± 12.46 ml/min/1.73 m2) than in the QLF- group (110.87 ± 14.58 ml/min/1.73 m2 vs. 114.18 ± 14.81 ml/min/1.73 m2). In addition, liver fibrosis changes in the central vein and pericentral regions were more strongly associated with eGFR decline than in periportal, zone 2 and portal tract regions. We combined these parameters to construct a prediction model, which better differentiated eGFR decline (a cut-off value of qFibrosis combined index = 0.52, p <0.001).

Conclusions

A decline in renal function is significantly related to liver fibrosis progression in MASLD. Regional qFibrosis assessment may efficiently predict eGFR decline, thus highlighting the importance of assessing renal function in patients with MASLD with worsening liver fibrosis.

Impact and implications

The study shows that liver fibrosis progression assessed by qFibrosis may be associated with renal function decline, which provides a new perspective for understanding complex pathological processes. A combination of artificial intelligence and digital pathology may earlier and more precisely quantify the progression of regional liver fibrosis, thus better identifying changes in renal function. This opens the possibility of early interventions, which are essential to improve patients' outcomes.

Utility of AI digital pathology as an aid for pathologists scoring fibrosis in MASH

BACKGROUND & AIMS

Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histological response in clinical trials. We evaluated the utility of an artificial intelligence (AI) digital pathology (DP) platform to help pathologists improve the reliability of fibrosis staging.

METHODS

A total of 120 digitized histology slides from two trials (NCT03517540, NCT03912532) were analyzed by four expert hepatopathologists, with and without AI assistance in a randomized, crossover design. We utilized an AI DP platform consisting of unstained second harmonic generation/two photon excitation fluorescence (SHG/TPEF) images and AI quantitative fibrosis (qFibrosis) values.

RESULTS

AI assistance significantly improved inter-pathologist kappa for fibrosis staging, particularly for early fibrosis (F0-F2), with reduced variance around the median reads. Intra-pathologist kappa was unchanged. AI assistance increased pathologist concordance for identifying clinical trial inclusion cases (F2-F3) from 45% to 71%, exclusion cases (F0/F1/F4) from 38% to 55%, and evaluation of fibrosis response to treatment from 49% to 61%. SHG/TPEF images, qFibrosis continuous values, and qFibrosis stage were considered useful by at least three out of four pathologists in 83%, 55%, and 38% of cases, respectively. In the context of a clinical trial, the increase in inter-pathologist concordance was modeled to result in a ∼25% reduction in the potential need for adjudication as well as a ∼45% increase in the study power for a kappa improvement from ∼0.4 to ∼0.7.

CONCLUSIONS

The use of AI DP enhances inter-rater reliability of fibrosis staging for MASH. This indicates that the SHG/TPEF-based AI DP tool is useful for assisting pathologists in assessing fibrosis, thereby enhancing clinical trial efficiency and reliability of fibrosis readouts in response to treatments.

IMPACT AND IMPLICATIONS

Implementing an AI DP platform as a tool for pathologists significantly improved inter-pathologist agreement on fibrosis staging, particularly for early-stage fibrosis (F0-F2), which is critical for clinical trial eligibility. The second harmonic generation imaging technology used in conjunction with AI quantitative scores provided enhanced visualization of fibrosis with an indication of severity along the disease continuum. This led to increased pathologist confidence in fibrosis staging and, therefore, increased pathologist concordance for the classification of clinical trial inclusion/exclusion and evaluation of treatment, compared to a standard scoring method based on traditional stains without AI assistance. Improved pathologist concordance with AI assistance could streamline clinical trial processes, reducing the need for adjudication and enhancing study power, potentially decreasing required sample sizes. Continued exploration of the utility of AI assistance across a broader range of pathologists and in prospective clinical trials will be essential for validating the effectiveness of AI assistance.

The Emerging Role of Second Harmonic Generation/Two Photon Excitation for Precision Digital Analysis of Liver Fibrosis in MASH Clinical Trials

Conventional histopathological evaluation of liver biopsy slides has been invaluable in assessing the causes of liver injury, the severity of the underlying disease processes, and the degree of resulting fibrosis. However, the use of conventional histologic assessments as endpoints in clinical trials is limited by the reliability of scoring systems, variability in interpretation of histologic features and translation of continuous variables into categorical scores. To increase the precision and reproducibility of liver biopsy assessment, several artificial intelligence/machine learning (AI/ML) approaches have been developed to analyse high resolution digital images of liver biopsy specimens. Multiple AI/ML platforms are in development with promising results in post-hoc analyses of clinical trial biopsies. One such technique employs images generated by Second Harmonic Generation/Two Photon Excitation (SHG/TPE) microscopy that uniquely uses unstained liver biopsies to provide high resolution images of collagen fibres to assess and quantify collagen morphometry, and avoid challenges related to staining variability. One SHG/TPE microscopy methodology coupled with AI/ML based analysis, qFibrosis™, has been used post-hoc as an exploratory endpoint in several clinical trials for metabolic dysfunction-associated steatohepatitis (MASH) demonstrating its ability to provide a consistent and more nuanced assessment of liver fibrosis that still correlates well with traditional staging. This review summarizes the development of qFibrosis and outlines the need for additional studies to validate it as a sensitive marker for changes in fibrosis in the context of treatment trials and correlate these changes with subsequent liver-related outcomes.

Second harmonic generation microscopy reveals the spatial orientation of glutamine-potentiated liver regeneration after hepatectomy

BACKGROUND

Glutamine (Gln) is a critical amino acid for energy expenditure. It participates in extracellular matrix (ECM) formation and circulates in the hepatic parenchyma in a spatial-oriented manner. Posthepatectomy liver mass recovery poses a regenerative challenge. However, little is known about the role of Gln in liver regeneration, notably the spatial orientation in the remodeling process. This study aimed to elucidate Gln-potentiated liver regeneration and ECM remodeling after mass loss.

METHODS

We studied the regenerative process in hepatectomized mice supplemented with Gln. Second harmonic generation/two-photon excitation fluorescence microscopy, an artificial intelligence-assisted structure-based imaging, was used to demonstrate the spatial-oriented process in a hepatic acinus.

RESULTS

Gln promotes liver mass regrowth through the cell cycle, Gln metabolism, and adipogenesis pathways after hepatectomy. Ornithine transaminase, one of the upregulated enzymes, showed temporal, spatial, and functional correspondence with the regeneration process. Second harmonic generation/two-photon excitation fluorescence microscopy highlighted transient hepatic steatosis and ECM collagen synthesis, predominantly in the portal tract instead of the central vein area. Structural remodeling was also observed in the portal tract area.

CONCLUSIONS

Gln promotes liver regeneration through cellular proliferation and metabolic reprogramming after hepatectomy. Using structure-based imaging, we found that Gln potentiated hepatic steatosis and ECM collagen deposition predominantly in the portal tract area. These results highlighted the spatial orientation and mechanistic implications of Gln in liver regeneration.

Aramchol improves hepatic fibrosis in metabolic dysfunction-associated steatohepatitis: Results of multimodality assessment using both conventional and digital pathology

BACKGROUND AND AIMS

Antifibrotic trials rely on conventional pathology despite recognized limitations. We compared single-fiber digital image analysis with conventional pathology to quantify the antifibrotic effect of Aramchol, a stearoyl-CoA desaturase 1 inhibitor in development for metabolic dysfunction-associated steatohepatitis.

APPROACH AND RESULTS

Fifty-one patients with metabolic dysfunction-associated steatohepatitis enrolled in the open-label part of the ARMOR trial received Aramchol 300 mg BID and had paired pre-post treatment liver biopsies scored by consensus among 3 hepatopathologists, and separately assessed by a digital image analysis platform (PharmaNest) that generates a continuous phenotypic Fibrosis Composite Severity (Ph-FCS) score. Fibrosis improvement was defined as: ≥1 NASH Clinical Research Network (NASH-CRN) stage reduction; "improved" by ranked pair assessment; reduction in Ph-FCS ("any" for ≥0.3 absolute reduction and "substantial" for ≥25% relative reduction). Fibrosis improved in 31% of patients (NASH-CRN), 51% (ranked pair assessment), 74.5% (any Ph-FCS reduction), and 41% (substantial Ph-FCS reduction). Most patients with stable fibrosis by NASH-CRN or ranked pair assessment had a Ph-FCS reduction (a third with substantial reduction). Fibrosis improvement increased with treatment duration: 25% for <48 weeks versus 39% for ≥48 weeks by NASH-CRN; 43% versus 61% by ranked pair assessment, mean Ph-FCS reduction -0.54 (SD: 1.22) versus -1.72 (SD: 1.02); Ph-FCS reduction (any in 54% vs. 100%, substantial in 21% vs. 65%). The antifibrotic effect of Aramchol was corroborated by reductions in liver stiffness, Pro-C3, and enhanced liver fibrosis. Changes in Ph-FCS were positively correlated with changes in liver stiffness.

CONCLUSIONS

Continuous fibrosis scores generated in antifibrotic trials by digital image analysis quantify antifibrotic effects with greater sensitivity and a larger dynamic range than conventional pathology.

A machine learning based algorithm accurately stages liver disease by quantification of arteries

A major histologic feature of cirrhosis is the loss of liver architecture with collapse of tissue and vascular changes per unit. We developed qVessel to quantify the arterial density (AD) in liver biopsies with chronic disease of varied etiology and stage. 46 needle liver biopsy samples with chronic hepatitis B (CHB), 48 with primary biliary cholangitis (PBC) and 43 with metabolic dysfunction-associated steatotic liver disease (MASLD) were collected at the Shuguang Hospital. The METAVIR system was used to assess stage. The second harmonic generation (SHG)/two-photon images were generated from unstained slides. Collagen proportionate area (CPA) using SHG. AD was counted using qVessel (previously trained on manually labeled vessels by stained slides (CD34/a-SMA/CK19) and developed by a decision tree algorithm). As liver fibrosis progressed from F1 to F4, we observed that both AD and CPA gradually increases among the three etiologies (P < 0.05). However, at each stage of liver fibrosis, there was no significant difference in AD or CPA between CHB and PBC compared to MASLD (P > 0.05). AD and CPA performed similar diagnostic efficacy in liver cirrhosis (P > 0.05). Using the qVessel algorithm, we discovered a significant correlation between AD, CPA and METAVIR stages in all three etiologies. This suggests that AD could underpin a novel staging system.

Steatotic Liver Disease: Navigating Pathologic Features, Diagnostic Challenges, and Emerging Insights

Steatotic liver disease (SLD) is now used as an overarching category encompassing five subcategories: metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic and alcohol related/associated liver disease (MetALD), alcohol-related/associated liver disease (ALD), SLD with specific etiology, and cryptogenic SLD. This review summarizes foundational and recent advances in the histologic evaluation of SLD, including common pathologic features across all subcategories, distinctions associated with different etiologies, scoring and grading systems, and the evolution of digital pathology techniques for SLD assessment.

Hepatic Steatosis Analysis in Metabolic Dysfunction-Associated Steatotic Liver Disease Based on Artificial Intelligence

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by the accumulation of fat in the liver, excluding excessive alcohol consumption and other known causes of liver injury. Animal models are often used to explore different pathogenic mechanisms and therapeutic targets of MASLD. The aim of this study is to apply an artificial intelligence (AI) system based on second-harmonic generation (SHG)/two-photon-excited fluorescence (TPEF) technology to automatically assess the dynamic patterns of hepatic steatosis in MASLD mouse models.

METHODS

We evaluated the characteristics of hepatic steatosis in mouse models of MASLD using AI analysis based on SHG/TPEF images. Six different models of MASLD were induced in C57BL/6 mice by feeding with a western or high-fat diet, with or without fructose in their drinking water, and/or by weekly injections of carbon tetrachloride.

RESULTS

Body weight, serum lipids, and liver enzyme markers increased at 8 and 16 weeks in each model compared to baseline. Steatosis grade showed a steady upward trend. However, the non-alcoholic steatohepatitis (NASH) Clinical Research Network (CRN) histological scoring method detected no significant difference between 8 and 16 weeks. In contrast, AI analysis was able to quantify dynamic changes in the area, number, and size of hepatic steatosis automatically and objectively, making it more suitable for preclinical MASLD animal experiments.

CONCLUSIONS

AI recognition technology may be a new tool for the accurate diagnosis of steatosis in MASLD, providing a more precise and objective method for evaluating steatosis in preclinical murine MASLD models under various experimental and treatment conditions.

Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology

BACKGROUND AND AIMS

Metabolic dysfunction-associated steatohepatitis (MASH) with bridging fibrosis is a critical stage in the evolution of fatty liver disease. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence (AI) provides sensitive and reproducible quantitation of liver fibrosis. This methodology was applied to gain an in-depth understanding of intra-stage fibrosis changes and septa analyses in a homogenous, well-characterised group with MASH F3 fibrosis.

METHODS

Paired liver biopsies (baseline [BL] and end of treatment [EOT]) of 57 patients (placebo, n = 17 and tropifexor n = 40), with F3 fibrosis stage at BL according to the clinical research network (CRN) scoring, were included. Unstained sections were examined using SHG/TPEF microscopy with AI. Changes in liver fibrosis overall and in five areas of liver lobules were quantitatively assessed by qFibrosis. Progressive, regressive septa, and 12 septa parameters were quantitatively analysed.

RESULTS

qFibrosis demonstrated fibrosis progression or regression in 14/17 (82%) patients receiving placebo, while the CRN scoring categorised 11/17 (65%) as 'no change'. Radar maps with qFibrosis readouts visualised quantitative fibrosis dynamics in different areas of liver lobules even in cases categorised as 'No Change'. Measurement of septa parameters objectively differentiated regressive and progressive septa (p < .001). Quantitative changes in individual septa parameters (BL to EOT) were observed both in the 'no change' and the 'regression' subgroups, as defined by the CRN scoring.

CONCLUSION

SHG/TPEF microscopy with AI provides greater granularity and precision in assessing fibrosis dynamics in patients with bridging fibrosis, thus advancing knowledge development of fibrosis evolution in natural history and in clinical trials.

Denifanstat for the treatment of metabolic dysfunction-associated steatohepatitis: a multicentre, double-blind, randomised, placebo-controlled, phase 2b trial

BACKGROUND

Denifanstat, an oral fatty acid synthase (FASN) inhibitor, blocks de-novo lipogenesis, a key pathway driving progressive lipotoxicity, inflammation, and fibrosis in metabolic dysfunction-associated steatohepatitis (MASH). This study aimed to examine the safety and efficacy of denifanstat for improving liver histology in individuals with MASH and moderate to advanced fibrosis.

METHODS

This multicentre, double-blind, randomised, placebo-controlled, phase 2b trial was conducted at 100 clinical sites in the USA, Canada, and Poland. After a screening period of up to 90 days, participants aged 18 years and older with biopsy-confirmed MASH and stage F2 or F3 fibrosis were randomly assigned (2:1) to receive either 50 mg oral denifanstat or placebo once per day for 52 weeks. Participants were dynamically allocated to treatment groups via a centrally administered interactive web-based response system and stratified by type 2 diabetes, region, and fibrosis stage. Investigators, patients, and the sponsor were masked to group allocation until database lock. The primary efficacy endpoints were a 2-point or greater improvement in non-alcoholic fatty liver disease activity score (NAS) without a worsening of fibrosis or MASH resolution with a 2-point or greater improvement in NAS without a worsening of fibrosis at week 52, assessed by intention to treat. Safety was assessed in all participants who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov, NCT04906421, and is closed for enrolment.

FINDINGS

Of the 1087 individuals screened between June 2, 2021, and June 28, 2022, 168 eligible participants were randomly assigned to receive a dose of 50 mg denifanstat once per day (n=112) or placebo (n=56). All 168 participants (100 female, 68 male) received at least one dose of study treatment. In the ITT population, 42 (38%) of 112 participants in the denifanstat group had a 2-point or greater improvement in NAS without a worsening of fibrosis versus nine (16%) of 56 participants in the placebo group (common risk difference 21·0%, 95% CI 8·1-33·9; p=0·0035). 29 (26%) of 112 participants in the denifanstat group showed MASH resolution with a 2-point or greater improvement in NAS without a worsening of fibrosis compared with six (11%) of 56 participants in the placebo group (common risk difference 13·0%, 0·7-25·3; p=0·0173). The most common treatment-emergent adverse events were COVID-19 (19 [17%] of 112 in the denifanstat group vs six [11%] of 56) in the placebo group, dry eye symptoms (ten [9%] of 112 vs eight [14%] of 56), and alopecia (21 [19%] of 112 vs two [4%] of 56). All adverse events considered to be related to the study drug were of grade 1 or grade 2. None of the serious adverse events (13 [12%] of 112 participants in the denifanstat group vs three [5%] of 56 in the placebo group) were considered drug-related.

INTERPRETATION

Treatment with denifanstat resulted in statistically significant and clinically meaningful improvements in disease activity, MASH resolution, and fibrosis. The results of this phase 2b trial support the advancement of denifanstat to phase 3 development.

FUNDING

Sagimet Biosciences.

Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis

Background

Chronic liver disease diagnoses depend on liver biopsy histopathological assessment. However, due to the limitations associated with biopsy, there is growing interest in the use of quantitative digital pathology to support pathologists. We evaluated the performance of computational algorithms in the assessment of hepatic inflammation in an autoimmune hepatitis in which inflammation is a major component.

Methods

Whole-slide digital image analysis was used to quantitatively characterize the area of tissue covered by inflammation [Inflammation Density (ID)] and number of inflammatory foci per unit area [Focal Density (FD)] on tissue obtained from 50 patients with autoimmune hepatitis undergoing routine liver biopsy. Correlations between digital pathology outputs and traditional categorical histology scores, biochemical, and imaging markers were assessed. The ability of ID and FD to stratify between low-moderate (both portal and lobular inflammation ≤1) and moderate-severe disease activity was estimated using the area under the receiver operating characteristic curve (AUC).

Results

ID and FD scores increased significantly and linearly with both portal and lobular inflammation grading. Both ID and FD correlated moderately-to-strongly and significantly with histology (portal and lobular inflammation; 0.36≤R≤0.69) and biochemical markers (ALT, AST, GGT, IgG, and gamma globulins; 0.43≤R≤0.57). ID (AUC: 0.85) and FD (AUC: 0.79) had good performance for stratifying between low-moderate and moderate-severe inflammation.

Conclusion

Quantitative assessment of liver biopsy using quantitative digital pathology metrics correlates well with traditional pathology scores and key biochemical markers. Whole-slide quantification of disease can support stratification and identification of patients with more advanced inflammatory disease activity.

Modernizing metabolic dysfunction-associated steatotic liver disease diagnostics: the progressive shift from liver biopsy to noninvasive techniques

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing public health concern worldwide. Liver biopsy is the gold standard for diagnosing and staging MASLD, but it is invasive and carries associated risks. In recent years, there has been significant progress in developing noninvasive techniques for evaluation. This review article discusses briefly current available noninvasive assessments and the various liver biopsy techniques available for MASLD, including invasive techniques such as transjugular and transcutaneous needle biopsy, intraoperative/laparoscopic biopsy, and the evolving role of endoscopic ultrasound-guided biopsy. In addition to discussing the various biopsy techniques, we review the current state of knowledge on the histopathologic evaluation of MASLD, including the various scoring systems used to grade and stage the disease. We also explore current and alternative modalities for histopathologic evaluation, such as whole slide imaging and the utility of immunohistochemistry. Overall, this review article provides a comprehensive overview of the progress in liver biopsy techniques for MASLD and compares invasive and noninvasive modalities. However, beyond clinical trials, the practical application of liver biopsy may be limited, as ongoing advancements in noninvasive fibrosis assessments are expected to more effectively identify candidates for MASLD treatment in real-world settings.

Liver biopsy evaluation in MASH drug development: Think thrice, act wise

During recent decades, the metabolic dysfunction-associated steatohepatitis (MASH) field has witnessed several paradigm shifts, including the recognition of liver fibrosis as the main predictor of major adverse liver outcomes. Throughout this evolution, liver histology has been recognised as one of the main hurdles in MASH drug development due to its invasive nature, associated cost, and high inter- and intra-reader variability. Collective experience demonstrates the importance of consistency in the central reading process, where consensus methods have emerged as appropriate ways to mitigate against well-known challenges. Using crystalized knowledge in the field, stakeholders should collectively work towards the next paradigm shift, where non-invasive biomarkers will be considered surrogate endpoints for accelerated approval. In this review, we provide an overview of the evolution of the regulatory histology endpoints and the liver biopsy reading process, within the MASH trial landscape, over recent decades; we then review the biggest challenges associated with liver biopsy endpoints. Finally, we discuss and provide recommendations on the best practices for liver biopsy evaluation in MASH drug development.

A roadmap for clinical trials in MASH-related compensated cirrhosis

Although metabolic dysfunction-associated steatohepatitis (MASH) is rapidly becoming a leading cause of cirrhosis worldwide, therapeutic options are limited and the number of clinical trials in MASH-related compensated cirrhosis is low as compared to those conducted in earlier disease stages. Moreover, designing clinical trials in MASH cirrhosis presents a series of challenges regarding the understanding and conceptualization of the natural history, regulatory considerations, inclusion criteria, recruitment, end points and trial duration, among others. The first international workshop on the state of the art and future direction of clinical trials in MASH-related compensated cirrhosis was held in April 2023 at Vall d'Hebron University Hospital in Barcelona (Spain) and was attended by a group of international experts on clinical trials from academia, regulatory agencies and industry, encompassing expertise in MASH, cirrhosis, portal hypertension, and regulatory affairs. The presented Roadmap summarizes important content of the workshop on current status, regulatory requirements and end points in MASH-related compensated cirrhosis clinical trials, exploring alternative study designs and highlighting the challenges that should be considered for upcoming studies on MASH cirrhosis.

Role of artificial intelligence in staging and assessing of treatment response in MASH patients

Background and Aims

The risk of disease progression in MASH increases proportionally to the pathological stage of fibrosis. This latter is evaluated through a semi-quantitative process, which has limited sensitivity in reflecting changes in disease or response to treatment. This study aims to test the clinical impact of Artificial Intelligence (AI) in characterizing liver fibrosis in MASH patients.

Methods

The study included 60 patients with clinical pathological diagnosis of MASH. Among these, 17 received a medical treatment and underwent a post-treatment biopsy. For each biopsy (n = 77) a Sirius Red digital slide (SR-WSI) was obtained. AI extracts >30 features from SR-WSI, including estimated collagen area (ECA) and entropy of collagen (EnC).

Results

AI highlighted that different histopathological stages are associated with progressive and significant increase of ECA (F2: 2.6% ± 0.4; F3: 5.7% ± 0.4; F4: 10.9% ± 0.8; p: 0.0001) and EnC (F2: 0.96 ± 0.05; F3: 1.24 ± 0.06; F4: 1.80 ± 0.11, p: 0.0001); disclosed the heterogeneity of fibrosis among pathological homogenous cases; revealed post treatment fibrosis modification in 76% of the cases (vs 56% detected by histopathology).

Conclusion

AI characterizes the fibrosis process by its true, continuous, and non-categorical nature, thus allowing for better identification of the response to anti-MASH treatment.

Natural history and progression of metabolic dysfunction-associated steatotic liver disease

The natural history of metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as non-alcoholic fatty liver disease (NAFLD), is complex and long. A minority of patients develop inflammation and risk progressive fibrosis that can result in cirrhosis. Progression to cirrhosis occurs in 3-5% of patients and often takes more than 20 years. This narrative review presents an update on the natural history of MASLD, discussing studies and risk estimates for progression to severe outcomes, such as decompensated cirrhosis or hepatocellular carcinoma. We highlight the dynamic progression of liver damage, how to identify patients whose disease progresses over time, and how risk factors might be mitigated to reduce the risk for disease progression.

AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25-69%) and number of collagen strings (#string) (9-72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.

Artificial intelligence scoring of liver biopsies in a phase II trial of semaglutide in nonalcoholic steatohepatitis

BACKGROUND AND AIMS

Artificial intelligence-powered digital pathology offers the potential to quantify histological findings in a reproducible way. This analysis compares the evaluation of histological features of NASH between pathologists and a machine-learning (ML) pathology model.

APPROACH AND RESULTS

This post hoc analysis included data from a subset of patients (n=251) with biopsy-confirmed NASH and fibrosis stage F1-F3 from a 72-week randomized placebo-controlled trial of once-daily subcutaneous semaglutide 0.1, 0.2, or 0.4 mg (NCT02970942). Biopsies at baseline and week 72 were read by 2 pathologists. Digitized biopsy slides were evaluated by PathAI's NASH ML models to quantify changes in fibrosis, steatosis, inflammation, and hepatocyte ballooning using categorical assessments and continuous scores. Pathologist and ML-derived categorical assessments detected a significantly greater percentage of patients achieving the primary endpoint of NASH resolution without worsening of fibrosis with semaglutide 0.4 mg versus placebo (pathologist 58.5% vs. 22.0%, p < 0.0001; ML 36.9% vs. 11.9%; p =0.0015). Both methods detected a higher but nonsignificant percentage of patients on semaglutide 0.4 mg versus placebo achieving the secondary endpoint of liver fibrosis improvement without NASH worsening. ML continuous scores detected significant treatment-induced responses in histological features, including a quantitative reduction in fibrosis with semaglutide 0.4 mg versus placebo ( p =0.0099) that could not be detected using pathologist or ML categorical assessment.

CONCLUSIONS

ML categorical assessments reproduced pathologists' results of histological improvement with semaglutide for steatosis and disease activity. ML-based continuous scores demonstrated an antifibrotic effect not measured by conventional histopathology.

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

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

New and emerging treatments for metabolic dysfunction-associated steatohepatitis

Metabolic dysfunction-associated steatohepatitis (MASH) is a leading etiology of chronic liver disease worldwide, with increasing incidence and prevalence in the setting of the obesity epidemic. MASH is also a leading indication for liver transplantation, given its associated risk of progression to end-stage liver disease. A key challenge in managing MASH is the lack of approved pharmacotherapy. In its absence, lifestyle interventions with a focus on healthy nutrition and regular physical activity have been the cornerstone of therapy. Real-world efficacy and sustainability of lifestyle interventions are low, however. Pharmacotherapy development for MASH is emerging with promising data from several agents with different mechanisms of action (MOAs) in phase 3 clinical trials. In this review, we highlight ongoing challenges and potential solutions in drug development for MASH and provide an overview of available data from emerging therapies across multiple MOAs.

Potential of Using qFibrosis Analysis to Predict Recurrent and Survival Outcome of Patients with Hepatocellular Carcinoma after Hepatic Resection

BACKGROUND

There remains a lack of studies addressing the stromal background and fibrosis features and their prognostic value in liver cancer. qFibrosis can identify, quantify, and visualize the fibrosis features in biopsy samples. In this study, we aim to demonstrate the prognostic value of histological features by using qFibrosis analysis in liver cancer patients.

METHODS

Liver specimens from 201 patients with hepatocellular carcinoma (HCC) who underwent curative resection were imaged and assessed using qFibrosis system and generated a total of 33 and 156 collagen parameters from tumor part and non-tumor liver tissue, respectively. We used these collagen parameters on patients to build two combined indexes, RFS index and OS index, in order to differentiate patients with early recurrence and early death, respectively. The models were validated using the leave-one-out method.

RESULTS

Both combined indexes had significant prediction value for patients' outcome. The RFS index of 0.52 well differentiates patients with early recurrence (p < 0.001), and the OS index of 0.73 well differentiates patients with early death during follow-up (p = 0.02).

CONCLUSIONS

Combined index calculated with qFibrosis from a digital readout of the fibrotic status of peri-tumor liver specimen in patients with HCC has prediction values for their disease and survival outcomes. These results demonstrated the potential to transform histopathological features into quantifiable data that could be used to correlate with clinical outcome.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features

Goal: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. Methods: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. Results: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). Conclusions: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

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.

Design of the phase 3 MAESTRO clinical program to evaluate resmetirom for the treatment of nonalcoholic steatohepatitis

BACKGROUND

Non-alcoholic steatohepatitis (NASH) is a progressive form of non-alcoholic fatty liver disease (NAFLD) associated with steatosis, hepatocellular injury, inflammation and fibrosis. In a Phase 2 trial in adults with NASH (NCT02912260), resmetirom, an orally administered, liver-targeted thyroid hormone receptor-β selective agonist, significantly reduced hepatic fat (via imaging) and resolved NASH without worsening fibrosis (via liver biopsy) in a significant number of patients compared with placebo.

AIMS

To present the design of the Phase 3 MAESTRO clinical programme evaluating resmetirom for treatment of NASH (MAESTRO-NAFLD-1 [NCT04197479], MAESTRO-NAFLD-OLE [NCT04951219], MAESTRO-NASH [NCT03900429], MAESTRO-NASH-OUTCOMES [NCT05500222]).

METHODS

MAESTRO-NASH is a pivotal serial biopsy trial in up to 2000 adults with biopsy-confirmed at-risk NASH. Patients are randomised to a once-daily oral placebo, 80 mg resmetirom, or 100 mg resmetirom. Liver biopsies are conducted at screening, week 52 and month 54. MAESTRO-NAFLD-1 is a 52-week safety trial in ~1400 adults with NAFLD/presumed NASH (based on non-invasive testing); ~700 patients from MAESTRO-NAFLD-1 are enrolled in MAESTRO-NAFLD-OLE, a 52-week active treatment extension to further evaluate safety. MAESTRO-NASH-OUTCOMES is enrolling 700 adults with well-compensated NASH cirrhosis to evaluate the potential for resmetirom to slow progression to hepatic decompensation events. Non-invasive tests (biomarkers, imaging) are assessed longitudinally throughout, in addition to validated patient-reported outcomes.

CONCLUSION

The MAESTRO clinical programme was designed in conjunction with regulatory authorities to support approval of resmetirom for treatment of NASH. The surrogate endpoints, based on week 52 liver biopsy, serum biomarkers and imaging, are confirmed by long-term clinical liver-related outcomes in MAESTRO-NASH (month 54) and MAESTRO-NASH-OUTCOMES (time to event).

Digital pathology for nonalcoholic steatohepatitis assessment

Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) - the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.

Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

AIMS

A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI).

METHODS

The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease.

RESULTS

Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists.

CONCLUSIONS

This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education.

Leukotriene B4 receptor 1 (BLT1) does not mediate disease progression in a mouse model of liver fibrosis

MASH is a prevalent liver disease that can progress to fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and ultimately death, but there are no approved therapies. Leukotriene B4 (LTB4) is a potent pro-inflammatory chemoattractant that drives macrophage and neutrophil chemotaxis, and genetic loss or inhibition of its high affinity receptor, leukotriene B4 receptor 1 (BLT1), results in improved insulin sensitivity and decreased hepatic steatosis. To validate the therapeutic efficacy of BLT1 inhibition in an inflammatory and pro-fibrotic mouse model of MASH and fibrosis, mice were challenged with a choline-deficient, L-amino acid defined high fat diet and treated with a BLT1 antagonist at 30 or 90 mg/kg for 8 weeks. Liver function, histology, and gene expression were evaluated at the end of the study. Treatment with the BLT1 antagonist significantly reduced plasma lipids and liver steatosis but had no impact on liver injury biomarkers or histological endpoints such as inflammation, ballooning, or fibrosis compared to control. Artificial intelligence-powered digital pathology analysis revealed a significant reduction in steatosis co-localized fibrosis in livers treated with the BLT1 antagonist. Liver RNA-seq and pathway analyses revealed significant changes in fatty acid, arachidonic acid, and eicosanoid metabolic pathways with BLT1 antagonist treatment, however, these changes were not sufficient to impact inflammation and fibrosis endpoints. Targeting this LTB4-BLT1 axis with a small molecule inhibitor in animal models of chronic liver disease should be considered with caution, and additional studies are warranted to understand the mechanistic nuances of BLT1 inhibition in the context of MASH and liver fibrosis.

Clinical trial: Effects of pegozafermin on the liver and on metabolic comorbidities in subjects with biopsy-confirmed nonalcoholic steatohepatitis

BACKGROUND

An approved therapy for nonalcoholic steatohepatitis (NASH) and fibrosis remains a major unmet medical need.

AIM

To investigate the histological and metabolic benefits of pegozafermin, a glycoPEGylated FGF21 analogue, in subjects with biopsy-confirmed NASH.

METHODS

This proof-of-concept, open-label, single-cohort study, part 2 of a phase 1b/2a clinical trial, was conducted at 16 centres in the United States. Adults (age 21-75 years) with NASH (stage 2 or 3 fibrosis, NAS≥4) and magnetic resonance imaging proton density fat fraction (MRI-PDFF) ≥8% received subcutaneous pegozafermin 27 mg once weekly for 20 weeks. Primary outcomes were improvements in liver histology, and safety and tolerability.

RESULTS

Of 20 enrolled subjects, 19 completed the study. Twelve subjects (63%) met the primary endpoint of ≥2-point improvement in NAFLD activity score with ≥1-point improvement in ballooning or lobular inflammation and no worsening of fibrosis. Improvement of fibrosis without worsening of NASH was observed in 26% of subjects, and NASH resolution without worsening of fibrosis in 32%. Least-squares mean relative change from baseline in MRI-PDFF was -64.7% (95% CI: -71.7, -57.7; p < 0.0001). Significant improvements from baseline were also seen in serum aminotransferases, noninvasive fibrosis tests, serum lipids, glycaemic control and body weight. Adverse events (AEs) were reported in 18 subjects (90%). The most frequently reported AEs were mild/moderate nausea and diarrhoea. There were no serious AEs, discontinuations due to AEs, or deaths.

CONCLUSIONS

Pegozafermin treatment for 20 weeks had beneficial effects on hepatic and metabolic parameters and was well tolerated in subjects with NASH.

CLINICALTRIALS

gov: NCT04048135.

Tropifexor plus cenicriviroc combination versus monotherapy in nonalcoholic steatohepatitis: Results from the phase 2b TANDEM study

BACKGROUND AND AIMS

With distinct mechanisms of action, the combination of tropifexor (TXR) and cenicriviroc (CVC) may provide an effective treatment for NASH. This randomized, multicenter, double-blind, phase 2b study assessed the safety and efficacy of TXR and CVC combination, compared with respective monotherapies.

APPROACH AND RESULTS

Patients (N = 193) were randomized 1:1:1:1 to once-daily TXR 140 μg (TXR 140 ), CVC 150 mg (CVC), TXR 140 μg + CVC 150 mg (TXR 140 + CVC), or TXR 90 μg + CVC 150 mg (TXR 90 + CVC) for 48 weeks. The primary and secondary end points were safety and histological improvement, respectively. Rates of adverse events (AEs) were similar across treatment groups. Pruritus was the most frequently experienced AE, with highest incidence in the TXR 140 group (40.0%). In TXR and combination groups, alanine aminotransferase (ALT) decreased from baseline to 48 weeks (geometric mean change: -21%, TXR 140 ; -16%, TXR 140 + CVC; -13%, TXR 90 + CVC; and +17%, CVC). Reductions in body weight observed at week 24 (mean changes from baseline: TXR 140 , -2.5 kg; TXR 140 + CVC, -1.7 kg; TXR 90 + CVC, -1.0 kg; and CVC, -0.1 kg) were sustained to week 48. At least 1-point improvement in fibrosis stage/steatohepatitis resolution without worsening of fibrosis was observed in 32.3%/25.8%, 31.6%/15.8%, 29.7%/13.5%, and 32.5%/22.5% of patients in the TXR 140 , CVC, TXR 140 + CVC, and TXR 90 + CVC groups, respectively.

CONCLUSIONS

The safety profile of TXR + CVC combination was similar to respective monotherapies, with no new signals. TXR monotherapy showed sustained ALT and body weight decreases. No substantial incremental efficacy was observed with TXR + CVC combination on ALT, body weight, or in histological end points compared with monotherapy.

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD

BACKGROUND

Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment.

AIM

To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.

METHODS

AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.

RESULTS

AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.

CONCLUSION

AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.

Recent advances in fibrosis assessment for metabolic dysfunction-associated fatty liver disease

This article is linked to Zhan et al papers. To view these articles, visit https://doi.org/10.1111/apt.17635 and https://doi.org/10.1111/apt.17660

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets.

METHODS

The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes.

RESULTS

Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD.

CONCLUSION

we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD.

Collagen co-localized with macrovesicular steatosis better differentiates fibrosis progression in non-alcoholic fatty liver disease mouse models

Background

Non-alcoholic fatty liver disease (NAFLD) is a global commonly occurring liver disease. However, its exact pathogenesis is not fully understood. The purpose of this study was to quantitatively evaluate the progression of steatosis and fibrosis by examining their distribution, morphology, and co-localization in NAFLD animal models.

Methods

Six mouse NAFLD groups were established: (1) western diet (WD) group; (2) WD with fructose in drinking water (WDF) group; (3) WDF + carbon tetrachloride (CCl4) group, WDF plus intraperitoneal injection of CCl4; (4) high-fat diet (HFD) group, (5) HFD with fructose (HFDF) group; and (6) HFDF + CCl4 group, HFDF plus intraperitoneal injection of CCl4. Liver tissue specimens from NAFLD model mice were collected at different time points. All the tissues were serially sectioned for histological staining and second-harmonic generation (SHG)/two-photon excitation fluorescence imaging (TPEF) imaging. The progression of steatosis and fibrosis was analyzed using SHG/TPEF quantitative parameters with respect to the non-alcoholic steatohepatitis Clinical Research Network scoring system.

Results

qSteatosis showed a good correlation with steatosis grade (R: 0.823-0.953, p < 0.05) and demonstrated high performance (area under the curve [AUC]: 0.617-1) in six mouse models. Based on their high correlation with histological scoring, qFibrosis containing four shared parameters (#LongStrPS, #ThinStrPS, #ThinStrPSAgg, and #LongStrPSDis) were selected to create a linear model that could accurately identify differences among fibrosis stages (AUC: 0.725-1). qFibrosis co-localized with macrosteatosis generally correlated better with histological scoring and had a higher AUC in six animal models (AUC: 0.846-1).

Conclusion

Quantitative assessment using SHG/TPEF technology can be used to monitor different types of steatosis and fibrosis progression in NAFLD models. The collagen co-localized with macrosteatosis could better differentiate fibrosis progression and might aid in developing a more reliable and translatable fibrosis evaluation tool for animal models of NAFLD.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

Zonated quantification of immunohistochemistry in normal and steatotic livers

Immunohistochemical stains (IHC) reveal differences between liver lobule zones in health and disease, including nonalcoholic fatty liver disease (NAFLD). However, such differences are difficult to accurately quantify. In NAFLD, the presence of lipid vacuoles from macrovesicular steatosis further hampers interpretation by pathologists. To resolve this, we applied a zonal image analysis method to measure the distribution of hypoxia markers in the liver lobule of steatotic livers.The hypoxia marker pimonidazole was assessed with IHC in the livers of male C57BL/6 J mice on standard diet or choline-deficient L-amino acid-defined high-fat diet mimicking NAFLD. Another hypoxia marker, carbonic anhydrase IX, was evaluated by IHC in human liver tissue. Liver lobules were reconstructed in whole slide images, and staining positivity was quantified in different zones in hundreds of liver lobules. This method was able to quantify the physiological oxygen gradient along hepatic sinusoids in normal livers and panlobular spread of the hypoxia in NAFLD and to overcome the pronounced impact of macrovesicular steatosis on IHC. In a proof-of-concept study with an assessment of the parenchyma between centrilobular veins in human liver biopsies, carbonic anhydrase IX could be quantified correctly as well.The method of zonated quantification of IHC objectively quantifies the difference in zonal distribution of hypoxia markers (used as an example) between normal and NAFLD livers both in whole liver as well as in liver biopsy specimens. It constitutes a tool for liver pathologists to support visual interpretation and estimate the impact of steatosis on IHC results.

Hepatocytic ballooning in non-alcoholic steatohepatitis: Dilemmas and future directions

Hepatocytic ballooning is a key histological feature in the diagnosis of non-alcoholic steatohepatitis (NASH) and is an essential component of the two most widely used histological scoring systems for diagnosing and staging non-alcoholic fatty liver disease (NAFLD) [namely, the NAFLD activity score (NAS), and the steatosis, activity and fibrosis (SAF) scoring system]. As a result of the increasing incidence of NASH globally, the diagnostic challenges of hepatocytic ballooning are unprecedented. Despite the clear pathological concept of hepatocytic ballooning, there are still challenges in assessing hepatocytic ballooning in 'real life' situations. Hepatocytic ballooning can be confused with cellular oedema and microvesicular steatosis. Significant inter-observer variability does exist in assessing the presence and severity of hepatocytic ballooning. In this review article, we describe the underlying mechanisms associated with hepatocytic ballooning. Specifically, we discuss the increased endoplasmic reticulum stress and the unfolded protein response, as well as the rearrangement of the intermediate filament cytoskeleton, the appearance of Mallory-Denk bodies and activation of the sonic Hedgehog pathway. We also discuss the use of artificial intelligence in the detection and interpretation of hepatocytic ballooning, which may provide new possibilities for future diagnosis and treatment.

Automated Segmentation and Morphological Characterization of Hepatic Steatosis and Correlation with Histopathology

BACKGROUND/OBJECTIVES

Prevalence of nonalcoholic fatty liver disease (NAFLD) has increased to 25% of the world population. Hepatic steatosis is a hallmark feature of NAFLD and is assessed histologically using visual and ordinal fat grading criteria (0-3) from the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network (CRN) scoring system. The purpose of this study is to automatically segment and extract morphological characteristics and distributions of fat droplets (FDs) on liver histology images and find associations with severity of steatosis.

METHODS

A previously published human cohort of 68 NASH candidates was graded for steatosis by an experienced pathologist using the Fat CRN grading system. The automated segmentation algorithm quantified fat fraction (FF) and fat-affected hepatocyte ratio (FHR), extracted fat morphology by calculating radius and circularity of FDs, and examined FDs distribution and heterogeneity using nearest neighbor distance and regional isotropy.

RESULTS

Regression analysis and Spearman correlation (ρ) yielded high correlations for radius (R2 = 0.86, ρ = 0.72), nearest neighbor distance (R2 = 0.82, ρ = -0.82), regional isotropy (R2 = 0.84, ρ = 0.74), and FHR (R2 = 0.90, ρ = 0.85), and low correlation for circularity (R2 = 0.48, ρ = -0.32) with FF and pathologist grades, respectively. FHR showed a better distinction between pathologist Fat CRN grades compared to conventional FF measurements, making it a potential surrogate measure for Fat CRN scores. Our results showed variation in distribution of morphological features and steatosis heterogeneity within the same patient's biopsy sample as well as between patients of similar FF.

CONCLUSIONS

The fat percentage measurements, specific morphological characteristics, and patterns of distribution quantified with the automated segmentation algorithm showed associations with steatosis severity; however, future studies are warranted to evaluate the clinical significance of these steatosis features in progression of NAFLD and NASH.

Machine learning liver histology scores correlate with portal hypertension assessments in nonalcoholic steatohepatitis cirrhosis

BACKGROUND AND AIMS

In cirrhotic nonalcoholic steatohepatitis (NASH) clinical trials, primary efficacy endpoints have been hepatic venous pressure gradient (HVPG), liver histology and clinical liver outcomes. Important histologic features, such as septa thickness, nodules features and fibrosis area have not been included in the histologic assessment and may have important clinical relevance. We assessed these features with a machine learning (ML) model.

METHODS

NASH patients with compensated cirrhosis and HVPG ≥6 mm Hg (n = 143) from the Belapectin phase 2b trial were studied. Liver biopsies, HVPG measurements and upper endoscopies were performed at baseline and at end of treatment (EOT). A second harmonic generation/two-photon excitation fluorescence provided an automated quantitative assessment of septa, nodules and fibrosis (SNOF). We created ML scores and tested their association with HVPG, clinically significant HVPG (≥10 mm Hg) and the presence of varices (SNOF-V).

RESULTS

We derived 448 histologic variables (243 related to septa, 21 related to nodules and 184 related to fibrosis). The SNOF score (≥11.78) reliably distinguished CSPH at baseline and in the validation cohort (baseline + EOT) [AUC = 0.85 and 0.74, respectively]. The SNOF-V score (≥0.57) distinguished the presence of varices at baseline and in the same validation cohort [AUC = 0.86 and 0.73, respectively]. Finally, the SNOF-C score differentiated those who had >20% change in HVPG against those who did not, with an AUROC of 0.89.

CONCLUSION

The ML algorithm accurately predicted HVPG, CSPH, the development of varices and HVPG changes in patients with NASH cirrhosis. The use of ML histology model in NASH cirrhosis trials may improve the assessment of key outcome changes.

Non-alcoholic fatty liver disease: the pathologist's perspective

Non-alcoholic fatty liver disease (NAFLD) is a spectrum of diseases characterized by fatty accumulation in hepatocytes, ranging from steatosis, non-alcoholic steatohepatitis, to cirrhosis. While histopathological evaluation of liver biopsies plays a central role in the diagnosis of NAFLD, limitations such as the problem of interobserver variability still exist and active research is underway to improve the diagnostic utility of liver biopsies. In this article, we provide a comprehensive overview of the histopathological features of NAFLD, the current grading and staging systems, and discuss the present and future roles of liver biopsies in the diagnosis and prognostication of NAFLD.

Non-invasive imaging biomarkers for liver steatosis in non-alcoholic fatty liver disease: present and future

Non-alcoholic fatty liver disease is currently the most common chronic liver disease, affecting up to 25% of the global population. Simple fatty liver, in which fat is deposited in the liver without fibrosis, has been regarded as a benign disease in the past, but it is now known to be prognostic. In the future, more emphasis should be placed on the quantification of liver fat. Traditionally, fatty liver has been assessed by histological evaluation, which requires an invasive examination; however, technological innovations have made it possible to evaluate fatty liver by non-invasive imaging methods, such as ultrasonography, computed tomography, and magnetic resonance imaging. In addition, quantitative as well as qualitative measurements for the detection of fatty liver have become available. In this review, we summarize the currently used qualitative evaluations of fatty liver and discuss quantitative evaluations that are expected to further develop in the future.

Tropifexor for nonalcoholic steatohepatitis: an adaptive, randomized, placebo-controlled phase 2a/b trial

The multimodal activities of farnesoid X receptor (FXR) agonists make this class an attractive option to treat nonalcoholic steatohepatitis. The safety and efficacy of tropifexor, an FXR agonist, in a randomized, multicenter, double-blind, three-part adaptive design, phase 2 study, in patients with nonalcoholic steatohepatitis were therefore assessed. In Parts A + B, 198 patients were randomized to receive tropifexor (10-90 μg) or placebo for 12 weeks. In Part C, 152 patients were randomized to receive tropifexor 140 µg, tropifexor 200 µg or placebo (1:1:1) for 48 weeks. The primary endpoints were safety and tolerability to end-of-study, and dose response on alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hepatic fat fraction (HFF) at week 12. Pruritus was the most common adverse event in all groups, with a higher frequency in the 140- and 200-µg tropifexor groups. Decreases from baseline in ALT and HFF were greater with tropifexor versus placebo at week 12, with a relative decrease in least squares mean from baseline observed with all tropifexor doses for ALT (tropifexor 10-90-μg dose groups ranged from -10.7 to -16.5 U l-1 versus placebo (-7.8 U l-1) and tropifexor 140- and 200-μg groups were -18.0 U l-1 and -23.0 U l-1, respectively, versus placebo (-8.3 U l-1)) and % HFF (tropifexor 10-90-μg dose groups ranged from -7.48% to -15.04% versus placebo (-6.19%) and tropifexor 140- and 200-μg groups were -19.07% and -39.41%, respectively, versus placebo (-10.77%)). Decreases in ALT and HFF were sustained up to week 48; however, similar trends in AST with tropifexor at week 12 were not observed. As with other FXR agonists, dose-related pruritus was frequently observed. Clinicaltrials.gov registration: NCT02855164.

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.

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.