Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH

Assessing the diagnostic accuracy of ChatGPT-4 in the histopathological evaluation of liver fibrosis in MASH

BACKGROUND

Large language models like ChatGPT have demonstrated potential in medical image interpretation, but their efficacy in liver histopathological analysis remains largely unexplored. This study aims to assess ChatGPT-4-vision's diagnostic accuracy, compared to liver pathologists' performance, in evaluating liver fibrosis (stage) in metabolic dysfunction-associated steatohepatitis.

METHODS

Digitized Sirius Red-stained images for 59 metabolic dysfunction-associated steatohepatitis tissue biopsy specimens were evaluated by ChatGPT-4 and 4 pathologists using the NASH-CRN staging system. Fields of view at increasing magnification levels, extracted by a senior pathologist or randomly selected, were shown to ChatGPT-4, asking for fibrosis staging. The diagnostic accuracy of ChatGPT-4 was compared with pathologists' evaluations and correlated to the collagen proportionate area for additional insights. All cases were further analyzed by an in-context learning approach, where the model learns from exemplary images provided during prompting.

RESULTS

ChatGPT-4's diagnostic accuracy was 81% when using images selected by a pathologist, while it decreased to 54% with randomly cropped fields of view. By employing an in-context learning approach, the accuracy increased to 88% and 77% for selected and random fields of view, respectively. This method enabled the model to fully and correctly identify the tissue structures characteristic of F4 stages, previously misclassified. The study also highlighted a moderate to strong correlation between ChatGPT-4's fibrosis staging and collagen proportionate area.

CONCLUSIONS

ChatGPT-4 showed remarkable results with a diagnostic accuracy overlapping those of expert liver pathologists. The in-context learning analysis, applied here for the first time to assess fibrosis deposition in metabolic dysfunction-associated steatohepatitis samples, was crucial in accurately identifying the key features of F4 cases, critical for early therapeutic decision-making. These findings suggest the potential for integrating large language models as supportive tools in diagnostic pathology.

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.

Digital Pathology Tailored for Assessment of Liver Biopsies

Improved image quality, better scanners, innovative software technologies, enhanced computational power, superior network connectivity, and the ease of virtual image reproduction and distribution are driving the potential use of digital pathology for diagnosis and education. Although relatively common in clinical oncology, its application in liver pathology is under development. Digital pathology and improving subjective histologic scoring systems could be essential in managing obesity-associated steatotic liver disease. The increasing use of digital pathology in analyzing liver specimens is particularly intriguing as it may offer a more detailed view of liver biology and eliminate the incomplete measurement of treatment responses in clinical trials. The objective and automated quantification of histological results may help establish standardized diagnosis, treatment, and assessment protocols, providing a foundation for personalized patient care. Our experience with artificial intelligence (AI)-based software enhances reproducibility and accuracy, enabling continuous scoring and detecting subtle changes that indicate disease progression or regression. Ongoing validation highlights the need for collaboration between pathologists and AI developers. Concurrently, automated image analysis can address issues related to the historical failure of clinical trials stemming from challenges in histologic assessment. We discuss how these novel tools can be incorporated into liver research and complement post-diagnosis scenarios where quantification is necessary, thus clarifying the evolving role of digital pathology in the field.

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.

Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as "hallucinations". This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations.

Insights into Clinical Trials for Drugs Targeting MASLD: Progress, Challenges, and Future Directions

The transition in terminology from fatty liver disease to metabolic dysfunction-associated steatotic liver disease (MASLD) marks a considerable evolution in diagnostic standards. This new definition focuses on liver fat accumulation in the context of overweight/obesity, type 2 diabetes, or metabolic dysfunction, without requiring the exclusion of other concurrent liver diseases. The new definition also provides clear guidelines for defining alcohol consumption in relation to the disease. MASLD is currently acknowledged as the most widespread liver disorder globally, affecting ~25% of the population. Despite the extensive array of clinical trials conducted in recent years, the number of approved treatments for metabolic dysfunction-associated fatty liver disease is very limited. In the review critically evaluates the results of clinical trials of related drugs and assesses the future directions for drug development trials. The renaming of MASLD presents new challenges and opportunities for the design of clinical trials and the selection of target populations for drug development.

A phase IIb randomised-controlled trial of the FFAR1/FFAR4 agonist icosabutate in MASH

BACKGROUND & AIMS

Via regulation of glycaemic control and inflammation, free fatty receptor (FFAR)1 and 4 are potential targets for the treatment of metabolic dysfunction-associated steatohepatitis (MASH). In this study, we tested the efficacy and safety of icosabutate, a FFAR1/FFAR4 agonist, in patients with MASH.

METHODS

We performed a phase IIb, multicentre, 52-week, randomised, placebo-controlled trial (ICONA) testing the efficacy of icosabutate in patients with MASH and F1-F3 (mild to severe) fibrosis. Patients were randomised 1:1:1 to receive once-daily, oral icosabutate 300 mg, 600 mg or placebo for 52 weeks. The primary efficacy endpoint was the proportion of patients with MASH resolution with no worsening of fibrosis in the 600 mg arm.

RESULTS

The primary population for efficacy analysis comprised 187 patients (placebo [n = 62], 300 mg icosabutate [n = 58] or 600 mg icosabutate [n = 67]). The percentage of patients with MASH resolution favoured the icosabutate 600 mg arm without reaching statistical significance (23.9% vs. 14.5%; odds ratio 2.01; 95% CI 0.8-5.08; p = 0.13). A higher percentage of patients treated with icosabutate achieved a ≥1-stage improvement in fibrosis, with a response rate of 29.3% in the 300 mg arm (odds ratio 2.89; 95% CI 1.09-7.70) and 23.9% in the 600 mg arm (odds ratio 2.4; 95% CI 0.90-6.37) vs. 11.3% in the placebo arm. An improvement in fibrosis was observed using AI-assisted digital pathology. Marked decreases in biomarkers of liver damage were observed. Icosabutate was generally safe and well tolerated, with mild to moderate treatment-emergent adverse events and no reports of drug-induced liver injury.

CONCLUSION

Although the primary endpoint was not met, treatment with icosabutate was associated with encouraging fibrosis (as measured by both conventional and AI-assisted digital pathology) and non-invasive biomarker data, supporting further development in patients with MASH.

CLINICALTRIALS

GOV IDENTIFIER

NCT04052516.

IMPACT AND IMPLICATIONS

With expression on multiple cell types regulating both glycaemic control and liver inflammation, targeting free fatty acid receptors (FFAR)1 and 4 could offer an attractive approach for the treatment of both fibrosing metabolic dysfunction-assoicated steatohepatitis (MASH) and its comorbidities. Although treatment of patients with MASH and F1-F3 fibrosis with oral icosabutate (a FFAR1/FFAR4 agonist) did not meet the pre-defined primary endpoint (MASH resolution without worsening of fibrosis), the overall dataset (including AI-assisted digital pathology) suggest an improvement in fibrosis in treated patients. Improvements in multiple biomarkers of liver damage, inflammation and glycaemic control were observed in response to therapy. Icosabutate was generally safe and well tolerated, and the overall data support further testing of icosabutate in patients with MASH, in particular those with more advanced disease (F2-F3 fibrosis) and type 2 diabetes.

Loss of mitochondrial amidoxime-reducing component 1 (mARC1) prevents disease progression by reducing fibrosis in multiple mouse models of chronic liver disease

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease is a prevalent disease that affects nearly one-third of the global population. Recent genome-wide association studies revealed that a common missense variant in the gene encoding mitochondrial amidoxime reducing component 1 (mARC1) is associated with protection from metabolic dysfunction-associated steatotic liver disease, all-cause cirrhosis, and liver-related mortality suggesting a role for mARC1 in liver pathophysiology; however, little is known about its function in the liver. In this study, we aimed to evaluate the impact of mARC1 hepatoprotective variants on protein function, the effect of loss of mARC1 on cellular lipotoxic stress response, and the effect of global or hepatocyte-specific loss of mARC1 in various mouse models of metabolic dysfunction-associated steatohepatitis and liver fibrosis.

METHODS AND RESULTS

Expression and characterization of mARC1 hepatoprotective variants in cells and mouse liver revealed that the mARC1 p.A165T exhibited lower protein levels but maintained its mitochondrial localization. In cells, the knockdown of mARC1 improved cellular bioenergetics and decreased mitochondrial superoxide production in response to lipotoxic stress. Global genetic deletion and hepatocyte-specific knockdown of mARC1 in mice significantly reduced liver steatosis and fibrosis in multiple mouse models of metabolic dysfunction-associated steatohepatitis and liver fibrosis. Furthermore, RNA-seq analysis revealed that the pathways involved in extracellular matrix remodeling and collagen formation were downregulated in the liver, and the plasma lipidome was significantly altered in response to the loss of mARC1 in mice.

CONCLUSIONS

Overall, we have demonstrated that loss of mARC1 alters hepatocyte response to lipotoxic stress and protects mice from diet-induced MASH and liver fibrosis consistent with findings from human genetics.

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.

Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease

BACKGROUND

Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize that tissue-based analysis of liver fibrosis by second harmonic generation (SHG) microscopy with artificial intelligence analysis can yield prognostic biomarkers in AILD.

METHODS

Patients from single-center studies with unstained slides from clinically obtained liver biopsies at AILD diagnosis were identified. Baseline demographics and liver biochemistries at diagnosis and 1 year were collected. Clinical endpoints studied included the presence of varices, variceal bleeding, ascites, HE, and liver transplant. In collaboration with HistoIndex, unstained slides underwent SHG/artificial intelligence analysis to map fibrosis according to 10 quantitative fibrosis parameters based on tissue location, including total, periportal, perisinusoidal, and pericentral area and length of strings.

RESULTS

Sixty-three patients with AIH (51%), primary sclerosing cholangitis (30%), or autoimmune sclerosing cholangitis (19%) at a median of 14 years old (range: 3-24) were included. An unsupervised analysis of quantitative fibrosis parameters representing total and portal fibrosis identified a patient cluster with more primary sclerosing cholangitis/autoimmune sclerosing cholangitis. This group had more fibrosis at diagnosis by METAVIR classification of histopathological review of biopsies (2.5 vs. 2; p = 0.006). This quantitative fibrosis pattern also predicted abnormal 12-month ALT with an OR of 3.6 (1.3-10, p = 0.014), liver complications with an HR of 3.2 (1.3-7.9, p = 0.01), and liver transplantation with an HR of 20.1 (3-135.7, p = 0.002).

CONCLUSIONS

The application of SHG/artificial intelligence algorithms in pediatric-onset AILD provides improved insight into liver histopathology through fibrosis mapping. SHG allows objective identification of patients with biliary tract involvement, which may be associated with a higher risk for refractory disease.

Extracellular Vesicles from Mesenchymal Stem Cells: Potential as Therapeutics in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by the accumulation of triglycerides within hepatocytes, which can progress to more severe conditions, such as metabolic dysfunction-associated steatohepatitis (MASH), which may include progressive fibrosis, leading to cirrhosis, cancer, and death. This goal of this review is to highlight recent research showing the potential of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) in reducing the key pathogenic pathways of MASLD or MASH. Methods: Relevant published studies were identified using PubMed with one or more of the following search terms: MASLD, MASH, NAFLD, NASH, exosome, extracellular vesicle (EV), therapy, and/or mesenchymal stem cells (MSC). The primary literature were subsequently downloaded and summarized. Results: Using in vitro or in vivo models, MSC-EVs have been found to counteract oxidative stress, a significant contributor to liver injury in MASH, and to suppress disease progression, including steatosis, inflammation, and, in a few instances, fibrosis. Some of these outcomes have been attributed to specific EV cargo components including microRNAs and proteins. Thus, MSC-EVs enriched with these types of molecules may have improved the therapeutic efficacy for MASLD/MASH and represent a novel approach to potentially halt or reverse the disease process. Conclusions: MSC-EVs are attractive therapeutic agents for treating MASLD/MASH. Further studies are necessary to validate the clinical applicability and efficacy of MSC-EVs in human MASH patients, focusing on optimizing delivery strategies and identifying the pathogenic pathways that are targeted by specific EV components.

Predicting and managing liver fibrosis in biliary atresia

Regardless of the underlying etiology and success of PE, progressive liver fibrosis and eventually cirrhosis represent the dominant pathology and the end-stage of BA. Ascending bile duct injury-induced cholestasis, inflammation and ductular reaction provide profibrogenic cytokine environment leading to myofibroblast activation and rapid progression of fibrosis especially after unsuccessful portoenterostomy. Although liver fibrosis and development of cirrhosis play a crucial role in determining BA outcomes, the exact prognostic significance and dynamics of mild to moderate liver fibrosis remain unclear. Manual scoring systems categorizing the degree of liver fibrosis are prone to intra- and interobserver variability, whereas novel combinations of digital pathology with artificial intelligence quantification can provide accurate information on fibrosis structure and dynamics at the level of individual collagen fibers. Although several studies have analyzed noninvasive assessment of fibrosis at time of PE, including imaging-based elastography and different serum biomarkers, current knowledge on their accuracy during the postoperative follow-up of BA is scarce. While therapeutic management of liver fibrosis in BA remains in its infancy, the resolution potential for liver fibrosis has been demonstrated after successful PE. Achievement of effective antifibrotic treatment may require combination of different therapies with complementary modes of action like anti-inflammatory medication, antioxidants and bile acid lowering agents.

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.

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.

Clinical care guidance in patients with diabetes and metabolic dysfunction-associated steatotic liver disease: A joint consensus

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide, affecting >30% of the global population. Metabolic dysregulation, particularly insulin resistance and its subsequent manifestation as type 2 diabetes mellitus, serves as the fundamental pathogenesis of metabolic liver disease. Clinical evidence of the recent nomenclature evolution is accumulating. The interaction and impacts are bidirectional between MASLD and diabetes in terms of disease course, risk, and prognosis. Therefore, there is an urgent need to highlight the multifaceted links between MASLD and diabetes for both hepatologists and diabetologists. The surveillance strategy, risk stratification of management, and current therapeutic achievements of metabolic liver disease remain the major pillars in a clinical care setting. Therefore, the Taiwan Association for the Study of the Liver (TASL), Taiwanese Association of Diabetes Educators, and Diabetes Association of the Republic of China (Taiwan) collaboratively completed the first guidance in patients with diabetes and MASLD, which provides practical recommendations for patient care.

Current challenges and future perspectives in treating patients with NAFLD-related cirrhosis

Despite the slow, progressive nature of NAFLD, the number of patients with NAFLD-related cirrhosis has significantly increased. Although the management of patients with cirrhosis is constantly evolving, improving the prognosis of patients with NAFLD-related cirrhosis is a challenge because it is situated at the crossroads between the liver, the metabolic, and the cardiovascular diseases. Therefore, the therapeutic interventions should not only target the liver but also the associated cardiometabolic conditions and should be adapted accordingly. The objective of the current review is to critically discuss the particularities in the management of patients with NAFLD-related cirrhosis. We relied on the recommendations of scientific societies and discussed them in the specific context of NAFLD cirrhosis and the surrounding cardiometabolic milieu. Herein, we covered the following aspects: (1) the weight loss strategies through lifestyle interventions to avoid sarcopenia and improve portal hypertension; (2) the optimal control of metabolic comorbidities in particular type 2 diabetes aimed not only to improve cardiovascular morbidity/mortality but also to lower the incidence of cirrhosis-related complications (we discussed various aspects related to the safety of oral antidiabetic drugs in cirrhosis); (3) the challenges in performing bariatric surgery in patients with cirrhosis related to the portal hypertension and the risk of cirrhosis decompensation; (4) the particularities in the diagnosis and management of the portal hypertension and the difficulties in managing patients awaiting for liver transplantation; and (5) the difficulties in developing drugs and conducting clinical trials in patients with NAFLD-related cirrhosis. Moreover, we discussed the emerging options to overcome these obstacles.

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.

AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study

BACKGROUND AND AIMS

Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH.

METHODS

We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions.

RESULTS

About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region.

CONCLUSION

Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

Envisioning how to advance the MASH field

Since 1980, the cumulative effort of scientists and health-care stakeholders has advanced the prerequisites to address metabolic dysfunction-associated steatotic liver disease (MASLD), a prevalent chronic non-communicable liver disease. This effort has led to, among others, the approval of the first drug specific for metabolic dysfunction-associated steatohepatitis (MASH; formerly known as nonalcoholic steatohepatitis). Despite substantial progress, MASLD is still a leading cause of advanced chronic liver disease, including primary liver cancer. This Perspective contextualizes the nomenclature change from nonalcoholic fatty liver disease to MASLD and proposes important considerations to accelerate further progress in the field, optimize patient-centric multidisciplinary care pathways, advance pharmacological, behavioural and diagnostic research, and address health disparities. Key regulatory and other steps necessary to optimize the approval and access to upcoming additional pharmacological therapeutic agents for MASH are also outlined. We conclude by calling for increased education and awareness, enhanced health system preparedness, and concerted action by policy-makers to further the public health and policy agenda to achieve at least parity with other non-communicable diseases and to aid in growing the community of practice to reduce the human and economic burden and end the public health threat of MASLD and MASH by 2030.

Outcome prediction in metabolic dysfunction-associated steatotic liver disease using stain-free digital pathological assessment

Computational quantification reduces observer-related variability in histological assessment of metabolic dysfunction-associated steatotic liver disease (MASLD). We undertook stain-free imaging using the SteatoSITE resource to generate tools directly predictive of clinical outcomes. Unstained liver biopsy sections (n = 452) were imaged using second-harmonic generation/two-photon excitation fluorescence (TPEF) microscopy, and all-cause mortality and hepatic decompensation indices constructed. The mortality index had greater predictive power for all-cause mortality (index >.14 vs. .31 vs. Collapse

Key Words

• computer‐assisted
• cox model
• image processing
• metabolic dysfunction‐associated steatotic liver disease
• non‐alcoholic fatty liver disease
• pathology

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MESH Headings

• Humans
• Fatty Liver/pathology
• Fatty Liver/diagnostic imaging
• Liver/pathology
• Liver/diagnostic imaging
• Male
• Female
• Biopsy
• Predictive Value of Tests
• Middle Aged
• Microscopy, Fluorescence, Multiphoton/methods
• Non-alcoholic Fatty Liver Disease/pathology
• Non-alcoholic Fatty Liver Disease/complications
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Prognosis

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Grants

• Innovate UK

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Affiliation(s)

Timothy J Kendall Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK Edinburgh Pathology, University of Edinburgh, Edinburgh, UK
Elaine Chng HistoIndex Pte Ltd, Singapore, Singapore
Yayun Ren HistoIndex Pte Ltd, Singapore, Singapore
Dean Tai HistoIndex Pte Ltd, Singapore, Singapore
Gideon Ho HistoIndex Pte Ltd, Singapore, Singapore
Jonathan A Fallowfield Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK

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AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases

Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impacted clinical trial outcomes. We developed an artificial intelligence-based measurement (AIM) tool for scoring MASH histology (AIM-MASH). AIM-MASH predictions for MASH Clinical Research Network necroinflammation grades and fibrosis stages were reproducible (κ = 1) and aligned with expert pathologist consensus scores (κ = 0.62-0.74). The AIM-MASH versus consensus agreements were comparable to average pathologists for MASH Clinical Research Network scores (82% versus 81%) and fibrosis (97% versus 96%). Continuous scores produced by AIM-MASH for key histological features of MASH correlated with mean pathologist scores and noninvasive biomarkers and strongly predicted progression-free survival in patients with stage 3 (P < 0.0001) and stage 4 (P = 0.03) fibrosis. In a retrospective analysis of the ATLAS trial (NCT03449446), responders receiving study treatment showed a greater continuous change in fibrosis compared with placebo (P = 0.02). Overall, these results suggest that AIM-MASH may assist pathologists in histologic review of MASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient responses.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

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.

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.

Immunolocalization of Matrix Metalloproteinases 2 and 9 and Their Inhibitors in the Hearts of Rats Treated with Immunosuppressive Drugs-An Artificial Intelligence-Based Digital Analysis

BACKGROUND

Immunosuppressive agents represent a broad group of drugs, such as calcineurin inhibitors, mTOR inhibitors, and glucocorticosteroids, among others. These drugs are widely used in a number of conditions, but lifelong therapy is crucial in the case of organ recipients to prevent rejection. To further increase the safety and efficacy of these agents, their off-target mechanisms of action, as well as processes underlying the pathogenesis of adverse effects, need to be thoroughly investigated. The aim of this study was to examine the impact of various combinations of cyclosporine/tacrolimus/mycophenolate with rapamycin and steroids (CRG, TRG, MRG), on the morphology and morphometry of rats' cardiomyocytes, together with the presence of cardiac collagen and the immunoexpression of MMPs and TIMPs.

METHODS

Twenty-four rats were divided into four groups receiving different immunosuppressive regiments. After six months of treatment, the hearts were collected and analyzed.

RESULTS

Cardiomyocytes from the CRG cohorts demonstrated the most pronounced morphological alterations. In addition, chronic immunosuppression reduced the width and length of cardiac cells. However, immunosuppressive therapy did not alter the presence of cardiac collagen fibers. Nevertheless, we observed significant alterations regarding MMP/TIMP homeostasis.

CONCLUSIONS

Chronic immunosuppression seems to disturb the MMP/TIMP balance in aspects of immunolocalization in the hearts of rats. Further studies are required to analyze other mechanisms and pathways affected by the use of immunosuppressants.

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.

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.

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.

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.

An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology

Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.

Recompensation in cirrhosis: unravelling the evolving natural history of nonalcoholic fatty liver disease

Recompensation has gained increasing attention in the field of cirrhosis, particularly in chronic liver disease with a definite aetiology. The current global prevalence of obesity and nonalcoholic fatty liver disease (NAFLD) is increasing, but there is currently a lack of a clear definition for recompensation in NAFLD-related cirrhosis. Here, we provide an up-to-date perspective on the natural history of NAFLD, emphasizing the reversible nature of the disease, summarizing possible mechanisms underlying recompensation in NAFLD, discussing challenges that need to be addressed and outlining future research directions in the field. Recompensation is a promising goal in patients with NAFLD-related cirrhosis, and further studies are needed to explore its underlying mechanisms and uncover its clinical features.

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.

Utility of pathologist panels for achieving consensus in NASH histologic scoring in clinical trials: Data from a phase 3 study

BACKGROUND

Liver histopathologic assessment is the accepted surrogate endpoint in NASH trials; however, the scoring of NASH Clinical Research Network (CRN) histologic parameters is limited by intraobserver and interobserver variability. We designed a consensus panel approach to minimize variability when using this scoring system. We assessed agreement between readers, estimated linear weighted kappas between 2 panels, compared them with published pairwise kappa estimates, and addressed how agreement or disagreement might impact the precision and validity of the surrogate efficacy endpoint in NASH trials.

METHODS

Two panels, each comprising 3 liver fellowship-trained pathologists who underwent NASH histology training, independently evaluated scanned whole slide images, scoring fibrosis, inflammation, hepatocyte ballooning, and steatosis from baseline and month 18 biopsies for 100 patients from the precirrhotic NASH study REGENERATE. The consensus score for each parameter was defined as agreement by ≥2 pathologists. If consensus was not reached, all 3 pathologists read the slide jointly to achieve a consensus score.

RESULTS

Between the 2 panels, the consensus was 97%-99% for steatosis, 91%-93% for fibrosis, 88%-92% for hepatocyte ballooning, and 84%-91% for inflammation. Linear weighted kappa scores between panels were similar to published NASH CRN values.

CONCLUSIONS

A panel of 3 trained pathologists independently scoring 4 NASH CRN histology parameters produced high consensus rates. Interpanel kappa values were comparable to NASH CRN metrics, supporting the accuracy and reproducibility of this method. The high concordance for fibrosis scoring was reassuring, as fibrosis is predictive of liver-specific outcomes and all-cause mortality.

MASLD treatment-a shift in the paradigm is imminent

MASLD prevalence is growing towards the leading cause of end-stage liver disease. Up to today, the most effective treatment is weight loss. Weight loss interventions are moving from lifestyle changes to bariatric surgery or endoscopy, and, more recently, to a new wave of anti-obesity drugs that can compete with bariatric surgery. Liver-targeted therapy is a necessity for those patients who already present liver fibrosis. The field is moving fast, and in the near future, we will testify to a disruptive change in MASLD treatment, similar to the paradigm-shift that occurred for hepatitis C almost one decade ago with direct antiviral agents.

Hyperinsulinemia, an overlooked clue and potential way forward in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease is closely associated with other features of the metabolic syndrome such as type 2 diabetes. The progression of the disease may lead to liver fibrosis, which is the main predictor of major adverse liver outcomes. Insulin resistance plays a major role in the pathogenesis of the disease. A component of fasting hyperinsulinemia is a failure of the liver to adjust the peripheral level of insulin due to reduced clearance. The associated fasting hyperinsulinemia has been independently associated as a predictor of major adverse liver outcomes and major adverse cardiovascular events. In this review, we discuss the potential mechanism and entanglement between liver fibrosis and hyperinsulinemia, and we hypothesize that the measure of fasting insulin could become a hepatic functional test within the armamentarium of noninvasive tests for the assessment of Metabolic dysfunction-associated steatotic liver disease.

Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia

BACKGROUND

We aimed to quantify ductular reaction (DR) in biliary atresia using a neural network in relation to underlying pathophysiology and prognosis.

METHODS

Image-processing neural network model was applied to 259 cytokeratin-7-stained native liver biopsies of patients with biliary atresia and 43 controls. The model quantified total proportional DR (DR%) composed of portal biliary epithelium (BE%) and parenchymal intermediate hepatocytes (PIH%). The results were related to clinical data, Sirius Red-quantified liver fibrosis, serum biomarkers, and bile acids.

RESULTS

In total, 2 biliary atresia biopsies were obtained preoperatively, 116 at Kasai portoenterostomy (KPE) and 141 during post-KPE follow-up. DR% (8.3% vs. 5.9%, p=0.045) and PIH% (1.3% vs. 0.6%, p=0.004) were increased at KPE in patients remaining cholestatic postoperatively. After KPE, patients with subsequent liver transplantation or death showed an increase in DR% (7.9%-9.9%, p = 0.04) and PIH% (1.6%-2.4%, p = 0.009), whereas patients with native liver survival (NLS) showed decreasing BE% (5.5%-3.0%, p = 0.03) and persistently low PIH% (0.9% vs. 1.3%, p = 0.11). In Cox regression, high DR predicted inferior NLS both at KPE [DR% (HR = 1.05, p = 0.01), BE% (HR = 1.05, p = 0.03), and PIH% (HR = 1.13, p = 0.005)] and during follow-up [DR% (HR = 1.08, p<0.0001), BE% (HR = 1.58, p = 0.001), and PIH% (HR = 1.04, p = 0.008)]. DR% correlated with Sirius red-quantified liver fibrosis at KPE (R = 0.47, p<0.0001) and follow-up (R = 0.27, p = 0.004). A close association between DR% and serum bile acids was observed at follow-up (R = 0.61, p<0.001). Liver fibrosis was not prognostic for NLS at KPE (HR = 1.00, p = 0.96) or follow-up (HR = 1.01, p = 0.29).

CONCLUSIONS

DR predicted NLS in different disease stages before transplantation while associating with serum bile acids after KPE.

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.

FXR agonists in NASH treatment

The farnesoid X receptor (FXR), a bile acid (BA)-activated nuclear receptor highly expressed in the liver and intestine, regulates the expression of genes involved in cholesterol and bile acid homeostasis, hepatic gluconeogenesis, lipogenesis, inflammation and fibrosis, in addition to controlling intestinal barrier integrity, preventing bacterial translocation and maintaining gut microbiota eubiosis. Non-alcoholic steatohepatitis (NASH), an advanced stage of non-alcoholic fatty liver disease, is characterized by hepatic steatosis, hepatocyte damage (ballooning) and inflammation, leading to fibrosis, cirrhosis and hepatocellular carcinoma. NASH represents a major unmet medical need, but no pharmacological treatments have yet been approved. The pleiotropic mechanisms involved in NASH development offer a range of therapeutic opportunities and among them FXR activation has emerged as an established pharmacological target. Various FXR agonists with different physicochemical properties, which can be broadly classified as BA derivatives, non-BA-derived steroidal FXR agonists, non-steroidal FXR agonists, and partial FXR agonists, are in advanced clinical development. In this review we will summarize key preclinical and clinical features of the most advanced FXR agonists and critically evaluate their potential in NASH treatment.

Found in translation-Fibrosis in metabolic dysfunction-associated steatohepatitis (MASH)

Metabolic dysfunction-associated steatohepatitis (MASH) is a severe form of liver disease that poses a global health threat because of its potential to progress to advanced fibrosis, leading to cirrhosis and liver cancer. Recent advances in single-cell methodologies, refined disease models, and genetic and epigenetic insights have provided a nuanced understanding of MASH fibrogenesis, with substantial cellular heterogeneity in MASH livers providing potentially targetable cell-cell interactions and behavior. Unlike fibrogenesis, mechanisms underlying fibrosis regression in MASH are still inadequately understood, although antifibrotic targets have been recently identified. A refined antifibrotic treatment framework could lead to noninvasive assessment and targeted therapies that preserve hepatocellular function and restore the liver's architectural integrity.

Antifibrotic therapy in nonalcoholic steatohepatitis: time for a human-centric approach

Nonalcoholic steatohepatitis (NASH) might soon become the leading cause of end-stage liver disease and indication for liver transplantation worldwide. Fibrosis severity is the only histological predictor of liver-related morbidity and mortality in NASH identified to date. Moreover, fibrosis regression is associated with improved clinical outcomes. However, despite numerous clinical trials of plausible drug candidates, an approved antifibrotic therapy remains elusive. Increased understanding of NASH susceptibility and pathogenesis, emerging human multiomics profiling, integration of electronic health record data and modern pharmacology techniques hold enormous promise in delivering a paradigm shift in antifibrotic drug development in NASH. There is a strong rationale for drug combinations to boost efficacy, and precision medicine strategies targeting key genetic modifiers of NASH are emerging. In this Perspective, we discuss why antifibrotic effects observed in NASH pharmacotherapy trials have been underwhelming and outline potential approaches to improve the likelihood of future clinical success.

Quantitative Assessment of Hepatic Steatosis Using Label-Free Multiphoton Imaging and Customized Image Processing Program

Nonalcoholic fatty liver disease is rapidly becoming one of the most common causes of chronic liver disease worldwide and is the leading cause of liver-related morbidity and mortality. A quantitative assessment of the degree of steatosis would be more advantageous for diagnostic evaluation and exploring the patterns of disease progression. Here, multiphoton microscopy, based on the second harmonic generation and 2-photon excited fluorescence, was used to label-free image the samples of nonalcoholic fatty liver. Imaging results confirm that multiphoton microscopy is capable of directly visualizing important pathologic features such as normal hepatocytes, hepatic steatosis, Mallory bodies, necrosis, inflammation, collagen deposition, microvessel, and so on and is a reliable auxiliary tool for the diagnosis of nonalcoholic fatty liver disease. Furthermore, we developed an image segmentation algorithm to simultaneously assess hepatic steatosis and fibrotic changes, and quantitative results reveal that there is a correlation between the degree of steatosis and collagen content. We also developed a feature extraction program to precisely display the spatial distribution of hepatocyte steatosis in tissues. These studies may be beneficial for a better clinical understanding of the process of steatosis as well as for exploring possible therapeutic targets.