Quantifying and monitoring fibrosis in non-alcoholic fatty liver disease using dual-photon microscopy

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.

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.

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.

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.

Two-photon imaging reveals histopathological changes in the gastric tumor microenvironment induced by neoadjuvant treatment

There is a close association between tumor response and survival in gastric cancer patients after receiving neoadjuvant treatment. An accurate and rapid assessment of therapeutic efficacy would be helpful for subsequent treatments and individual prognosis. At present, pathological examination is the gold standard for evaluating treatment response, however, it requires additional staining and the process is tedious, labor-intensive, as well as time-consuming. Here, we introduce a label-free imaging technique, two-photon imaging, to evaluate histopathological changes induced by pre-operative therapy, with a focus on assessing tumor regression as well as stromal response. Imaging data show that two-photon imaging allows label-free, rapid visualization of various aspects of pathological alterations in tumor microenvironment such as fibrotic reaction, inflammatory cell infiltration, mucinous response, isolated residual tumor cells. Moreover, a semi-automatic image processing approach is developed to extract the collagen morphological features, and statistical results show that there are significant differences in collagen area, length, width, cross-link space between the gastric cancer tissues with and without treatment. With the advent of a portable, miniaturized two-photon imaging device, we have enough reason to believe that this technique will become as an important auxiliary diagnostic tool in assessing neoadjuvant treatment response and thereby tailoring the most appropriate therapy strategies for the patients.

Liver fibrosis quantified by image morphometry predicts clinical outcomes in patients with non-alcoholic fatty liver disease

BACKGROUND AND AIMS

Liver fibrosis predicts adverse clinical outcomes, such as liver-related death (LRD) and hepatocellular carcinoma (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). We aimed to investigate the accuracy of semi-automated quantification of collagen proportionate area (CPA) as an objective new method for predicting clinical outcomes.

METHOD

Liver biopsies from patients with NAFLD underwent computerized image morphometry of Sirius Red staining with CPA quantification performed by ImageScope. Clinical outcomes, including total mortality, LRD, and combined liver outcomes (liver decompensation, HCC, or LRD), were determined by medical records and population-based data-linkage. The accuracy of CPA for predicting outcomes was compared with non-invasive fibrosis tests (Hepascore, FIB-4, APRI).

RESULTS

A total of 295 patients (mean age 50 years) were followed for a median (range) of 9 (0.2-25) years totalling 3253 person-years. Patients with CPA ≥ 10% had significantly higher risks for total death [hazard ratio (HR): 5.0 (1.9-13.2)], LRD [19.0 (2.0-182.0)], and combined liver outcomes [15.6 (3.1-78.6)]. CPA and pathologist fibrosis staging (FS) showed similar accuracy (AUROC) for the prediction of total death (0.68 vs. 0.70), LRD (0.72 vs. 0.77) and combined liver outcomes (0.75 vs. 0.78). Non-invasive serum markers Hepascore, APRI, and FIB-4 reached higher AUROC; however, they were not statistically significant compared to that of CPA except for Hepascore in predicting total mortality (0.86 vs. 0.68, p = 0.009).

CONCLUSION

Liver fibrosis quantified by CPA analysis was significantly associated with clinical outcomes including total mortality, LRD, and HCC. CPA achieved similar accuracy in predicting outcomes compared to pathologist fibrosis staging and non-invasive serum markers.

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

A cuproptosis-related lncRNA signature predicts the prognosis and immune cell status in head and neck squamous cell carcinoma

Introduction

The incidence of head and neck squamous cell carcinoma (HNSCC), one of the most prevalent tumors, is increasing rapidly worldwide. Cuproptosis, as a new copper-dependent cell death form, was proposed recently. However, the prognosis value and immune effects of cuproptosis-related lncRNAs (CRLs) have not yet been elucidated in HNSCC.

Methods

In the current study, the expression pattern, differential profile, clinical correlation, DNA methylation, functional enrichment, univariate prognosis factor, and the immune effects of CRLs were analyzed. A four-CRL signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm.

Results

Results showed that 20 CRLs had significant effects on the stage progression of HNSCC. Sixteen CRLs were tightly correlated with the overall survival (OS) of HNSCC patients. Particularly, lnc-FGF3-4 as a single risk factor was upregulated in HNSCC tissues and negatively impacted the prognosis of HNSCC. DNA methylation probes of cg02278768 (MIR9-3HG), cg07312099 (ASAH1-AS1), and cg16867777 (TIAM1-AS1) were also correlated with the prognosis of HNSCC. The four-CRL signature that included MAP4K3-DT, lnc-TCEA3-1, MIR9-3HG, and CDKN2A-DT had a significantly negative effect on the activation of T cells follicular helper and OS probability of HNSCC. Functional analysis revealed that cell cycle, DNA replication, and p53 signal pathways were enriched.

Discussion

A novel CRL-related signature has the potential of prognosis prediction in HNSCC. Targeting CRLs may be a promising therapeutic strategy for HNSCC.

Extracellular Vesicles and Fatty Liver

Fatty liver is a complex pathological process caused by multiple etiologies. In recent years, the incidence of fatty liver has been increasing year by year, and it has developed into a common chronic disease that seriously affects people's health around the world. It is an important risk factor for liver cirrhosis, liver cancer, and a variety of extrahepatic chronic diseases. Therefore, the early diagnosis and early therapy of fatty liver are important. Except for invasive liver biopsy, there is still a lack of reliable diagnosis and staging methods. Extracellular vesicles are small double-layer lipid membrane vesicles derived from most types of cells. They play an important role in intercellular communication and participate in the occurrence and development of many diseases. Since extracellular vesicles can carry a variety of biologically active substances after they are released by cells, they have received widespread attention. The occurrence and development of fatty liver are also closely related to extracellular vesicles. In addition, extracellular vesicles are expected to provide a new direction for the diagnosis of fatty liver. This article reviews the relationship between extracellular vesicles and fatty liver, laying a theoretical foundation for the development of new strategies for the diagnosis and therapy of fatty liver.

Artificial intelligence in nonalcoholic fatty liver disease

Background

Nonalcoholic fatty liver disease (NAFLD) has led to serious health-related complications worldwide. NAFLD has wide pathological spectra, ranging from simple steatosis to hepatitis to cirrhosis and hepatocellular carcinoma. Artificial intelligence (AI), including machine learning and deep learning algorithms, has provided great advancement and accuracy in identifying, diagnosing, and managing patients with NAFLD and detecting squeal such as advanced fibrosis and risk factors for hepatocellular cancer. This review summarizes different AI algorithms and methods in the field of hepatology, focusing on NAFLD.

Methods

A search of PubMed, WILEY, and MEDLINE databases were taken as relevant publications for this review on the application of AI techniques in detecting NAFLD in suspected population

Results

Out of 495 articles searched in relevant databases, 49 articles were finally included and analyzed. NASH-Scope model accurately distinguished between NAFLD and non-NAFLD and between NAFLD without fibrosis and NASH with fibrosis. The logistic regression (LR) model had the highest accuracy, whereas the support vector machine (SVM) had the highest specificity and precision in diagnosing NAFLD. An extreme gradient boosting model had the highest performance in predicting non-alcoholic steatohepatitis (NASH). Electronic health record (EHR) database studies helped the diagnose NAFLD/NASH. Automated image analysis techniques predicted NAFLD severity. Deep learning radiomic elastography (DLRE) had perfect accuracy in diagnosing the cases of advanced fibrosis.

Conclusion

AI in NAFLD has streamlined specific patient identification and has eased assessment and management methods of patients with NAFLD.

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

BACKGROUND & AIMS

Liver fibrosis is a key prognostic determinant for clinical outcomes in non-alcoholic steatohepatitis (NASH). Current scoring systems have limitations, especially in assessing fibrosis regression. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale. This approach was applied to gain in-depth understanding of fibrosis dynamics after treatment with tropifexor (TXR), a non-bile acid farnesoid X receptor agonist in patients participating in the FLIGHT-FXR study (NCT02855164).

METHOD

Unstained sections from 198 liver biopsies (paired: baseline and end-of-treatment) from 99 patients with NASH (fibrosis stage F2 or F3) who received placebo (n = 34), TXR 140 μg (n = 37), or TXR 200 μg (n = 28) for 48 weeks were examined. Liver fibrosis (qFibrosis®), hepatic fat (qSteatosis®), and ballooned hepatocytes (qBallooning®) were quantitated using SHG/TPEF microscopy. Changes in septa morphology, collagen fiber parameters, and zonal distribution within liver lobules were also quantitatively assessed.

RESULTS

Digital analyses revealed treatment-associated reductions in overall liver fibrosis (qFibrosis®), unlike conventional microscopy, as well as marked regression in perisinusoidal fibrosis in patients who had either F2 or F3 fibrosis at baseline. Concomitant zonal quantitation of fibrosis and steatosis revealed that patients with greater qSteatosis reduction also have the greatest reduction in perisinusoidal fibrosis. Regressive changes in septa morphology and reduction in septa parameters were observed almost exclusively in F3 patients, who were adjudged as 'unchanged' with conventional scoring.

CONCLUSION

Fibrosis regression following hepatic fat reduction occurs initially in the perisinusoidal regions, around areas of steatosis reduction. Digital pathology provides new insights into treatment-induced fibrosis regression in NASH, which are not captured by current staging systems.

LAY SUMMARY

The degree of liver fibrosis (tissue scarring) in non-alcoholic steatohepatitis (NASH) is the main predictor of negative clinical outcomes. Accurate assessment of the quantity and architecture of liver fibrosis is fundamental for patient enrolment in NASH clinical trials and for determining treatment efficacy. Using digital microscopy with artificial intelligence analyses, the present study demonstrates that this novel approach has greater sensitivity in demonstrating treatment-induced reversal of fibrosis in the liver than current systems. Furthermore, additional details are obtained regarding the pathogenesis of NASH disease and the effects of therapy.

Efficacy and Safety of a Botanical Formula Fuzheng Huayu for Hepatic Fibrosis in Patients with CHC: Results of a Phase 2 Clinical Trial

Background. Hepatitis C virus (HCV) is a common cause of progressive hepatic fibrosis, cirrhosis, and hepatocellular carcinoma worldwide. Despite the availability of effective direct-acting antivirals, patients often have significant hepatic fibrosis at the time of diagnosis due to delay in diagnosis and comorbidities which promote fibrogenesis. Thus, antifibrotic agents represent an attractive adjunctive therapy. Fuzheng Huayu (FZHY), a traditional Chinese medicine botanical formulation, has been used as an antifibrotic agent in chronic HBV infection. Our aim was to assess FZHY in patients with HCV infection and active viremia. Method. We randomized 118 patients with active viremia from 8 liver centers in the U.S. to receive oral FZHY (n = 59) or placebo (n = 59) for 48 weeks. Efficacy was assessed by histopathologic changes at the end of therapy. A subset of biopsies was further analyzed using qFibrosis to detect subtle changes in fibrosis in different zones of the hepatic lobules. Results. FZHY was well tolerated and safe. Patients with baseline Ishak fibrosis stages F3 and F4 had better response rates to FZHY than patients with baseline F0–F2 ( $p=0.03$ ). qFibrosis zonal analysis showed significant improvement in fibrosis in all zones in patients with regression of the fibrosis stage. Conclusions. FZHY produced antifibrotic effects in patients with baseline Ishak F3 and F4 fibrosis stages. Reduction in fibrosis severity was zonal and correlated with the severity of inflammation. Based on its tolerability, safety, and efficacy, FZHY should be further investigated as a therapy in chronic liver diseases because of its dual anti-inflammatory and antiibrotic properties. Lay Summary. This is the first US-based, multicenter and placebo-controlled clinical trial that shows statistically significant reduction in fibrosis in patients with active HCV using an antifibrotic botanical formula. This has important implications as there is an immediate need for effective antifibrotic agents in treating many chronic diseases including NASH that lead to scarring of the liver. With artificial intelligence-based methodology, qFibrosis, we may provide a more reliable way to assess the FZHY as a therapy in chronic liver diseases because of its dual anti-inflammatory and antifibrotic properties.

Breakthroughs in therapies for NASH and remaining challenges

Initially a condition that received limited recognition and whose clinical impact was controversial, non-alcoholic steatohepatitis (NASH) has become a leading cause of chronic liver disease. Although there are no approved therapies, major breakthroughs, which will be reviewed here, have paved the way for future therapeutic successes. The unmet medical need in NASH is no longer disputed, and progress in the understanding of its pathogenesis has resulted in the identification of many pharmacological targets. Key surrogate outcomes for therapeutic trials are now accepted by regulatory agencies, thus creating a path for drug registration. A set of non-invasive measurements enabled early-stage trials to be conducted expeditiously, thus providing early indications on the biological and possibly clinical actions of therapeutic candidates. This generated efficacy results for a number of highly promising compounds that are now in late-stage development. Intense research aimed at further improving the assessment of histological endpoints and in developing non-invasive predictive biomarkers is underway. This will help improve the design and feasibility of successful trials, ultimately providing patients with therapeutic options that can change the course of the disease.

Heparan sulfate proteoglycans-mediated targeted delivery of TGF-β1-binding peptide to liver for improved anti-liver fibrotic activity in vitro and in vivo

Elevated expressions of transforming growth factor β1 (TGF-β1) have been implicated in the pathogenesis of liver fibrosis, thus attenuating the excessive TGF-β1's activity by TGF-β1-binding peptide is an ideal strategy for the treatment of liver fibrosis. However, the application of small peptide as a pharmaceutical agent is obstacle due to difficult preparation and non-selective delivery. The I-plus sequences of circumsporozoite protein (CSP-I) possesses high affinity for heparan sulfate proteoglycans, which are primarily located on liver tissues. TGF-β1-binding peptide P15 holds specific ability of binding to TGF-β1. In this study, we describe an approach to efficiently preparing liver-targeting peptide P15-CSP-I, which is conjugation of the sequences of P15 to the N-terminus of CSP-I, from the cleavage of biological macromolecule SUMO-tagged P15-CSP-I. In vitro and ex vivo binding assay showed that P15-CSP-I specifically targeted to the hepatocytes and liver tissues. Moreover, P15-CSP-I inhibited cell proliferation, migration and invasion, and decreased fibrosis-related proteins expression in TGF-β1-activated HSCs in vitro. Furthermore, P15-CSP-I ameliorated liver morphology and decreased the fibrosis responses in vivo. Taken together, P15-CSP-I may be a potential candidate for targeting therapy on liver fibrosis due to its high efficient preparation, specific liver-targeting potential and improved anti-liver fibrotic activity.

Dysregulated N6-methyladenosine (m6A) processing in hepatocellular carcinoma

N6-methyladenosine (m⁶A) is the most thoroughly studied type of internal RNA modification, as this epigenetic modification is the most abundant in eukaryotic RNAs to date. This modification occurs in various types of RNAs and plays significant roles in dominant RNA-related processes, such as translation, splicing, export and degradation. These processes are catalyzed by three types of prominent enzymes: writers, erasers and readers. Increasing evidence has shown that m⁶A modification is vital for the regulation of gene expression, carcinogenesis, tumor progression and other abnormal changes, and recent studies have shown that m⁶A is important in the development of hepatocellular carcinoma (HCC). Herein, we summarize the nature and regulatory mechanisms of m⁶A modification, including its role in the pathogenesis of HCC and related chronic liver diseases. We also highlight the clinical significance and future strategies involving RNA m⁶A modifications in HCC.

The Role of Notch Signaling Pathway in Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, and progressive NAFLD can develop into non-alcoholic steatohepatitis (NASH), liver cirrhosis, or hepatocellular carcinoma (HCC). NAFLD is a kind of metabolic disordered disease, which is commonly associated with lipid metabolism, insulin resistance, oxidative stress, inflammation, and fibrogenesis, as well as autophagy. Growing studies have shown Notch signaling pathway plays a pivotal role in the regulation of NAFLD progression. Here, we review the profile of the Notch signaling pathway, new evidence of Notch signaling involvement in NAFLD, and describe the potential of Notch as a biomarker and therapeutic target for NAFLD treatment.

Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

Simple Summary

Hepatocellular carcinoma (HCC) is the third most commonly diagnosed cancer in the world, and surgical resection is the commonly used curative management of early-stage disease. However, the recurrence rate is high after resection, and liver fibrosis has been thought to increase the risk of recurrence. Conventional histological staging of fibrosis is highly subjective to observer variations. To overcome this limitation, we used a fully quantitative fibrosis assessment tool, qFibrosis (utilizing second harmonic generation and two-photon excitation fluorescence microscopy), with multi-dimensional artificial intelligence analysis to establish a fully-quantitative, accurate fibrotic score called a “combined index”, which can predict early recurrence of HCC after curative intent resection. Therefore, we can pay more attention on the patients with high risk of early recurrence.

Abstract

Background: Liver fibrosis is thought to be associated with early recurrence of hepatocellular carcinoma (HCC) after resection. To recognize HCC patients with higher risk of early recurrence, we used a second harmonic generation and two-photon excitation fluorescence (SHG/TPEF) microscopy to create a fully quantitative fibrosis score which is able to predict early recurrence. Methods: The study included 81 HCC patients receiving curative intent hepatectomy. Detailed fibrotic features of resected hepatic tissues were obtained by SHG/TPEF microscopy, and we used multi-dimensional artificial intelligence analysis to create a recurrence prediction model “combined index” according to the morphological collagen features of each patient’s non-tumor hepatic tissues. Results: Our results showed that the “combined index” can better predict early recurrence (area under the curve = 0.917, sensitivity = 81.8%, specificity = 90.5%), compared to alpha fetoprotein level (area under the curve = 0.595, sensitivity = 68.2%, specificity = 47.6%). Using a Cox proportional hazards analysis, a higher “combined index” is also a poor prognostic factor of disease-free survival and overall survival. Conclusions: By integrating multi-dimensional artificial intelligence and SHG/TPEF microscopy, we may locate patients with a higher risk of recurrence, follow these patients more carefully, and conduct further management if needed.

Second-harmonic generation (SHG) microscopy and hepatic venous pressure gradient-based validation of a novel histological staging system for alcoholic hepatitis

Alcoholic hepatitis (AH) lacks specific histological staging. A novel fibrosis staging that encompasses perisinusoidal fibrosis and cirrhosis sub-stages, substantiated by Hepatic venous pressure gradient (HVPG) and automated fibrosis quantification, is imperative. To correlate novel histological staging system of AH with second-harmonic generation microscopy (SHG)-based q-fibrosis, HVPG, and activated hepatic stellate cells (HSCs). Liver biopsies of AH (n = 175) were staged semi-quantitatively as F0, F1, F2, F3A and F3B and Laennec substages of cirrhosis 4A, 4B and 4C. Stages were correlated with SHG q-fibrosis parameters, HVPG and HSCs. Mean age 41.2 ± 9.4 years, 96.6% males, bilirubin 20.58 ± 8.0 mg/dl and Maddrey's discriminant function 78.9 ± 36.7 displayed advanced fibrosis in 98.6%. With increasing histological stages, an increase in q-fibrosis indices and mean HVPG (p < 0.0001) were recorded; stage 4C showed the most significant difference from other stages (p < 0.000). Stages 3A and 3B were comparable with the stages 4A and 4B, respectively, for q-fibrosis (p = 1) and HVPG (p = 1). HSCs (> 30%) were significantly higher in stage 3 (75%) compared with 4 (49%) and 2 (59%), p = 0.018. Overall agreement for histological staging was excellent for all stages (0.82). SHG quantified fibrosis and HVPG corroborates the novel histological staging of AH. Expansive PCF matches with collagen content and clinical severity to early sub-stages of cirrhosis. This highlights the need for an accurate quantification and inclusion of PCF as a separate stage. SHG-based quantification can be a useful adjunct to histological fibrosis staging systems.

Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis

Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.

Liver biopsy in the quantitative assessment of liver fibrosis in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) is a major cause of liver fibrosis/cirrhosis and liver-related mortality. Despite emergence of noninvasive tests, liver biopsy remains the mainstay for the diagnosis and assessment of disease severity and chronicity. Accurate detection and quantification of liver fibrosis with architectural localization are essential for assessing the severity of NAFLD and its response to antifibrotic therapy in clinical trials. Conventional histological scoring systems for liver fibrosis are semiquantitative. Collagen proportionate area is morphometric by measuring the percentage of fibrosis on a continuous scale but is limited by the absence of architectural input. Ultra-fast laser microscopy, e.g., second harmonic generation (SHG) imaging, has enabled in-depth analysis of fibrillary collagen based on intrinsic optical signals. Quantification and calculation of different detailed variables of collagen fibers can be used to establish algorithm-based quantitative fibrosis scores (e.g. qFibrosis, q-FPs) in NAFLD. Artificial intelligence is being explored to further develop quantitative fibrosis scoring methods. SHG microscopy should be considered the new gold standard for the quantitative assessment of liver fibrosis, reaffirming the pivotal role of the liver biopsy in NAFLD, at least for the near-future. The ability of SHG-derived algorithms to intuitively detect subtle nuances in liver fibrosis changes over a continuous scale should be employed to redress the efficacy endpoint for fibrosis in NASH clinical trials. The current decrease by 1-point or more in fibrosis stage may not be realistic for the evaluation of therapeutic response to antifibrotic drugs in relatively short-term trials.

Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials

BACKGROUND & AIMS

Liver biopsies are a critical component of pivotal studies in non-alcoholic steatohepatitis (NASH), constituting inclusion criteria, risk stratification factors and endpoints. We evaluated the reliability of NASH Clinical Research Network scoring of liver biopsies in a NASH clinical trial.

METHODS

Digitized slides of 678 biopsies from 339 patients with paired biopsies randomized into the EMMINENCE study - examining a novel insulin sensitizer (MSDC-0602K) in NASH - were read independently by 3 hepatopathologists blinded to treatment code and scored using the NASH CRN histological scoring system. Various endpoints were computed from these scores.

RESULTS

Inter-reader linearly weighted kappas were 0.609, 0.484, 0.328, and 0.517 for steatosis, fibrosis, lobular inflammation, and ballooning, respectively. Inter-reader unweighted kappas were 0.400 for the diagnosis of NASH, 0.396 for NASH resolution without worsening fibrosis, and 0.366 for fibrosis improvement without worsening NASH. In the current study, 46.3% of the patients included in the study based on 1 hepatopathologist's qualifying reading were deemed not to meet the study's histologic inclusion criteria by at least 1 of the 3 hepatopathologists. The MSDC-0602K treatment effect was lowest for those histologic features with lower inter-reader reliability. Simulations show that the lack of reliability of endpoints and inclusion criteria can drastically reduce study power - from >90% in a well-powered study to as low as 40%.

CONCLUSIONS

The reliability of hepatopathologists' liver biopsy evaluation using currently accepted criteria is suboptimal. This lack of reliability may affect NASH pivotal studies by introducing patients who do not meet NASH study entry criteria, misclassifying fibrosis subgroups, and attenuating apparent treatment effects.

LAY SUMMARY

Since liver biopsy analysis plays such an important role in clinical studies of non-alcoholic steatohepatitis, it is important to understand the reliability of hepato-pathologist readings. We examined both inter- and intra-reader variability in a large data set of paired liver biopsies from a clinical trial. We found very poor inter-reader and modest intra-reader variability. This result has important implications for entry criteria, fibrosis stratification, and the ability to measure a treatment effect in clinical trials.

Updates in the quantitative assessment of liver fibrosis for nonalcoholic fatty liver disease: Histological perspective

Nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) is a major cause of liver fibrosis and cirrhosis. Accurate assessment of liver fibrosis is important for predicting disease outcomes and assessing therapeutic response in clinical practice and clinical trials. Although noninvasive tests such as transient elastography and magnetic resonance elastography are preferred where possible, histological assessment of liver fibrosis via semiquantitative scoring systems remains the current gold standard. Collagen proportionate area provides more granularity by measuring the percentage of fibrosis on a continuous scale, but is limited by the absence of architectural input. Although not yet used in routine clinical practice, advances in second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy imaging show great promise in characterising architectural features of fibrosis at the individual collagen fiber level. Quantification and calculation of different detailed variables of collagen fibers can be used to establish algorithm-based quantitative fibrosis scores (e.g., qFibrosis, q-FPs), which have been validated against fibrosis stage in NAFLD. Artificial intelligence is being explored to further refine and develop quantitative fibrosis scoring methods. SHG-microscopy shows promise as the new gold standard for the quantitative measurement of liver fibrosis. This has reaffirmed the pivotal role of the liver biopsy in fibrosis assessment in NAFLD, at least for the near-future. The ability of SHG-derived algorithms to intuitively detect subtle nuances in liver fibrosis changes over a continuous scale should be employed to redress the efficacy endpoint for fibrosis in NASH clinical trials; this approach may improve the outcomes of the trials evaluating therapeutic response to antifibrotic drugs.

An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials

Background: Many clinical trials with potential drug treatment options for non-alcoholic fatty liver disease (NAFLD) are focused on patients with non-alcoholic steatohepatitis (NASH) stages 2 and 3 fibrosis. As the histological features differentiating stage 1 (F1) from stage 2 (F2) NASH fibrosis are subtle, some patients may be wrongly staged by the in-house pathologist and miss the opportunity for enrollment into clinical trials. We hypothesized that our refined artificial intelligence (AI)-based algorithm (qFibrosis) can identify these subtle differences and serve as an assistive tool for in-house pathologists. Methods: Liver tissue from 160 adult patients with biopsy-proven NASH from Singapore General Hospital (SGH) and Peking University People’s Hospital (PKUH) were used. A consensus read by two expert hepatopathologists was organized. The refined qFibrosis algorithm incorporated the creation of a periportal region that allowed for the increased detection of periportal fibrosis. Consequently, an additional 28 periportal parameters were added, and 28 pre-existing perisinusoidal parameters had altered definitions. Results: Twenty-eight parameters (20 periportal and 8 perisinusoidal) were significantly different between the F1 and F2 cases that prompted a change of stage after a careful consensus read. The discriminatory ability of these parameters was further demonstrated in a comparison between the true F1 and true F2 cases as 26 out of the 28 parameters showed significant differences. These 26 parameters constitute a novel sub-algorithm that could accurately stratify F1 and F2 cases. Conclusion: The refined qFibrosis algorithm incorporated 26 novel parameters that showed a good discriminatory ability for NASH fibrosis stage 1 and 2 cases, representing an invaluable assistive tool for in-house pathologists when screening patients for NASH clinical trials.

Expression of Notch family is altered in non‑alcoholic fatty liver disease

The aim of the present study was to explore the dynamic relationship between Notch and non‑alcoholic fatty liver disease (NAFLD), both in vitro and in vivo. The LX2, Huh7 and MIHA hepatic cell lines were used to establish a cell steatosis model induced by palmitic acid (PA) at different concentrations (0.1, 0.25 and 0.5 mM). Cell proliferation and migration were assessed using a 5‑bromo‑2'‑deoxyuridine kit and a wound healing assay. The dosage of 0.25 mM PA for 36‑48 h treatment was chosen for subsequent experiments. Steatotic cells were identified by Oil Red O staining. Feeding mice a methionine‑choline‑deficient (MCD) diet is known induce a model of NAFLD, compared with a methionine‑choline‑sufficient (MCS) diet. Therefore, Notch family mRNA expression was evaluated in the liver of MCD‑fed mice at varying time points (days 5, 10, 21 and 70) using reverse transcription‑quantitative PCR. Notch expression levels were also assessed in cell lines at 12, 24, 36 and 48 h after PA treatment. Notch signaling molecules changed in the PA or MCD model over time. In vitro, the mRNA levels of Notch1, ‑2 and ‑4 increased in all cell lines after 12‑h PA treatment. At 24 h, these genes were upregulated only in LX2 cells, while showing a 'down‑up' pattern in MIHA cells (i.e. these genes were downregulated at 24 h but upregulated at 36 h). However, expression of Notch1, ‑2, ‑3 and ‑4 mRNA rose significantly in the early stage (day 10) of NAFLD. At week 3, the levels of Notch1 and ‑2 were higher in the MCD group than in the MCS group, while the reverse was observed for Notch3 and ‑4. Expression of these four genes increased again in the late stage (day 70) of NAFLD. Therefore, these results indicated that Notch family members Notch1‑4 were involved in the development of NAFLD and played an important role in steatosis in this model.