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  Table of Contents    
REVIEW ARTICLE  
Year : 2021  |  Volume : 64  |  Issue : 5  |  Page : 104-111
Liver biopsy in the quantitative assessment of liver fibrosis in nonalcoholic fatty liver disease


Department of Pathology, National University Hospital, Singapore

Click here for correspondence address and email

Date of Submission07-Aug-2020
Date of Decision22-Sep-2020
Date of Acceptance02-Oct-2020
Date of Web Publication7-Jun-2021
 

   Abstract 


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.

Keywords: Liver biopsy, liver fibrosis, nonalcoholic fatty liver disease, quantification

How to cite this article:
Ting Soon GS, Wee A. Liver biopsy in the quantitative assessment of liver fibrosis in nonalcoholic fatty liver disease. Indian J Pathol Microbiol 2021;64:104-11

How to cite this URL:
Ting Soon GS, Wee A. Liver biopsy in the quantitative assessment of liver fibrosis in nonalcoholic fatty liver disease. Indian J Pathol Microbiol [serial online] 2021 [cited 2021 Jun 13];64:104-11. Available from: https://www.ijpmonline.org/text.asp?2021/64/5/104/317934





   Introduction Top


Nonalcoholic fatty liver disease (NAFLD) has emerged globally as a leading cause of liver-related morbidity/mortality, especially with the increasing incidence of metabolic syndrome and unhealthy lifestyles. Following behind the United States, the prevalence of NAFLD has similarly increased in Asia from 25% (1999-2005) to 34% (2012-2017).[1] NAFLD encompasses a spectrum of phenotypes ranging from steatosis, referred to as nonalcoholic fatty liver (NAFL), to nonalcoholic steatohepatitis (NASH). The latter has a propensity for fibrosis leading to liver-related mortality, namely, cirrhotic decompensation and hepatocellular carcinoma.[2]

Despite the increasing utility and recommendations for noninvasive tests, liver biopsy remains the current gold standard for diagnosis of NAFLD and assessment of disease severity and chronicity. Four criteria are assessed in the histological diagnosis of NASH – steatosis, ballooning degeneration of hepatocytes, and lobular inflammation determine the activity, while fibrosis determines the stage [Figure 1]. Accurate assessment of liver fibrosis is crucial for understanding the pathogenesis of the disease, patient management, and establishment of precise and realistic efficacy endpoints for assessing therapeutic response in NASH clinical trials.
Figure 1: Liver biopsy demonstrating three of the four histological features assessed in patients with NAFLD (Hematoxylin&Eosin). Steatosis (*) is represented by hepatocytes containing large to small droplet fat; there is peripheral nuclear displacement in the cell highlighted. Hepatocellular ballooning degeneration (black arrow) is characterized by an enlarged hepatocyte with rounded contour and clear reticular appearance of the cytoplasm due to clumping of the intermediate filaments. Lobular inflammation (circle) is present as spotty necrosis of a hepatocyte surrounded by neutrophils and lymphocytes

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Current tissue assessment methods such as semiquantitative histological scoring systems or morphometric analysis via collagen proportionate area (CPA) undoubtedly have their limitations. However, the advent of ultra-fast laser microscopy such as second harmonic generation (SHG) imaging allows a more detailed assessment of collagen fibers that can be utilized to both reflect the actual amount of collagen deposited as well as retain the architectural pattern of collagen distribution. SHG-based imaging techniques thus have great potential to further our understanding of the progression and regression of liver fibrosis, help establish prognostic indicators and prediction models, and contribute to the standardization of fibrosis stage assessment/other related NASH histological indices for clinical trials. This review focuses on the pivotal contribution of the liver biopsy to the detection and quantitative assessment of liver fibrosis in NAFLD/NASH.


   Evolution and Pattern of Liver Fibrosis Top


In the steatohepatitic liver, ballooning and lobular inflammation are believed to be the primary drivers of fibrosis [Figure 2]. The PIVENS and FLINT trials have both demonstrated a close and concordant relationship between changes in disease activity and fibrosis stage.[3] This has great impact on the choice of surrogate endpoints in short-term clinical trials and drug development for NAFLD/NASH.
Figure 2: (a-e) Stages of fibrosis according to NASH CRN system: (a) Stage 1b, (b)Stage 2, (c) Stage 3, (d-e) Stage 4, assessed on Masson Trichrome-stained liver sections. Within stage 4 (cirrhosis), pericellular/perisinusoidal fibrosis may or may not still be discernible (d and e, respectively). Note the thick fibrous septa in established cirrhosis (e). (f) This example of cirrhosis regression demonstrates the features of the hepatic repair complex, such as thinning and perforation of fibrous septa with confluence of cirrhotic nodules to form macronodular appearance, and “misplaced” central veins

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Progression of fibrosis: The histological pattern of fibrosis differs between adult and pediatric patients with NAFLD. Adult NAFLD has a distinctive centrilobular pattern of fibrosis, in contrast to the portal-based scarring seen in chronic hepatitis B (CHB) or C (CHC). The fibrosis in NAFLD begins as perivenular perisinusoidal fibrosis by way of activated hepatic stellate cells (HSC) laying down matrix in the space of Disse. As this delicate fibrosis fans out in the lobule, periportal fibrosis sets in, followed by bridging fibrosis linking central and portal regions. Further hepatocyte damage/death leads to (confluent areas of) parenchymal extinction, approximation of central veins to portal regions, and disruption of lobular architecture with thick fibrous bands encircling cirrhotic nodules. This represents the conventionally ascribed four stages of fibrosis in NAFLD. On the other hand, in a proportion of pediatric NAFLD patients, there is a tendency for zone 1-centric steatosis with preferential accentuation of portal inflammation and periportal fibrosis, noticeably lacking perisinusoidal fibrosis.

Regression of fibrosis: It is now well documented that cirrhosis can regress, even in NAFLD patients.[4] According to Wanless, the evolution of cirrhosis is complex—it is initially etiology-driven but thereafter, vascular/ischemic processes ensue. Wanless et al.[5] has elegantly described the histological features of parenchymal extinction, scarring, and the “hepatic repair complex” for the recognition of cirrhosis regression. Briefly, there is thinning and perforation of the thick fibrous septa as hepatocytes proliferate and adjoining nodules converge. Central veins are seen juxtaposed/incorporated into expanded fibrotic portal regions or standing isolated and out of sync with the rest of the landmarks, either by being too close to or too distant from a portal region, in an altered macronodular landscape. The cirrhotic configuration is still discernible; however, the amount of collagen is much reduced.


   Role of Liver Biopsy and its Limitations Top


The liver biopsy has a pivotal role to play in the quantitative assessment of liver fibrosis, as it facilitates direct visualization of the amount and pattern of fibrous tissue deposited, along with any associated architectural or vascular changes. Traditionally, the diagnosis of NAFLD/NASH is established on light microscopic examination of 3 to 4 microns-thick tissue sections stained with hematoxylin and eosin (H&E), and a connective tissue stain, such as Masson trichrome (MT) or Picrosirius red (PSR). The liver should contain ≥5% of predominantly macrovesicular steatosis to merit a diagnosis of NAFLD. The tissue biopsy is then assessed for grading the severity of activity and staging of fibrosis, while also allowing the exclusion of any comorbidity and liver-related outcomes such as dysplasia or malignancy.

Alternative noninvasive methods of fibrosis assessment such as transient elastography currently still take reference from liver biopsy findings; as such, many clinical trials still rely on the liver biopsy as a reference tool for patient enrolment decisions and assessment of therapeutic efficacy. To date, histological endpoints are considered valid surrogates for clinical outcome, of which fibrosis is the major long-term histological prognostic criteria. In comparison, steatohepatitis is considered as the main trigger of fibrosis and thus, a valid surrogate to assess short-term prognosis. Resolution of NASH is thus the most reasonable histological endpoint attainable in the relatively short duration of regular trials, while regression of fibrosis would need a longer follow-up.

Limitations of liver biopsy

Besides the fact that liver biopsy is an invasive procedure with its attendant risks, sampling error and adequacy are two of the largest disadvantages with a liver core biopsy as it represents only about 1/50,000th of the organ. The situation is further aggravated if you consider that only several 3 to 4 microns-thick sections of the tissue core are routinely examined. Both the length and diameter of the cores must be sufficient for adequate assessment of portal tracts and central veins, with an ideal biopsy length of at least 25 mm obtained with a 16-gauge needle.[6],[7],[8],[9] Furthermore, suboptimal samples exacerbate the already well-recognized problem of inter- and intraobserver variability in histological scoring.[10] Central reads are therefore critical for standardization of scoring for clinical trials. Other technical factors that may influence the accuracy of histological assessment include the quality of the slide preparation and the crispness of the connective tissue stains employed. Despite all these technical limitations and the invasiveness of the procedure, the liver biopsy is still the mainstay in the study of NAFLD/NASH.


   Histological Scoring Systems Top


There are three commonly used histological scoring systems for assessing fibrosis in NAFLD, using MT or other connective tissue stains to highlight particularly the earliest stage of perisinusoidal fibrosis [Table 1]. The Brunt and NAS systems were intended to be used only after overall evaluation of the biopsy by a pathologist had established a diagnosis of NASH, while the FLIP algorithm/SAF scoring does attempt to help distinguish between NASH and non-NASH.[11] In 2011, Goodman et al.[12] compared the former 2 scoring systems and further proposed a more detailed assessment scheme of NASH and fibrosis; however, this scheme has not been widely utilized as yet.
Table 1: Comparison of histological fibrosis scoring systems

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The Brunt system was the first standardized histological assessment system proposed to categorize the morphologic features of NASH for grading the severity and staging the disease in adult patients.[13] Perisinusoidal/pericellular fibrosis was recognized as the earliest stage of fibrosis with subsequent progression to periportal fibrosis, bridging fibrosis, and finally cirrhosis.

A new semiquantitative scoring system applicable to both adults and children was developed and validated by the NIDDK sponsored Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) Pathology Committee.[10] The NASH CRN system requires only routine histochemical stains and is currently the most recognized system for scoring the range of histological features of NAFLD in clinical trials and experimental studies.[14],[15] The NAFLD activity score (NAS) is a summative score of steatosis, ballooning and lobular inflammation. Fibrosis is described separately using a 5-stage system (0 to 4), with further subclassification of stage 1 into substages 1a/1b for perisinusoidal fibrosis and 1c for portal fibrosis encountered in children.

The SAF scoring system, developed by the Fatty Liver: Inhibition of Progression (FLIP) European consortium, differs from the NASH CRN score by assessing separately the grade of steatosis (S0 to S3), grade of activity (A0 to A4) based on ballooning and lobular inflammation, and stage of fibrosis (F0 to F4).[16],[17] The fibrosis staging is similar to the NASH CRN system. Use of the FLIP algorithm helps to separate patients into NASH or not NASH, while the SAF score provides an overall snapshot of the patient's disease status.

The Goodman scheme was devised to correlate numerous histological features of NASH with liver-related mortality.[12] Each form of fibrosis (pericellular/perisinusoidal, portal, bridging and cirrhosis) were individually graded from none to many/established. Although a strong correlation between fibrosis and liver-related mortality was demonstrated, its validity for use in clinical trials is unknown.

Limitations of histological assessment scoring systems

Fibrosis deposition is actually a continuum; even with the Goodman scheme, the use of semiquantitative histological scoring systems as detailed above does not fully convey information regarding the extent of changes seen within each stage, nor the architectural/vascular alterations present.[18] Distinguishing between the intermediate stages of fibrosis is also challenging on liver biopsies.[19] The accuracy of the staging process relies heavily on sample adequacy to overcome inherent disease heterogeneity and pathologist judgment.

Furthermore, fibrosis is a dynamic process with progression and regression occurring simultaneously with parenchymal remodeling, even at the “end-stage”.[20] In recognition of this, Sun et al. proposed the Beijing classification for the categorization of advanced liver fibrosis into progressive, indeterminate and regressive states in CHB patients in order to predict clinical outcomes.[21],[22] This work was an expansion on the intra-stage concept of the Laennec staging system for 4A, B, and C cirrhosis. This classification would suffice for most forms of chronic liver disease with portal-based activity and fibrosis. However, this system has yet to be validated in NAFLD.

As fibrosis is a slow process, current scoring systems also do not provide sufficient granularity for assessing subtle changes during follow-up studies of shorter duration.[23] Traditional light microscopy simply displays the presence of collagen fibers via connective tissue stains; it does not disclose collagen fiber structure nor status of other cellular components that are now increasingly known to play an active role in fibrosis. It is also of note that the degree of perisinusoidal fibrosis is not captured in the higher stages of the 3 main scoring systems used. Severe perisinusoidal fibrosis may contribute to portal hypertension in the absence of advanced fibrosis. Measurement of perisinusoidal fibrosis at all stages (as suggested in the Goodman scheme) might improve efficacy results in NASH clinical trials.[24]


   Morphometric Analysis Top


Collagen proportionate area (CPA) measurement is the most commonly used morphometric approach that quantifies the amount of fibrous tissue present as a proportion of the total biopsy area, after editing out structural collagen (e.g., in liver capsule, large vessel walls, or hilum). This automated process employs digital image analysis on PSR-stained histological sections.[25] The utility of CPA has been validated against hepatic venous pressure gradient and clinical outcomes, mainly in CHC patients,[26],[27] but also recently as an independent predictor of long-term outcome in NAFLD.[28]

CPA provides a sensitive linear quantification of fibrosis capable of detecting small variations in the amount of collagen, which is especially useful in clinical trials that are often of relatively short duration.[29] However, one big drawback is that CPA is unable to evaluate spatial alteration in the lobular architecture such as bridging fibrosis and nodularity. For example, some cases of stage 3 fibrosis may have still relatively limited areas of bridging fibrosis despite the presence of altered architecture, potentially leading to an underestimation of the fibrosis stage if a linear relationship between Brunt fibrosis stage and CPA is assumed.[30] CPA measurement also does not provide any information on the dynamic nature of fibrosis or the other cellular components involved, and is still subject to the same potential sampling error that traditional histology suffers from, and to technical issues such as variances in staining procedure, operator experience, and imaging software used.


   Second Harmonic Generation (SHG) Microscopy Top


Recent advances in ultra-fast lasers have enabled imaging of optical signals such as, autofluorescence, from paraffin-embedded histological sections of biological samples without use of additional tissue stains. Laser imaging coupled with computer-assisted imaging analytics has thus provided a unique platform for the analysis of the combinations and permutations of the 4 key parameters of NASH, in particular fibrosis. This has opened up a whole new realm for re-establishing the integral role of the liver biopsy in the detection and quantitative assessment of liver fibrosis. These new technologies are not suited for establishing a de novo diagnosis of NASH but are rather adjunct tools to quantify disease severity. They are potentially useful for therapeutic trials but require large scale validation.

Second harmonic generation (SHG) microscopy is a nonlinear optical tissue imaging system that enables automated quantification of fibrosis based on the unique architectural features of collagen.[31],[32] Two photon excitation fluorescence (TPEF) allows visualization of the background liver through endogenous tissue signals.[33] SHG/TPEF microscopy therefore permits identification of individual collagen fibers, localization of collagen patterns in 2D and 3D formats, and quantification of their physical attributes such as number, length, diameter, orientation, contour, and alignment and cross-linkages of the collagen fibers with each other, presenting insights into the dynamic remodeling process that would be of potential pathomechanistic and pharmaceutical value [Figure 3].
Figure 3: Illustration of SHG/TPEF imaging on a liver biopsy with NASH CRN stage 1b fibrosis. Note the prominent perivenular perisinusoidal fibrosis in the space of Disse fanning out in the hepatic lobule

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Several proposed models based on SHG microscopy for quantifying liver fibrosis in NAFLD have been published in the past decade, starting with proof-of-concept studies to recent large-scale validation studies.[34],[35],[36],[37],[38],[39],[40] The three main models proposed (qFibrosis, q-FPs and SHG B-index) all utilize SHF/TPEF to assess collagen architectural changes along a continuous, quantitative scale in an automated fashion, while incorporating spatial architectural features of pathological relevance at tissue level; in other words, providing a more in-depth histological staging. The images captured by SHG/TPEF are assessed with image-analysis software/algorithms to characterize the collagen characteristics in different regions of the liver biopsy such as perisinusoidal, central vein, portal tract, or the entire tissue section. The different collagen parameters are then selected individually or combined into indices for comparison of performance against established histological grading systems.

qFibrosis was initially developed by Xu et al. and first validated on CHB biopsies, where it was demonstrated to reliably stage liver fibrosis with reduced variability of sampling error and inter- and intraobserver bias, outperforming both CPA and (hepato) pathologists.[39] One of its most valuable features was the detection of intra-stage changes in cirrhosis—crucial information for monitoring progression/regression and therapeutic response to antifibrotic drugs. Liu et al. modified the model to assess liver fibrosis in adult and pediatric NAFLD patients,[35] and then subsequently expanded and incorporated it into a new automated quantitative tool aptly termed qFIBS.[34] This computational algorithm quantifies the 4 key histopathological features of NASH, namely, fibrosis (qFibrosis), lobular inflammation (qInflammation), hepatocellular ballooning (qBallooning), and steatosis (qSteatosis). This model was validated in a multicenter cohort that included all age groups. The qFibrosis component in qFIBS is a prediction model based on 17 collagen parameters, with output as a numerical index from 0 and 6.55. Each qFIBS component strongly correlated with the corresponding NASH CRN component (p < 0.001); qFibrosis was able to accurately differentiate between fibrosis stages (Area under operating curve [AUC] 0.870-0.951).

Wang et al. also established an SHG-based quantification of fibrosis-related parameters (q-FPs) model in NAFLD in 2017.[37] Their initial principal component analysis model based on 16 q-FPs was able to differentiate subtle differences between fibrosis stages 1a, 1b, and 1c (NASH CRN system) and differences in zonal distribution of fibrosis in patients with cirrhosis. In a 2019 validation study, Wang et al. showed that q-FP was also highly accurate in assessment of different stages of fibrosis in NAFLD patients and correlated strongly with histological scoring and liver stiffness measurement.[38] 25 q-FPs had an AUC >0.90 for different fibrosis stages; perimeter of collagen fibers and number of long collagen fibers had the best accuracy (88.3%-96.2% sensitivity and 78.1%-91.1% specificity for different fibrosis stages).

SHG B-index is a based on a prediction model developed by Chang et al., comprising 14 unique SHG-based collagen parameters that correlated with severity of NAFLD fibrosis in a continuous fashion.[40] In the cross-validation analysis, the SHG B-index demonstrated high specificity for diagnosis of all stages of fibrosis (Brunt fibrosis stage) [AUC 0.853-0.985] and a high correlation of 0.820 with fibrosis stage (p < 0.001), although it was less discerning in discriminating between early stages of fibrosis.

All three models detailed above have demonstrated high AUC in discriminating between fibrosis stages in NAFLD patients. As current fibrosis staging systems tend to be disease-specific, the development and validation of these NAFLD-specific SHG algorithms has been a major advancement in paving the way for adoption of these models in clinical trials. The qFIBS model goes one step further by offering a comprehensive evaluation of steatosis, ballooning and lobular inflammation in addition to fibrosis. An objective quantitative assessment of fibrosis changes on a continuous scale determined by SHG-based models provides greater reflection of subtle nuances compared to a semiquantitative score of mere stage migration, without losing the spatial information integral to accurate fibrosis staging. These new models therefore show great promise in therapeutic trials for NASH that require demonstrable histological improvement in fibrosis as a hard efficacy endpoint. However, it must be cautioned that using SHG microscopy requires liver tissue samples and thus suffers the same issues of sampling variances and risks of an invasive biopsy procedure. It also requires specialized equipment, which may limit its utility in resource-poor areas or countries.


   Artificial Intelligence (AI)-Assisted Systems Top


Another pivotal development in the quantitative assessment of liver fibrosis on tissue biopsies is the advent of AI with deep learning-based algorithms developed through learning from large datasets of images. Current computer-assisted SHG/TPEF image analytics for liver fibrosis scoring are not fully automated since it requires manual segmentation and feature extraction based on liver pathology domain knowledge. Proof-of-concept studies have demonstrated the use of AI to develop fully automated and accurate detection and quantification of fibrosis. Yu et al.[41] established a fully automated algorithm using pre-trained AlexNet-Convolutional Neural Networks to automatically quantify liver fibrosis and score different stages of fibrosis (METAVIR scoring system) with high sensitivity and specificity in a Thioacetamide-induced fibrosis rat model. This approach can automatically score liver fibrosis stages with a level of accuracy similar to conventional non-deep learning-based algorithms but has yet to be validated in NAFLD patients.

The promise of AI is also to reduce the computational effort required for accurate fibrosis assessment. The model developed by Forlano et al. in NAFLD patients is touted to be fully automated, user-friendly and fast-operating, with quantification of steatosis, inflammation, ballooning and collagen occurring in 2 minutes.[42] This high-throughput, machine learning-based algorithm was devised to analyze images from the liver biopsy and compute percentages of each feature as well as CPA.

Recently, an integrated AI-based automated tool to detect and quantify liver fibrosis and assess its architectural pattern in NAFLD liver biopsies was also developed by Gawrieh et al.[43] Digital images of MT-stained slides were used to calculate CPA, stage fibrosis, and establish six fibrosis patterns. There was good to excellent correlation between CPA and the pathologist semiquantitative fibrosis staging, although there was considerable overlap in the CPA across different stages. The model's AUC was 78.6% for detection of periportal fibrosis, 83.3% for pericellular fibrosis, 86.4% for portal fibrosis, and more than 90% for normal fibrosis, bridging fibrosis, and presence of nodule/cirrhosis.

Further validation of these models in larger patient cohorts, as well as the integration of AI with SHG-based microscopy, is eagerly awaited.

Investigating other biopsy-based contributors to fibrosis

Fibrosis is a mixture of various ECM proteins and glycoproteins, the deposition of which is mediated by the activation of HSCs during liver injury and inflammation, in conjunction with endothelial cells, Kupffer cell infiltration and activation, and secretion of other inflammatory molecules. Quantitative measurement of these other components, such as elastin[30],[44] and activated HSCs (detected with antibodies against α-smooth muscle cells),[45],[46] have been attempted to determine their relationship with the various stages of fibrosis in NAFLD and discover their predictive value in fibrosis progression/regression. In a bid to provide a more dynamic assessment of fibrosis, Decaris et al.[47] also ventured to quantify collagen fractional synthesis rate within liver tissue and in blood using tandem mass spectrometry. However, none of these methods are as of yet widely adopted.


   Future Directions Top


There are still many areas for exploration in NAFLD/NASH. The pathogenesis of the disease and mechanisms responsible for liver fibrosis and cirrhosis remodeling are still unclear, leading to questions regarding the current hard efficacy endpoints in NASH clinical trials of absence of ballooning for NAS resolution and at least 1-point decrease in fibrosis stage based on current histological scoring systems. Much work needs to be done not only on liver fibrosis but concurrently on the relationship with the other components of NASH as disease activity drives fibrosis.

At present, histological assessment of severity at baseline and subsequent changes in response to intervention as assessed by expert pathologists remains an essential endpoint in phase II/III clinical trials for NASH. However, SHG-based studies have illustrated that current staging systems do not capture the full spectrum of fibrosis in NASH, which may partly account for the failure of NASH clinical trials based on current histological endpoints. SHG-based tools show promise as a more standardized, accurate and precise approach to staging NASH, by detecting subtle nuances and giving weightage to the amount/density of collagen deposition. Intuitive quantitative assessment of changes on continuous scale should provide a more tractable and sensitive reference to analysis of progression or regression of fibrosis in NASH. The severity of perisinusoidal fibrosis can and also should be captured at all stages.

So far, none of the noninvasive tools can replace liver biopsy for the evaluation of the various histological patterns of disease, their severity, and their associations in NAFLD in clinical practice and clinical trials. Bedossa has reiterated that the liver biopsy may still be indicated in the future to select the most relevant personalized treatment based on the dominant histological features of disease.[48] Wanless in a recent editorial also stated that “quantitative biopsy assessment using SHG-microscopy should be considered the new gold standard for the measurement of liver fibrosis”.[49] With the aid of new techniques and methods of assessment, the liver biopsy thus remains integral to deepening our understanding of the pathogenesis of NAFLD and monitoring the disease trajectory in clinical practice and clinical trials.

Acknowledgments

The authors would like to Dr Dean Tai of HistoIndex for contributing some of the microscopy images.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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Correspondence Address:
Aileen Wee
Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, National University Hospital, 5 Lower Kent Ridge Road, 119074
Singapore
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJPM.IJPM_947_20

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    Abstract
   Introduction
    Evolution and Pa...
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