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A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease
Maria Jimenez Ramosa, Timothy J. Kendalla,b, Ignat Drozdovc, Jonathan A. Fallowfielda,
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Jonathan.Fallowfield@ed.ac.uk

Corresponding author.
a Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
b Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
c Bering Limited, 54 Portland Place, London, W1B 1DY, UK
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="cesectitle0004">Introduction</span><p id="para0001" class="elsevierStylePara elsevierViewall">Metabolic dysfunction-associated steatotic liver disease &#40;MASLD&#41;&#44; previously termed non-alcoholic fatty liver disease &#40;NAFLD&#41;&#44; is characterized by the presence of liver steatosis and at least one of the five cardiometabolic criteria proposed in a multi-society Delphi consensus statement <a class="elsevierStyleCrossRef" href="#bib0001">&#91;1&#93;</a>&#46; Importantly&#44; other causes of steatosis&#44; including increased alcohol intake&#44; must be absent&#46; Metabolic dysfunction-associated steatohepatitis &#40;MASH&#41;&#44; previously termed non-alcoholic steatohepatitis &#40;NASH&#41;&#44; is the progressive stage of the disease distinguished by the presence of lobular inflammation&#44; hepatocyte ballooning&#44; and an increased risk of liver fibrosis&#46; In some instances&#44; fibrosis progression can lead to cirrhosis and the development of hepatocellular carcinoma &#40;HCC&#41;&#46; The presence of certain genetic variants&#44; such as single nucleotide polymorphisms in patatin-like phospholipase domain-containing protein 3 &#40;PNPLA3&#41;&#44; hydroxysteroid 17&#946; dehydrogenase 13 &#40;HSD17B13&#41;&#44; or transmembrane 6 superfamily member 2 &#40;TM6SF2&#41; genes has also been associated with an increased risk of MASLD development&#44; progression&#44; and unfavorable prognosis <a class="elsevierStyleCrossRefs" href="#bib0002">&#91;2&#8211;4&#93;</a>&#46; Currently&#44; MASLD represents the main cause of chronic liver disease and the leading indication for liver transplantation&#44; affecting &#8764;30&#37; of the global population <a class="elsevierStyleCrossRef" href="#bib0005">&#91;5&#93;</a>&#46; Epidemiological modeling predicts a substantial increase in prevalence&#44; clinical burden&#44; and socioeconomic costs in the coming years &#8211; a public health threat that no country appears well prepared to address <a class="elsevierStyleCrossRef" href="#bib0006">&#91;6&#93;</a>&#46;</p><p id="para0002" class="elsevierStylePara elsevierViewall">Crucially&#44; the severity of fibrosis in MASLD is strongly associated with an increased risk of overall and disease-specific morbidity and mortality <a class="elsevierStyleCrossRef" href="#bib0007">&#91;7&#93;</a>&#46; The most common cause of death in people with MASLD is cardiovascular disease&#44; followed by extra-hepatic malignancy&#44; then liver-related mortality &#91;<a class="elsevierStyleCrossRef" href="#bib0008">8</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0009">9</a>&#93;&#46; These findings reflect the range of comorbidities in MASLD and highlight the need for a multidisciplinary approach to the disease <a class="elsevierStyleCrossRef" href="#bib0001">&#91;1&#93;</a>&#46;</p><p id="para0003" class="elsevierStylePara elsevierViewall">Despite substantial advances in our understanding of disease pathogenesis&#44; there are still no approved therapies for MASLD&#44; and many drugs have shown limited efficacy in clinical trials&#44; especially in patients with cirrhosis <a class="elsevierStyleCrossRef" href="#bib0010">&#91;10&#93;</a>&#46; Given the complexity of disease pathogenesis&#44; combination drug therapy may be required to improve patient outcomes <a class="elsevierStyleCrossRef" href="#bib0011">&#91;11&#93;</a>&#44; but the optimal combinations or treatment regimens are unknown&#46; Additionally&#44; there is an unmet need for non-invasive biomarkers to accurately diagnose&#44; stage&#44; and monitor the progression of MASLD and reduce or obviate the necessity for a liver biopsy in clinical practice and pharmacological studies&#46; Moreover&#44; the heterogeneity in progression and prognosis of MASLD calls for novel approaches to disease stratification and prediction of individual risk of clinical outcomes&#59; this may require a re-evaluation of MASLD&#44; viewed through the prism of the new nomenclature&#44; with integration of multimodal information including demographic&#44; pathological&#44; genetic&#47;multi-omic&#44; environmental&#44; and electronic health record &#40;EHR&#41; data to understand patient trajectories and define discrete subphenotypes to enable precision medicine in MASLD <a class="elsevierStyleCrossRef" href="#bib0012">&#91;12&#93;</a>&#46;</p><p id="para0004" class="elsevierStylePara elsevierViewall">In this review&#44; we discuss how large-scale patient data and emerging artificial intelligence &#40;AI&#41; approaches are increasingly being leveraged in the MASLD field&#44; in the quest for new diagnostic biomarkers&#44; efficacious drug targets&#44; and improved patient stratification and prognostication methods&#46; A national-level multimodal database &#8211; SteatoSITE <a class="elsevierStyleCrossRef" href="#bib0013">&#91;13&#93;</a> &#8211; is used as an exemplar to demonstrate the utility and scope of an integrated data-driven approach&#44; to highlight the technical challenges&#44; and to illustrate possible future directions&#46;</p></span><span id="sec0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="cesectitle0005">Big data classes and their utility in MASLD research</span><p id="para0005" class="elsevierStylePara elsevierViewall">AI is a large and rapidly growing field&#44; using computer software that mimics human cognitive abilities to perform complex tasks&#46; Machine Learning &#40;ML&#41; is an application of AI that enables computers to learn and recognize patterns from data to make decisions and predictions &#40;<a class="elsevierStyleCrossRef" href="#fig0001">Fig&#46; 1</a>&#41;&#46; The two broad categories of ML algorithms are&#58; supervised &#40;the computer learns from both input data and corresponding correct answers&#41; and unsupervised &#40;the computer only processes input data&#41;&#46; Their main advantage is that they can recognize unique data patterns and include multiple components to create new disease classifications and predictive models through linkage to outcomes <a class="elsevierStyleCrossRef" href="#bib0014">&#91;14&#93;</a>&#46; AI&#47;ML applications in liver disease research has increased in recent years&#44; including in studies of MASLD to address the challenges of pathophysiological complexity and heterogeneity of presentation and patient outcomes&#46;</p><elsevierMultimedia ident="fig0001"></elsevierMultimedia><span id="sec0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="cesectitle0006">Electronic health record data</span><p id="para0006" class="elsevierStylePara elsevierViewall">Electronic Health Records &#40;EHRs&#41; are digital repositories of comprehensive patient health information&#44; stored in standardized formats for efficient retrieval and sharing among healthcare providers&#46; In both the United States &#40;US&#41; and the European Union&#44; the adoption of EHRs has become nearly ubiquitous in both acute hospital and primary care settings <a class="elsevierStyleCrossRef" href="#bib0015">&#91;15&#93;</a>&#46; EHR systems typically encompass administrative and healthcare utilization data&#44; demographic details&#44; diagnostic and procedural codes&#44; laboratory results&#44; pathology assessments&#44; and prescribed medications&#46;</p><p id="para0007" class="elsevierStylePara elsevierViewall">The increased accessibility of EHRs for research has opened new avenues for large-scale observational studies and the application of AI&#47;ML in MASLD&#44; especially for predicting the risk of MASLD development or refining its diagnosis <a class="elsevierStyleCrossRefs" href="#bib0016">&#91;16&#8211;21&#93;</a>&#46; For example&#44; Fialoke <span class="elsevierStyleItalic">et al</span>&#46; used one of the largest US-based EHR resources &#40;from Optum Analytics&#41;&#44; which integrates healthcare data from 50 provider organizations treating more than 80 million patients&#44; for a supervised ML classification of MASLD patients to predict the health status of the patient cohort&#46; The inclusion of time-stamped data also facilitates longitudinal profiling of candidate biomarkers and the identification of potential predictor variables associated with clinical outcomes&#46; Typically&#44; EHR data is characterized by noisy&#44; sparse&#44; and irregularly timed observations&#44; which poses a challenge for phenotype discovery in clinical data&#44; although computational ingenuity can overcome this <a class="elsevierStyleCrossRefs" href="#bib0022">&#91;22&#8211;24&#93;</a>&#46; To date&#44; there are very few AI&#47;ML-based studies in MASLD that have leveraged temporally defined EHR data to gain insights into disease progression or prognosis&#46; Vandrome <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0025">&#91;25&#93;</a> used data mining techniques to search for MASLD subtypes in a hospital database cohort of 13&#44;290 patients&#44; identified using electronic signatures of the disease&#46; Using hierarchical clustering&#44; they identified five distinct subtypes of patients&#46; Notably&#44; two of the major groups exhibited fewer comorbidities and favorable outcomes&#44; whereas a minority within the three smaller subtypes displayed more severe comorbidities and poorer outcomes&#46;</p></span><span id="sec0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="cesectitle0007">&#8216;Omics data</span><p id="para0008" class="elsevierStylePara elsevierViewall">While EHR-based studies involve a substantial number of patients&#44; none have integrated &#39;omics data to identify potential disease signatures for patient prediction and stratification&#46; Despite this gap&#44; several smaller studies have made efforts to address the issue&#46;</p><p id="para0009" class="elsevierStylePara elsevierViewall">Utilizing datasets from the Gene Expression Omnibus &#40;GEO&#41; <a class="elsevierStyleCrossRef" href="#bib0026">&#91;26&#93;</a>&#44; some researchers have conducted differential gene expression analyses&#44; followed by network analysis and the application of ML algorithms&#46; This methodology has enabled the identification of parsimonious gene signatures with a good Area Under Receiver Operating Characteristic Curve &#40;AUROC&#41; for the diagnosis of MASLD &#91;<a class="elsevierStyleCrossRef" href="#bib0027">27</a>&#44;<a class="elsevierStyleCrossRef" href="#bib0028">28</a>&#93;&#46; Sen <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0029">&#91;29&#93;</a> employed transcriptomics of whole liver tissue and serum metabolomics from a cohort based on genome-scale metabolic models to identify dysregulated glycosphingolipid pathways across the disease spectrum&#46; In the study by Luo <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0030">&#91;30&#93;</a> the focus was on identifying serum biomarkers associated with liver fibrosis in patients with MASH&#46; Although they identified key proteins linked to fibrosis and liver injury&#44; they were unable to establish a protein panel capable of distinguishing between early and late fibrosis&#46;</p><p id="para0010" class="elsevierStylePara elsevierViewall">The investigation of interactions between MASH and other diseases has yielded notable findings&#46; Qian <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0031">&#91;31&#93;</a> defined a 20-gene signature predicting fibrosis progression in MASLD and HCV patients over five years&#44; validated with an AUROC of 0&#46;86&#46; They also identified potential antifibrotic drug candidates and BCL2 as a therapeutic target&#46; Additionally&#44; Fujiwara <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0032">&#91;32&#93;</a> developed a 133-gene signature for MASLD patients developing HCC&#44; validated in a separate HCC cohort&#44; and converted into a four-parameter blood-based panel &#40;comprising XCL1&#44; GRN&#44; ANGPT2&#44; and MET&#41;&#46;</p><p id="para0011" class="elsevierStylePara elsevierViewall">More advanced models have also been explored&#46; Conway <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0033">&#91;33&#93;</a> utilized ML on clinical trial data &#40;STELLAR 3 and 4&#41; to establish a prognostic five-gene signature predicting progression to cirrhosis and liver-related events in MASH patients&#44; correlating with histological features&#46; Deep learning &#40;DL&#41; was also investigated&#44; outperforming other algorithms with an AUROC &#62;0&#46;80 in identifying genes associated with MASL to MASH progression <a class="elsevierStyleCrossRef" href="#bib0034">&#91;34&#93;</a>&#46; Among the final 39 candidates identified&#44; 11 were linked to HCC and survival rate&#46;</p></span><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3</span><span class="elsevierStyleSectionTitle" id="cesectitle0008">Imaging data</span><p id="para0012" class="elsevierStylePara elsevierViewall">Non-invasive imaging techniques have been employed in MASLD research and clinical settings&#46; Advanced magnetic resonance imaging &#40;MRI&#41;&#44; including proton-density fat fraction &#40;PDFF&#41; and MR elastography &#40;MRE&#41;&#44; facilitates accurate quantification of steatosis and fibrosis for MASH assessment <a class="elsevierStyleCrossRef" href="#bib0035">&#91;35&#93;</a>&#46; Recent applications of supervised ML and DL in medical imaging enhance automation&#44; enabling more precise diagnosis&#46; Training these models can unveil abnormal patterns beyond human perception&#44; enhancing the efficiency of non-invasive diagnostic procedures&#46; Studies have utilized ML to predict MRE liver stiffness&#44; achieving an AUROC of 0&#46;84 when combined with clinical data <a class="elsevierStyleCrossRef" href="#bib0036">&#91;36&#93;</a>&#46; In a study by Schawkat <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0037">&#91;37&#93;</a> MRI was employed to explore the viability of assessing liver scarring by integrating texture analysis&#44; a method for extracting information from grey-level intensity within an image&#44; with a supervised ML algorithm&#46; Their results demonstrated a classification accuracy of 87&#46;7&#37;&#44; equivalent to the performance level of MRE&#46;</p><p id="para0013" class="elsevierStylePara elsevierViewall">AI has also been utilized in the analysis of computerized tomography &#40;CT&#41; scans&#44; which can measure liver fat content&#46; Currently&#44; there are no standardized approaches for manually delineating the region of interest &#40;ROI&#41;&#44; although some proposals exist&#44; as outlined in Starekova <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0038">&#91;38&#93;</a>&#46; AI can facilitate automated liver segmentation&#44; contributing to standardized CT analysis methods for MASLD patients&#46; Several studies have already achieved this&#44; demonstrating a robust and significant correlation <a class="elsevierStyleCrossRefs" href="#bib0039">&#91;39&#8211;41&#93;</a>&#46; Notably&#44; a semi-automated DL-augmented method has been used on MRI-acquired 3D liver images to facilitate modeling of resectional surgery for liver cancer <a class="elsevierStyleCrossRef" href="#bib0042">&#91;42&#93;</a>&#46;</p><p id="para0014" class="elsevierStylePara elsevierViewall">Liver ultrasound scans are a standard non-invasive diagnostic tool for chronic liver diseases&#44; including MASLD&#44; but are influenced by examiner subjectivity and exhibit reduced sensitivity when the liver contains less than 20&#8211;30&#37; fat <a class="elsevierStyleCrossRef" href="#bib0043">&#91;43&#93;</a>&#46; Limited studies on AI&#39;s application for predicting and classifying MASLD patients indicate promising results with excellent AUROC scores <a class="elsevierStyleCrossRefs" href="#bib0044">&#91;44&#8211;46&#93;</a>&#46; Additionally&#44; ML algorithms integrated with transient elastography &#40;TE&#41; have been employed to predict liver fibrosis and MASLD in large clinical trial&#47;cohort studies <a class="elsevierStyleCrossRefs" href="#bib0047">&#91;47&#8211;49&#93;</a>&#46;</p></span><span id="sec0006" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;4</span><span class="elsevierStyleSectionTitle" id="cesectitle0009">Digital pathology data</span><p id="para0015" class="elsevierStylePara elsevierViewall">Despite these promising results&#44; the gold standard for diagnosis of MASLD and MASH requires a liver biopsy where steatosis&#44; inflammation&#44; hepatocyte ballooning&#44; and fibrosis are assessed&#46; Whilst a clinical histopathological diagnosis is made by a pathologist integrating all histological features&#44; in a research setting there are two main systems for ordinal scoring of the cardinal histological features&#46; Features of disease activity can be evaluated with the NAFLD Activity Score &#40;NAS&#41; and the stage scored using the NASH Clinical Research Network &#40;CRN&#41; system <a class="elsevierStyleCrossRef" href="#bib0050">&#91;50&#93;</a>&#44; or disease activity assessed using the SAF &#40;steatosis&#44; activity&#44; and fibrosis&#41; system that scores ballooning and inflammation using different criteria but incorporates the same NASH-CRN stage&#46; The architects of the NAS system explicitly state that a NAS score should not be used to define a diagnosis of steatohepatitis&#44; although a NAS&#8805;4 is often erroneously used for such a purpose&#46; A system based upon score assignment by an observer is inherently subjective with inter- and intra-observer variation&#46; To make the assignment of disease activity or stage scores more reproducible&#44; AI methodologies are being developed to automate feature scoring&#46;</p><p id="para0016" class="elsevierStylePara elsevierViewall">HistoIndex &#40;<a href="https://www.histoindex.com/">https&#58;&#47;&#47;www&#46;histoindex&#46;com&#47;</a>&#41; uses second harmonic generation &#40;SHG&#41; and two-photon excitation &#40;TPE&#41; microscopy with AI analysis to undertake histological assessment of unstained tissue sections <a class="elsevierStyleCrossRef" href="#bib0051">&#91;51&#93;</a>&#46; Computationally derived qFIBS scores <a class="elsevierStyleCrossRef" href="#bib0052">&#91;52&#93;</a>&#44; that are analogous to the pathologist-assigned NAS components and NASH-CRN stage&#44; can be generated&#44; and this tool was used in an international multicentre study to assess lobular inflammation&#44; steatosis&#44; fibrosis&#44; and hepatocyte ballooning&#46; qFIBS had a strong correlation with each component of NAS &#40;<span class="elsevierStyleItalic">P</span> &#60; 0&#46;001&#41; and had an AUROC between 0&#46;82 and 0&#46;986 for each component&#46;</p><p id="para0017" class="elsevierStylePara elsevierViewall">PathAI &#40;<a href="https://www.pathai.com">https&#58;&#47;&#47;www&#46;pathai&#46;com</a>&#41; has developed a ML model that uses the digital images of biopsies for automated and quantitative assessment of a disease&#46; The team used DL to predict NAS and fibrosis across three different clinical trials of advanced MASH <a class="elsevierStyleCrossRef" href="#bib0053">&#91;53&#93;</a>&#46; Their findings revealed a significant correlation between the NAS scores and fibrosis and their ML model&#46; Additionally&#44; they also developed a new metric called Deep Learning Treatment Assessment &#40;DELTA&#41; Liver Fibrosis Score&#44; designed to capture the change in fibrosis patterns from before to after the implementation of a treatment&#46;</p><p id="para0018" class="elsevierStylePara elsevierViewall">MorphoQuant<span class="elsevierStyleSup">TM</span> &#40;<a href="https://biocellvia.com/">https&#58;&#47;&#47;biocellvia&#46;com&#47;</a>&#41; also uses standard stained sections from biopsies to quantify the collagen fibres&#44; as well as the perivascular and septal percentage of collagen&#46; It is AI-based and relies on morphometric recognition and no training is required&#46; MorphoQuant<span class="elsevierStyleSup">TM</span> successfully quantified macrosteatosis&#44; inflammation&#44; and fibrosis in an automated manner in a mouse MASH model <a class="elsevierStyleCrossRef" href="#bib0054">&#91;54&#93;</a>&#46;</p><p id="para0019" class="elsevierStylePara elsevierViewall">While the models described above used hematoxylin and eosin &#40;H&#38;E&#41;-stained sections or unstained slides&#44; PharmaNest &#40;<a href="https://www.fibronest.com">https&#58;&#47;&#47;www&#46;fibronest&#46;com</a>&#41; developed the FibroNest AI algorithm&#44; capable of analysing many different tinctorial stains to automatically quantify fibrosis and inflammation&#46; Specifically&#44; it can quantify collagen amount&#44; structure&#44; and the morphometric traits of their fibres&#44; thereby providing a complete evaluation of fibrosis&#46; They successfully predicted the development of HCC from MASLD through histopathology imaging studies <a class="elsevierStyleCrossRef" href="#bib0055">&#91;55&#93;</a>&#46; Moreover&#44; they also used their AI tool to assess fibrosis in a mouse study evaluating semaglutide <a class="elsevierStyleCrossRef" href="#bib0056">&#91;56&#93;</a>&#46; Despite not observing a significant change in total fibrosis&#44; their AI-based system revealed an amelioration of the collagen network architecture after treatment&#46; While the total area of collagen remained unaltered&#44; the treatment prevented its further accumulation&#46;</p><p id="para0020" class="elsevierStylePara elsevierViewall">In addition to tools to computationally replicate subjective ordinal feature scoring&#44; methods have been developed to quantify features with continuous metrics&#46; The earliest application of digital pathology in this area was the quantification of scarring in stained sections using simple colour thresholding <a class="elsevierStyleCrossRef" href="#bib0057">&#91;57&#93;</a>&#44; and AI-based classifiers have subsequently been developed to undertake the same task and provide a metric that complements the ordinal scar staging&#46; Such classifiers are relatively easy to develop using open-source tools and have therefore been developed and used in a study-specific manner <a class="elsevierStyleCrossRef" href="#bib0013">&#91;13&#93;</a> that limits their generalizability and widespread application&#46;</p><p id="para0021" class="elsevierStylePara elsevierViewall">These studies show the importance of AI in enabling the stratification and automated quantification of key histopathological parameters in the diagnosis of MASLD&#46; However&#44; to maximize value it is important that such data is integrated with other diverse data sources&#44; including EHRs&#44; laboratory results&#44; and genomic &#40;and other &#8216;omics&#41; profiles&#46; There are several initiatives that are currently creating resources that store and analyze multimodal&#44; multiscale information to elucidate new patient subphenotypes&#44; identify new biomarkers and therapeutic targets&#46;</p></span></span><span id="sec0007" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="cesectitle0010">Academia-industry research consortia in MASLD</span><p id="para0022" class="elsevierStylePara elsevierViewall">AI-based approaches to understanding complex diseases are enabled by accessible large-scale multimodal datasets&#46; The Foundation for the National Institute of Health &#40;FNIH&#41; initiative Non-invasive Biomarkers of Metabolic Liver Disease &#40;NIMBLE&#41; is a multi-stakeholder project to support regulatory approval of MASH-related biomarkers <a class="elsevierStyleCrossRef" href="#bib0058">&#91;58&#93;</a>&#46; The diagnostic performance of five blood-based panels was evaluated in an observational cohort &#40;<span class="elsevierStyleItalic">n</span> &#61; 1073&#41; covering the full spectrum of MASLD <a class="elsevierStyleCrossRef" href="#bib0059">&#91;59&#93;</a>&#46; Multiple biomarkers met prespecified performance metrics&#46; NIS4&#174; had an AUROC of 0&#46;81 for &#8216;at-risk&#8217; MASH &#40;steatohepatitis and fibrosis stage &#8805;F2&#41;&#46; The AUROCs of the ELF<span class="elsevierStyleSup">TM</span> test&#44; PROC3&#44; and FibroMeter VCTE<span class="elsevierStyleSup">TM</span> for clinically significant fibrosis &#40;&#8805;F2&#41;&#44; advanced fibrosis &#40;&#8805;F3&#41;&#44; or cirrhosis &#40;F4&#41;&#44; respectively&#44; were all &#8805;0&#46;8&#46;</p><p id="para0023" class="elsevierStylePara elsevierViewall">The Liver Investigation&#58; Testing Marker Utility in Steatohepatitis &#40;LITMUS&#41; consortium&#44; supported by the European NAFLD registry <a class="elsevierStyleCrossRef" href="#bib0060">&#91;60&#93;</a>&#44; aims to develop&#44; validate&#44; and progress biomarkers for diagnosing&#44; risk stratifying&#44; and monitoring MASLD&#47;MASH progression and fibrosis stage&#46; The initiative involves a collaborative effort among end-users &#40;clinicians and the pharmaceutical industry&#41;&#44; independent academics specializing in medical test evaluation&#44; and biomarker researchers and developers from academic or commercial backgrounds&#46; Leveraging large-scale patient cohorts&#44; bioresources and multi-omics datasets&#46; The goal is to establish a definitive and impartial evaluation platform for these biomarkers&#46; The LITMUS investigators developed prediction models using supervised ML techniques&#44; that improved the detection of MASH and at-risk MASH <a class="elsevierStyleCrossRef" href="#bib0061">&#91;61&#93;</a>&#46; They also created a proteo-transcriptomic map of MASLD signatures and generated a composite model comprising four proteins &#40;ADAMTSL2&#44; AKR1B10&#44; CFHR4 and TREM2&#41;&#44; body mass index and type 2 diabetes mellitus status to identify at-risk steatohepatitis <a class="elsevierStyleCrossRef" href="#bib0062">&#91;62&#93;</a>&#46; LITMUS has recently added an imaging study where they will evaluate different MRI and elastography modalities against liver histology in MASLD <a class="elsevierStyleCrossRef" href="#bib0063">&#91;63&#93;</a>&#46;</p><p id="para0024" class="elsevierStylePara elsevierViewall">TARGET-NASH&#44; a longitudinal observational study&#44; tracks patients under usual clinical care for MASLD&#47;MASH in both academic and community settings <a class="elsevierStyleCrossRef" href="#bib0064">&#91;64&#93;</a>&#46; The dataset is essential for establishing a baseline and assessing the impact of current practice guidelines&#44; management&#44; and new therapies on patients with various medical outcomes&#46; The study&#39;s unique design&#44; involving three years of retrospective analysis of MASH patients followed by at least five years of prospective enrolment&#44; enables a comprehensive understanding of the disease&#39;s natural history&#46; The TARGET-NASH cohort has allowed the validation of a clinical risk-based classification system <a class="elsevierStyleCrossRef" href="#bib0065">&#91;65&#93;</a>&#44; among other studies <a class="elsevierStyleCrossRefs" href="#bib0066">&#91;66&#8211;68&#93;</a>&#46;</p></span><span id="sec0008" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="cesectitle0011">SteatoSITE</span><p id="para0025" class="elsevierStylePara elsevierViewall">The aforementioned consortia have compiled large multicentric prospective datasets&#46; However&#44; this presents potential disadvantages&#44; including selection bias&#44; loss to follow-up&#44; and long duration to accumulate clinical outcomes&#46; In contrast&#44; SteatoSITE &#40;<a href="https://www.steatosite.com">https&#58;&#47;&#47;www&#46;steatosite&#46;com</a>&#41; is a retrospective&#44; multimodal MASLD database &#40;<a class="elsevierStyleCrossRef" href="#fig0002">Fig&#46; 2</a>&#41; <a class="elsevierStyleCrossRef" href="#bib0013">&#91;13&#93;</a>&#46; SteatoSITE includes curated whole-slide images of H&#38;E and picro-sirius red-stained liver sections&#44; accompanying histological assessments &#40;NAS&#44; SAF&#44; NASH-CRN&#44; collagen &#37; area&#41;&#44; bulk hepatic RNA-sequencing &#40;RNA-seq&#41;&#44; and rich EHR data from a cohort of <span class="elsevierStyleItalic">n</span> &#61; 940 adult patients who had previously undergone either needle biopsy &#40;<span class="elsevierStyleItalic">n</span> &#61; 659&#41;&#44; explant &#40;<span class="elsevierStyleItalic">n</span> &#61; 56&#41; or liver resection &#40;<span class="elsevierStyleItalic">n</span> &#61; 225&#41; between January 2000 and October 2019&#46; Cases across the whole MASLD spectrum were identified from three of the four NHS Scotland Biorepositories &#40;Lothian&#44; Greater Glasgow &#38; Clyde&#44; and Grampian&#41;&#44; representing 12 of the 14 territorial Health Boards&#46; Covering a span of ten years before the tissue sampling date until May 2020&#44; the dataset encompasses over 5&#46;67 million days &#40;&#8764;15&#44;547 years&#41; of comprehensive routine clinical information derived from EHRs &#40;including demographic data&#44; International Classification of Diseases &#40;ICD&#41;-9&#47;10 and OPCS Classification of Interventions and Procedures version 4 &#40;OPCS-4&#41; codes&#44; laboratory results&#44; and medication history&#41;&#46;</p><elsevierMultimedia ident="fig0002"></elsevierMultimedia><p id="para0026" class="elsevierStylePara elsevierViewall">SteatoSITE is a resource that can support multiple facets of MASLD research <a class="elsevierStyleCrossRef" href="#bib0013">&#91;13&#93;</a> and fulfils the FAIR attributes &#40;Findability&#44; Accessibility&#44; Interoperability&#44; and Reuse of digital assets&#41; that underpin a &#8216;data commons&#8217; <a class="elsevierStyleCrossRef" href="#bib0069">&#91;69&#93;</a>&#46; One research avenue is the use of the extensive histopathological dataset linked to patient outcomes to develop new AI-augmented digital pathology tools for MASLD&#47;MASH&#46; Using training and validation sets derived from the SteatoSITE cohort&#44; new risk prediction indices derived from SHG&#47;TPE imaging features were shown to predict all-cause mortality&#44; decompensation events&#44; and HCC&#44; outperforming both NASH-CRN and qFibrosis ordinal staging <a class="elsevierStyleCrossRef" href="#bib0070">&#91;70&#93;</a>&#46;</p><p id="para0027" class="elsevierStylePara elsevierViewall">Additionally&#44; analysis of the SteatoSITE bulk RNA-seq data has enabled the discovery of molecular features linked to outcomes&#46; In Kendall <span class="elsevierStyleItalic">et al</span>&#46; <a class="elsevierStyleCrossRef" href="#bib0013">&#91;13&#93;</a>&#44; a 15-gene transcriptional risk score &#40;TRS&#41; was associated with a higher risk of developing decompensation events in advanced MASLD&#46; Moreover&#44; six of the 15 genes are predicted by bioinformatics to translate into secretome markers&#46; The TRS was also used to investigate transcriptional regulatory networks in MASLD&#46; Three regulons &#40;gene networks controlled by AE binding protein 1 &#40;AEBP1&#41;&#44; thyroid hormone receptor beta &#40;THRB&#41;&#44; and basonuclin zinc finger protein 2 &#40;BNC2&#41;&#41; exhibited significantly higher counts of TRS genes than anticipated by chance&#46; This suggests that these three networks might play a crucial role in the progression of MASLD&#46; Of particular interest&#44; given recent encouraging data on the THRB agonist resmetirom <a class="elsevierStyleCrossRef" href="#bib0071">&#91;71&#93;</a>&#44; THRB regulon activity not only decreased with advancing fibrosis stage but also predicted future hepatic decompensation &#40;beyond standard fibrosis scoring&#41;&#46;</p><p id="para0028" class="elsevierStylePara elsevierViewall">SteatoSITE was also used to perform deconvolution of the bulk RNA-seq using a published single-cell RNA-seq &#40;scRNA-seq&#41; reference dataset from healthy and cirrhotic patients <a class="elsevierStyleCrossRef" href="#bib0072">&#91;72&#93;</a>&#44; to estimate cell proportions in MASLD and correlate specific cell subpopulations with clinical outcomes&#46; Interestingly&#44; hepatic scar-associated macrophages &#40;SAMacs&#41; strongly correlated with fibrosis severity and were predictive for all-cause mortality and hepatic decompensation events&#46; Conversely&#44; more homeostatic liver resident cell types such as liver sinusoidal endothelial cells and vascular smooth muscle cells were protective against future mortality or hepatic decompensation&#46;</p><p id="para0029" class="elsevierStylePara elsevierViewall">Derived from a Scottish population with a high prevalence of MASLD and liver-related deaths <a class="elsevierStyleCrossRef" href="#bib0073">&#91;73&#93;</a>&#44; SteatoSITE is outcome-rich&#44; but also has some specific limitations including inherent spectrum bias and a lack of ethnic diversity&#46; Therefore&#44; compared to other cohorts&#44; SteatoSITE may be less suitable for modeling the population-level natural history of MASLD&#44; and caution is advised about the generalizability of findings to other geographical areas and ethnic populations&#46; Nevertheless&#44; SteatoSITE is currently a unique resource for broad research efforts in MASLD including patient stratification&#44; digital pathology methods&#44; biomarker <a class="elsevierStyleCrossRef" href="#bib0074">&#91;74&#93;</a> and drug target discovery&#46;</p></span><span id="sec0009" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="cesectitle0012">Technical challenges of using big data in MASLD research and practice</span><p id="para0030" class="elsevierStylePara elsevierViewall">The main technical challenges can be categorized into two domains&#58; those arising from using EHRs and those related specifically to AI&#46; Prominent EHR challenges include interoperability and usability&#46; Globally&#44; EHR systems have different clinical terminologies and technical specifications <a class="elsevierStyleCrossRef" href="#bib0075">&#91;75&#93;</a>&#44; which can create barriers when exchanging and using the data&#44; as both aspects need to be addressed to achieve true interoperability&#46; Additional factors hampering the use of EHRs for research purposes include human error &#40;e&#46;g&#46;&#44; incorrect data entry&#44; typographical errors&#44; sample mislabelling&#41;&#44; difficulties with data standardization&#44; errors arising from different delimiters or encoding in input files&#44; issues related to data formatting&#44; and instances of data duplication&#44; missing data or incompleteness&#46;</p><p id="para0031" class="elsevierStylePara elsevierViewall">Despite the promise of AI&#47;ML approaches in many aspects of MASLD research and clinical practice&#44; certain technical challenges and limitations should be acknowledged&#46; ML algorithms must undergo training to effectively identify patterns in the data&#46; This process is hindered by the notoriously large dimensionality of features in medical datasets&#44; referred to as the &#8220;curse of dimensionality&#8221;&#44; often resulting in suboptimal algorithm performance in independent studies and failure to generalize to clinical scenarios&#46; Additionally&#44; it is easy to ignore that all input data are generated within a non-stationary environment with shifting patient populations that &#8220;drift&#8221; away from original training data&#46; This phenomenon adversely affects algorithm performance and should be monitored and mitigated during live deployment&#46; Furthermore&#44; clinicians and pathologists&#44; with differing expertise&#44; contribute to the input data&#44; which may therefore exhibit discrepancies in features&#47;data for the model&#46; Variability in obtaining input data&#44; influenced by factors such as tissue quality&#44; experimental locations and equipment&#44; can contaminate feature selection and ground truthing and adversely impact model performance&#46; AI systems&#44; acting as black boxes &#40;with internal workings that are invisible to the researchers&#47;users&#41;&#44; can perpetuate biases that are challenging to detect&#44; such as hidden stratifications <a class="elsevierStyleCrossRef" href="#bib0076">&#91;76&#93;</a>&#46; Transparency is therefore crucial in publishing AI models for reliability&#44; reproducibility&#44; and diagnostic use&#46; Additionally&#44; although somewhat theoretical at present&#44; AI algorithms are susceptible to the risk of adversarial attack&#44; which describes an otherwise effective model that can be manipulated by the provision of inputs explicitly designed to fool it and to purposefully generate an incorrect prediction <a class="elsevierStyleCrossRef" href="#bib0077">&#91;77&#93;</a>&#46; Finally&#44; standardization and regulatory approval would be essential for future clinical utilization of these diverse algorithms and models in disease diagnosis and assessment&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6</span><span class="elsevierStyleSectionTitle" id="cesectitle0013">Future directions</span><p id="para0032" class="elsevierStylePara elsevierViewall">The incorporation of AI&#47;ML into MASLD research is swiftly advancing&#46; By leveraging appropriate tools and methodologies&#44; such as dimensionality reduction <a class="elsevierStyleCrossRef" href="#bib0078">&#91;78&#93;</a> and feature selection&#44; data scientists can extract valuable insights from the growing complexity of accessible datasets&#46; The assessment of liver histology using AI-augmented digital pathology tools is being assimilated into MASLD interventional trials&#44; where digital analyses might provide better reproducibility and greater insights into drug efficacy and mechanism of action than standard scoring methods <a class="elsevierStyleCrossRef" href="#bib0079">&#91;79&#93;</a>&#46; Moreover&#44; the integration of AI-digital pathology with spatially resolved &#8216;omics data and clinical outcomes could drive the development of new histopathological-based metrics and refined categorizations for the stratification and prognostication of MASLD&#46;</p><p id="para0033" class="elsevierStylePara elsevierViewall">Finally&#44; in the longer-term&#44; AI might be applied in various ways to enhance clinical trials in MASLD&#46; For example&#44; AI algorithms could analyze EHRs to pinpoint eligible patients for clinical trials&#44; improving patient recruitment efficiency&#44; or be used to predict patient responses to treatment&#44; helping in the selection of appropriate candidates for specific interventions&#46; In addition&#44; AI algorithms could continuously monitor patient data in real-time for early detection of adverse events&#44; enhancing participant safety during the trial&#46;</p></span><span id="sec0011" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0014">Funding</span><p id="para0034" class="elsevierStylePara elsevierViewall">The creation and initial analysis of SteatoSITE was funded by Innovate UK &#40;&#40;Precision medicine&#58; impacting through innovative technology &#40;Reference&#58; TS&#47;R017581&#47;1&#59; J&#46;A&#46;F&#46; and T&#46;J&#46;K&#46;&#41;&#41;&#44; Innovate UK Eureka &#40;Reference&#58; 105976&#59; J&#46;A&#46;F&#46;&#44; T&#46;J&#46;K&#46; and I&#46;D&#46;&#41;&#44; Guts UK Development Grant &#40;Reference&#58; DGO2019&#95;16&#59; J&#46;A&#46;F&#46; and T&#46;J&#46;K&#46;&#41;&#44; industrial Collaborative Awards in Science and Engineering &#40;iCASE&#41; PhD studentship funded by the Medical Research Council Precision Medicine Doctoral Training Programme &#40;Reference&#58; MR&#47;R01566X&#47;1&#59; M&#46;J&#46;R&#46;&#41; and Galecto Biotech &#40;M&#46;J&#46;-R&#46;&#41;&#46;</p></span></span>"
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              "titulo" => "Electronic health record data"
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          "titulo" => "Academia-industry research consortia in MASLD"
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            0 => "NAFLD"
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          "titulo" => "Abbreviations"
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            0 => "AI"
            1 => "AUROC"
            2 => "CT"
            3 => "DL"
            4 => "EHR&#40;s&#41;"
            5 => "HCC"
            6 => "ICD &#40;-9&#47;-10&#41;"
            7 => "LITMUS"
            8 => "MASLD"
            9 => "MASH"
            10 => "ML"
            11 => "MRI"
            12 => "MRE"
            13 => "NAFLD"
            14 => "NAS"
            15 => "NASH"
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        "resumen" => "<span id="abss0001" class="elsevierStyleSection elsevierViewall"><p id="spara003" class="elsevierStyleSimplePara elsevierViewall">Metabolic dysfunction-associated steatotic liver disease &#40;MASLD&#41;&#44; defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors&#44; is the most common cause of chronic liver disease worldwide&#44; affecting around one in three people&#46; Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable&#46; It&#44; therefore&#44; represents both a global public health threat and a precision medicine challenge&#46; Artificial intelligence &#40;AI&#41; is being investigated in MASLD to develop reproducible&#44; quantitative&#44; and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD&#46; This review details the different applications of AI and machine learning algorithms in MASLD&#44; particularly in analyzing electronic health record&#44; digital pathology&#44; and imaging data&#46; Additionally&#44; it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field&#46; Using a new national-level &#8216;data commons&#8217; &#40;SteatoSITE&#41; as an exemplar&#44; the opportunities&#44; as well as the technical challenges of large-scale databases in MASLD research&#44; are highlighted&#46;</p></span>"
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          "en" => "<p id="spara002" class="elsevierStyleSimplePara elsevierViewall">SteatoSITE Data Commons overview&#46; The right panel includes a schematic diagram in which horizontal lines represent individual patient timelines decorated with a variable amount of multimodal data preceding or following the date of liver tissue sampling &#40;time zero&#44; indicated by the vertical yellow line&#41;&#46; MASLD&#44; metabolic dysfunction-associated steatotic liver disease&#59; H&#38;E&#44; hematoxylin and eosin&#59; PSR&#44; picro-sirius red&#59; NASH-CRN&#44; Non-alcoholic steatohepatitis-Clinical Research Network&#59; SAF&#44; Steatosis&#44; Activity&#44; Fibrosis&#59; FFPE&#44; formalin-fixed paraffin-embedded&#59; RNA-seq&#44; RNA-sequencing&#46;</p>"
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ISSN: 16652681
Original language: English
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