Diagnostic accuracy of non-invasive tests to screen for at-risk MASH-An individual participant data meta-analysis.

BACKGROUND & AIMS: There is a need to reduce the screen failure rate (SFR) in metabolic dysfunction-associated steatohepatitis (MASH) clinical trials (MASH+F2-3; MASH+F4) and identify people with high-risk MASH (MASH+F2-4) in clinical practice. We aimed to evaluate non-invasive tests (NITs) screening approaches for these target conditions. METHODS: This was an individual participant data meta-analysis for the performance of NITs against liver biopsy for MASH+F2-4, MASH+F2-3 and MASH+F4. Index tests were the FibroScan-AST (FAST) score, liver stiffness measured using vibration-controlled transient elastography (LSM-VCTE), the fibrosis-4 score (FIB-4) and the NAFLD fibrosis score (NFS). Area under the receiver operating characteristics curve (AUROC) and thresholds including those that achieved 34% SFR were reported. RESULTS: We included 2281 unique cases. The prevalence of MASH+F2-4, MASH+F2-3 and MASH+F4 was 31%, 24% and 7%, respectively. Area under the receiver operating characteristics curves for MASH+F2-4 were .78, .75, .68 and .57 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F2-3 were .73, .67, .60, .58 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F4 were .79, .84, .81, .76 for FAST, LSM-VCTE, FIB-4 and NFS. The sequential combination of FIB-4 and LSM-VCTE for the detection of MASH+F2-3 with threshold of .7 and 3.48, and 5.9 and 20 kPa achieved SFR of 67% and sensitivity of 60%, detecting 15 true positive cases from a theoretical group of 100 participants at the prevalence of 24%. CONCLUSIONS: Sequential combinations of NITs do not compromise diagnostic performance and may reduce resource utilisation through the need of fewer LSM-VCTE examinations.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.

AIMS: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. METHODS: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. RESULTS: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. CONCLUSIONS: This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

Liver fibrosis phenotyping and severity scoring by quantitative image analysis of biopsy slides.

BACKGROUND & AIMS: Digital pathology image analysis can phenotype liver fibrosis using histological traits that reflect collagen content, morphometry and architecture. Here, we aimed to calculate fibrosis severity scores to quantify these traits. METHODS: Liver biopsy slides were categorised by Ishak stage and aetiology. We used a digital pathology technique to calculate four fibrosis severity scores: Architecture Composite Score (ACS), Collagen Composite Score (CCS), Morphometric Composite Score (MCS) and Phenotypic Fibrosis Composite Score (PH-FCS). We compared how these scores varied according to disease stage and aetiology. RESULTS: We included 80 patients (40% female, mean age 59.0 years, mean collagen proportionate area 17.1%) with mild (F0-2, n = 28), moderate (F3-4, n = 17) or severe (F5-6, n = 35) fibrosis. All four aetiology independent scores corelated with collagen proportionate area (ACS: rp = .512, CCS: rp = .727, MCS: rp = .777, PFCS: r = .772, p < .01 for all) with significant differences between moderate and severe fibrosis (p < .05). ACS increased primarily between moderate and severe fibrosis (by 95% to 226% depending on underlying aetiology), whereas MCS and CCS accumulation was more varied. We used 28 qFTs that distinguished between autoimmune- and alcohol-related liver disease to generate an MCS that significantly differed between mild and severe fibrosis for these aetiologies (p < .05). CONCLUSIONS: We describe four aetiology-dependent and -independent severity scores that quantify fibrosis architecture, collagen content and fibre morphometry. This approach provides additional insight into how progression of architectural changes and accumulation of collagen may differ depending on underlying disease aetiology.

Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis.

Background & Aims: Pathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease. Methods: Using individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies. Results: Eligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology. Conclusions: Histological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values. Impact and implications: Magnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol.

Non-alcoholic fatty liver disease (NAFLD) is the liver manifestation of the metabolic syndrome with global prevalence reaching epidemic levels. Despite the high disease burden in the population only a small proportion of those with NAFLD will develop progressive liver disease, for which there is currently no approved pharmacotherapy. Identifying those who are at risk of progressive NAFLD currently requires a liver biopsy which is problematic. Firstly, liver biopsy is invasive and therefore not appropriate for use in a condition like NAFLD that affects a large proportion of the population. Secondly, biopsy is limited by sampling and observer dependent variability which can lead to misclassification of disease severity. Non-invasive biomarkers are therefore needed to replace liver biopsy in the assessment of NAFLD. Our study addresses this unmet need. The LITMUS Imaging Study is a prospectively recruited multi-centre cohort study evaluating magnetic resonance imaging and elastography, and ultrasound elastography against liver histology as the reference standard. Imaging biomarkers and biopsy are acquired within a 100-day window. The study employs standardised processes for imaging data collection and analysis as well as a real time central monitoring and quality control process for all the data submitted for analysis. It is anticipated that the high-quality data generated from this study will underpin changes in clinical practice for the benefit of people with NAFLD. Study Registration: clinicaltrials.gov: NCT05479721.

An international Delphi consensus statement on metabolic dysfunction-associated fatty liver disease and risk of chronic kidney disease.

Background: With the rising global prevalence of fatty liver disease related to metabolic dysfunction, the association of this common liver condition with chronic kidney disease (CKD) has become increasingly evident. In 2020, the more inclusive term metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed to replace the term non-alcoholic fatty liver disease (NAFLD). The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD. However, to date, there is no appropriate guidance on CKD in individuals with MAFLD. Furthermore, there has been little attention paid to the link between MAFLD and CKD in the Nephrology community. Methods and Results: Using a Delphi-based approach, a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD. Conclusions: This Delphi-based consensus statement provided guidance on the epidemiology, mechanisms, management and treatment of MAFLD and CKD, as well as the relationship between the severity of MAFLD and risk of CKD, which establish a framework for the early prevention and management of these two common and interconnected diseases.

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study.

BACKGROUND AND AIMS: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. APPROACH AND RESULTS: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). CONCLUSIONS: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): a comparative diagnostic accuracy study.

BACKGROUND: The reference standard for detecting non-alcoholic steatohepatitis (NASH) and staging fibrosis-liver biopsy-is invasive and resource intensive. Non-invasive biomarkers are urgently needed, but few studies have compared these biomarkers in a single cohort. As part of the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) project, we aimed to evaluate the diagnostic accuracy of 17 biomarkers and multimarker scores in detecting NASH and clinically significant fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) and identify their optimal cutoffs as screening tests in clinical trial recruitment. METHODS: This was a comparative diagnostic accuracy study in people with biopsy-confirmed NAFLD from 13 countries across Europe, recruited between Jan 6, 2010, and Dec 29, 2017, from the LITMUS metacohort of the prospective European NAFLD Registry. Adults (aged ≥18 years) with paired liver biopsy and serum samples were eligible; those with excessive alcohol consumption or evidence of other chronic liver diseases were excluded. The diagnostic accuracy of the biomarkers was expressed as the area under the receiver operating characteristic curve (AUC) with liver histology as the reference standard and compared with the Fibrosis-4 index for liver fibrosis (FIB-4) in the same subgroup. Target conditions were the presence of NASH with clinically significant fibrosis (ie, at-risk NASH; NAFLD Activity Score ≥4 and F≥2) or the presence of advanced fibrosis (F≥3), analysed in all participants with complete data. We identified thres holds for each biomarker for reducing the number of biopsy-based screen failures when recruiting people with both NASH and clinically significant fibrosis for future trials. FINDINGS: Of 1430 participants with NAFLD in the LITMUS metacohort with serum samples, 966 (403 women and 563 men) were included after all exclusion criteria had been applied. 335 (35%) of 966 participants had biopsy-confirmed NASH and clinically significant fibrosis and 271 (28%) had advanced fibrosis. For people with NASH and clinically significant fibrosis, no single biomarker or multimarker score significantly reached the predefined AUC 0·80 acceptability threshold (AUCs ranging from 0·61 [95% CI 0·54-0·67] for FibroScan controlled attenuation parameter to 0·81 [0·75-0·86] for SomaSignal), with accuracy mostly similar to FIB-4. Regarding detection of advanced fibrosis, SomaSignal (AUC 0·90 [95% CI 0·86-0·94]), ADAPT (0·85 [0·81-0·89]), and FibroScan liver stiffness measurement (0·83 [0·80-0·86]) reached acceptable accuracy. With 11 of 17 markers, histological screen failure rates could be reduced to 33% in trials if only people who were marker positive had a biopsy for evaluating eligibility. The best screening performance for NASH and clinically significant fibrosis was observed for SomaSignal (number needed to test [NNT] to find one true positive was four [95% CI 4-5]), then ADAPT (six [5-7]), MACK-3 (seven [6-8]), and PRO-C3 (nine [7-11]). INTERPRETATION: None of the single markers or multimarker scores achieved the predefined acceptable AUC for replacing biopsy in detecting people with both NASH and clinically significant fibrosis. However, several biomarkers could be applied in a prescreening strategy in clinical trial recruitment. The performance of promising markers will be further evaluated in the ongoing prospective LITMUS study cohort. FUNDING: The Innovative Medicines Initiative 2 Joint Undertaking.

Noninvasive assessment of liver disease severity in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes.

BACKGROUND AND AIMS: We evaluated the diagnostic accuracy of simple, noninvasive tests (NITs) in NAFLD patients with type 2 diabetes (T2D). METHODS AND RESULTS: This was an individual patient data meta-analysis of 1780 patients with biopsy-proven NAFLD and T2D. The index tests of interest were FIB-4, NAFLD Fibrosis Score (NFS), aspartate aminotransferase-to-platelet ratio index, liver stiffness measurement (LSM) by vibration-controlled transient elastography, and AGILE 3+. The target conditions were advanced fibrosis, NASH, and fibrotic NASH(NASH plus F2-F4 fibrosis). The diagnostic performance of noninvasive tests. individually or in sequential combination, was assessed by area under the receiver operating characteristic curve and by decision curve analysis. Comparison with 2278 NAFLD patients without T2D was also made. In NAFLD with T2D LSM and AGILE 3+ outperformed, both NFS and FIB-4 for advanced fibrosis (area under the receiver operating characteristic curve:LSM 0.82, AGILE 3+ 0.82, NFS 0.72, FIB-4 0.75, aspartate aminotransferase-to-platelet ratio index 0.68; p < 0.001 of LSM-based versus simple serum tests), with an uncertainty area of 12%-20%. The combination of serum-based with LSM-based tests for advanced fibrosis led to a reduction of 40%-60% in necessary LSM tests. Decision curve analysis showed that all scores had a modest net benefit for ruling out advanced fibrosis at the risk threshold of 5%-10% of missing advanced fibrosis. LSM and AGILE 3+ outperformed both NFS and FIB-4 for fibrotic NASH (area under the receiver operating characteristic curve:LSM 0.79, AGILE 3+ 0.77, NFS 0.71, FIB-4 0.71; p < 0.001 of LSM-based versus simple serum tests). All noninvasive scores were suboptimal for diagnosing NASH. CONCLUSIONS: LSM and AGILE 3+ individually or in low availability settings in sequential combination after FIB-4 or NFS have a similar good diagnostic accuracy for advanced fibrosis and an acceptable diagnostic accuracy for fibrotic NASH in NAFLD patients with T2D.

Comparison of liver iron concentration calculated from R2* at 1.5 T and 3 T.

PURPOSE: R2*, a measurement obtained using magnetic resonance imaging (MRI) can be used to estimate liver iron concentration (LIC). 3 T and 1.5 T scanners can be used but conversion of 3 T R2* to LIC is less well validated. In this study the aim was to compare 3 T-R2* LIC and 1.5 T-R2* LIC estimations to assess if they can be used interchangeably. METHODS: Thirty participants were scanned at both 1.5 T and 3 T. R2* was measured at both field strengths. 3 T R2* and 1.5 R2* were compared using linear regression and were converted to LIC using different calibration curves. Pearson's rho and Intraclass Correlation Coefficients (ICCs) were used to assess correlation and agreement between 1.5 and 3 T LIC. Bland Altman plots were used to assess bias and limits of agreement. RESULTS: All 1.5 T and 3 T LIC comparisons gave Pearson's rho of 0.99 (p < 0.001). ICC ranged from 0.83 (p = 0.005) to 0.96 (p < 0.001). Biases had magnitude of less than 0.2 mg/g dry weight. CONCLUSION: Agreement and bias between 3 and 1.5 T-R2* LIC depended on the method used for conversion. There were instances when the agreement was excellent and bias was small, indicating that potentially 3 T-R2* LIC can be used alongside or instead of 1.5 T-R2* LIC but care needs to be taken over the conversion methods selected. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT03743272, 16 November 2018.

A novel serum metabolomic panel distinguishes IgG4-related sclerosing cholangitis from primary sclerosing cholangitis.

BACKGROUND & AIMS: Primary sclerosing cholangitis (PSC) and IgG4-related sclerosing cholangitis (IgG4-SC) are chronic fibro-inflammatory immune-mediated hepatobiliary conditions that are challenging to distinguish in a clinical setting. Accurate non-invasive biomarkers for discriminating PSC and IgG4-SC are important to ensure a correct diagnosis, prompt therapy and adequate cancer surveillance. METHODS: We performed nuclear magnetic resonance (NMR)-based metabolomic profiling using serum samples collected prospectively from patients with PSC (n = 100), IgG4-SC (n = 23) and healthy controls (HC; n = 16). RESULTS: Multivariate analysis of the serum metabolome discriminated PSC from IgG4-SC with greater accuracy (AUC 0.95 [95%CI 0.90-0.98]) than IgG4 titre (AUC 0.87 [95%CI 0.79-0.94]). When inflammatory bowel disease (IBD) was excluded as a comorbid condition (IgG4-SC n = 20, PSC n = 22), the diagnostic AUC increased to 1.0, suggesting that the metabolome differences identified are not a result of the increased prevalence of IBD in PSC relative to IgG4-SC patients. Serum lactate (p < .0001), glucose (p < .01) and glutamine (p < .01) metabolites were increased in IgG4-related disease (IgG4-RD) and IgG4-SC individuals compared to PSC, whereas mobile choline (p < .05), 3-hydroxybutyric acid (p < .01) and -CH3 lipoprotein resonances (p < .01) were decreased. CONCLUSIONS: Taken together, serum metabolomic profiling has the potential to be incorporated as a diagnostic criterion, independent of IgG4 titre, to improve the diagnosis of IgG4-RD and help distinguish IgG4-SC from PSC.

Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH). METHODS: Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC). RESULTS: A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74-0.82), for liver fat was 0.78 (95% CI, 0.73-0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78-0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74-0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64-0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75-0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers. CONCLUSIONS: This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways.

Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2-dimensional shear wave elastography (2DSWE), magnetic resonance elastography (MRE), and magnetic resonance imaging (MRI) have been proposed as non-invasive tests for patients with non-alcoholic fatty liver disease (NAFLD). This study evaluated their diagnostic accuracy for liver fibrosis and non-alcoholic steatohepatitis (NASH). METHODS: PubMED/MEDLINE, EMBASE and the Cochrane Library were searched for studies examining the diagnostic accuracy of these index tests, against histology as the reference standard, in adult patients with NAFLD. Two authors independently screened and assessed methodological quality of studies and extracted data. Summary estimates of sensitivity, specificity and area under the curve (sAUC) were calculated for fibrosis stages and NASH, using a random effects bivariate logit-normal model. RESULTS: We included 82 studies (14,609 patients). Meta-analysis for diagnosing fibrosis stages was possible in 53 VCTE, 11 MRE, 12 pSWE and 4 2DSWE studies, and for diagnosing NASH in 4 MRE studies. sAUC for diagnosis of significant fibrosis were: 0.83 for VCTE, 0.91 for MRE, 0.86 for pSWE and 0.75 for 2DSWE. sAUC for diagnosis of advanced fibrosis were: 0.85 for VCTE, 0.92 for MRE, 0.89 for pSWE and 0.72 for 2DSWE. sAUC for diagnosis of cirrhosis were: 0.89 for VCTE, 0.90 for MRE, 0.90 for pSWE and 0.88 for 2DSWE. MRE had sAUC of 0.83 for diagnosis of NASH. Three (4%) studies reported intention-to-diagnose analyses and 15 (18%) studies reported diagnostic accuracy against pre-specified cut-offs. CONCLUSIONS: When elastography index tests are acquired successfully, they have acceptable diagnostic accuracy for advanced fibrosis and cirrhosis. The potential clinical impact of these index tests cannot be assessed fully as intention-to-diagnose analyses and validation of pre-specified thresholds are lacking. LAY SUMMARY: Non-invasive tests that measure liver stiffness or use magnetic resonance imaging (MRI) have been suggested as alternatives to liver biopsy for assessing the severity of liver scarring (fibrosis) and fatty inflammation (steatohepatitis) in patients with non-alcoholic fatty liver disease (NAFLD). In this study, we summarise the results of previously published studies on how accurately these non-invasive tests can diagnose liver fibrosis and inflammation, using liver biopsy as the reference. We found that some techniques that measure liver stiffness had a good performance for the diagnosis of severe liver scarring.

Simple non-invasive scoring systems and histological scores in predicting mortality in patients with non-alcoholic fatty liver disease: A systematic review and meta-analysis.

BACKGROUND AND AIM: There is debate among the hepatology community regarding the simple non-invasive scoring systems and histological scores (even it was developed for histological classification) in predicting clinical outcomes in patients with non-alcoholic fatty liver disease (NAFLD). This study aimed to determine whether the presence of simple non-invasive scoring systems and histological scores could predict all-cause mortality, liver-related mortality, and liver disease decompensation (liver failure, cirrhosis, hepatocellular carcinoma, or decompensated liver disease). METHODS: The pooled hazard ratio of prognostic factors and incidence rate per 1000 person-years in patients with NAFLD was calculated and further adjusted by two different models of handling the duplicated data. RESULTS: A total of 19 longitudinal studies were included. Most simple non-invasive scoring systems (Fibrosis-4 Score, BARD, and aspartate aminotransferase-to-platelet ratio index ) and histological scores (NAFLD activity score, Brunt, and "steatosis, activity, and fibrosis" ) failed in predicting mortality, and only the NAFLD fibrosis score > 0.676 showed prognostic ability to all-cause mortality (four studies, 7564 patients, 118 352 person-years followed up, pooled hazard ratio 1.44, 95% confidence interval [CI] 1.05-1.96). The incidence rate per 1000 person-years of all-cause mortality, liver-related mortality, cardiovascular-related mortality, and liver disease decompensation resulted in a pooled incidence rate per 1000 person-years of 22.65 (14 studies, 95% CI 9.62-53.31), 3.19 (7 studies, 95% CI 1.14-8.93), 6.02 (6 studies, 95% CI 4.69-7.74), and 11.46 (4 studies, 95% CI 5.33-24.63), respectively. CONCLUSION: Non-alcoholic fatty liver disease fibrosis score showed promising prognostic value to all-cause mortality. Most present simple non-invasive scoring systems and histological scores failed to predict clinical outcomes.

A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis.

Non-alcoholic steatohepatitis (NASH) is major health burden lacking effective pharmacological therapies. Clinical trials enrol patients with histologically-defined NAFLD (non-alcoholic fatty liver disease) activity score (NAS) ≥ 4 and Kleiner-Brunt fibrosis stage (F) ≥ 2; however, screen failure rates are often high following biopsy. This study evaluated a non-invasive MRI biomarker, iron-corrected T1 mapping (cT1), as a diagnostic pre-screening biomarker for NASH. In a retrospective analysis of 86 biopsy confirmed NAFLD patients we explored the potential of blood and imaging biomarkers, both in isolation and in combination, to discriminate those who have NAS ≥ 4 and F ≥ 2 from those without. Stepwise logistic regression was performed to select the optimal combination of biomarkers, diagnostic accuracy was determined using area under the receiver operator curve and model validated confirmed with and fivefold cross-validation. Results showed that levels of cT1, AST, GGT and fasting glucose were all good predictors of NAS ≥ 4 and F ≥ 2, and the model identified the combination of cT1-AST-fasting glucose (cTAG) as far superior to any individual biomarker (AUC 0.90 [0.84-0.97]). This highlights the potential utility of the composite cTAG score for screening patients prior to biopsy to identify those suitable for NASH clinical trial enrolment.

Prognostic value of multiparametric magnetic resonance imaging, transient elastography and blood-based fibrosis markers in patients with chronic liver disease.

BACKGROUND & AIMS: Liver cT1 , liver T1 , transient elastography (TE) and blood-based biomarkers have independently been shown to predict clinical outcomes but have not been directly compared in a single cohort of patients. Our aim was to compare these tests' prognostic value in a cohort of patients with compensated chronic liver disease. METHODS: Patients with unselected compensated liver disease aetiologies had baseline assessments and were followed up for development of clinical outcomes, blinded to the imaging results. The prognostic value of non-invasive liver tests at prespecified thresholds was assessed for a combined clinical endpoint comprising ascites, variceal bleeding, hepatic encephalopathy, hepatocellular carcinoma, liver transplantation and mortality. RESULTS: One hundred and ninety-seven patients (61% male) with median age of 54 years were followed up for 693 patient-years (median (IQR) 43 (26-58) months). The main diagnoses were NAFLD (41%), viral hepatitis (VH, 25%) and alcohol-related liver disease (ArLD; 14%). During follow-up 14 new clinical events, and 11 deaths occurred. Clinical outcomes were predicted by liver cT1  > 825ms with HR 9.9 (95% CI: 1.29-76.4, P = .007), TE > 8kPa with HR 7.8 (95% CI: 0.97-62.3, P = .02) and FIB-4 > 1.45 with HR 4.09 (95% CI: 0.90-18.4, P = .05). In analysis taking into account technical failure and unreliability, liver cT1  > 825 ms could predict clinical outcomes (P = .03), but TE > 8kPa could not (P = .4). CONCLUSIONS: We provide further evidence that liver cT1 , TE and serum-based biomarkers can predict clinical outcomes, but when taking into account technical failure/unreliability, TE cut-offs perform worse than those of cT1 and blood biomarkers.

Consensus recommendations of three-dimensional visualization for diagnosis and management of liver diseases.

Three-dimensional (3D) visualization involves feature extraction and 3D reconstruction of CT images using a computer processing technology. It is a tool for displaying, describing, and interpreting 3D anatomy and morphological features of organs, thus providing intuitive, stereoscopic, and accurate methods for clinical decision-making. It has played an increasingly significant role in the diagnosis and management of liver diseases. Over the last decade, it has been proven safe and effective to use 3D simulation software for pre-hepatectomy assessment, virtual hepatectomy, and measurement of liver volumes in blood flow areas of the portal vein; meanwhile, the use of 3D models in combination with hydrodynamic analysis has become a novel non-invasive method for diagnosis and detection of portal hypertension. We herein describe the progress of research on 3D visualization, its workflow, current situation, challenges, opportunities, and its capacity to improve clinical decision-making, emphasizing its utility for patients with liver diseases. Current advances in modern imaging technologies have promised a further increase in diagnostic efficacy of liver diseases. For example, complex internal anatomy of the liver and detailed morphological features of liver lesions can be reflected from CT-based 3D models. A meta-analysis reported that the application of 3D visualization technology in the diagnosis and management of primary hepatocellular carcinoma has significant or extremely significant differences over the control group in terms of intraoperative blood loss, postoperative complications, recovery of postoperative liver function, operation time, hospitalization time, and tumor recurrence on short-term follow-up. However, the acquisition of high-quality CT images and the use of these images for 3D visualization processing lack a unified standard, quality control system, and homogeneity, which might hinder the evaluation of application efficacy in different clinical centers, causing enormous inconvenience to clinical practice and scientific research. Therefore, rigorous operating guidelines and quality control systems need to be established for 3D visualization of liver to develop it to become a mature technology. Herein, we provide recommendations for the research on diagnosis and management of 3D visualization in liver diseases to meet this urgent need in this research field.