Exploring the association between theobromine intake and hepatic steatosis in young people.

The incidence of non-alcoholic fatty liver disease (NAFLD) tends to be younger. And the role of theobromine in fatty liver disease remains unclear. The purpose of this study was to investigate the relationship between dietary theobromine intake and degree of hepatic steatosis in individuals aged 45 and below, using data from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) and liver ultrasonography transient elastography. A total of 1796 participants aged below 45 years were included from NHANES 2017-2020 data after applying exclusion criteria. Multivariate regression and subgroup analyses were conducted to examine the associations between theobromine intake and controlled attenuation parameter (CAP), adjusting for potential confounders. Generalized additive models and two-piecewise linear regression were used to analyze nonlinear relationships. In the unadjusted Model 1 and preliminarily adjusted Model 2, there was no significant correlation between theobromine intake and CAP values. However, in Models 3 and 4, which accounted for confounding factors, a higher intake of theobromine was significantly associated with lower CAP values. Subgroup analyses in the fully adjusted Model 4 revealed a significant negative correlation among individuals aged 18-45, women, and white populations. Nonlinear analysis revealed a U-shaped relationship in black Americans, with the lowest CAP values at 44.5 mg/day theobromine. This study provides evidence that higher theobromine intake is correlated with lower degree of hepatic steatosis in young people, especially those aged 18-45 years, women, and whites. For black Americans, maintaining theobromine intake around 44.5 mg/day may help minimize liver steatosis. These findings may help personalize clinical nutritional guidance, prevent the degree of hepatic steatosis, and provide pharmacological approaches to reverse fatty liver disease in young people.

Simulating federated learning for steatosis detection using ultrasound images.

We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis. We employed four data partitioning strategies to simulate FL scenarios and we assessed four FL algorithms. We investigated the impact of class imbalance and the mismatch between the global and local data distributions on the learning outcome. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. AUCs were 0.93 (95% CI 0.92, 0.94) for source-based partitioning scenario with FedAvg, 0.90 (95% CI 0.89, 0.91) for a centralized model, and 0.83 (95% CI 0.81, 0.85) for a model trained in a single-center scenario. When data was perfectly balanced on the global level and each site had an identical data distribution, the model yielded an AUC of 0.90 (95% CI 0.88, 0.92). When each site contained data exclusively from one single class, irrespective of the global data distribution, the AUC fell in the range of 0.34-0.70. FL applied to B-mode US images provide performance comparable to a centralized model and higher than single-center scenario. Global data imbalance and local data heterogeneity influenced the learning outcome.

Clinical research of fibroscan ‒ TE-CAP at noninvasive diagnosis of hepatic steatosis in children.

BACKGROUND & AIMS: The authors assess the diagnostic accuracy of the Transient Elastography-Controlled Attenuation Parameter (TE-CAP) in children of Southern China. METHODS: 105 obese or overweight children and adolescents were enrolled in the diagnostic test of TE-CAP assessment of hepatic steatosis using MRI-PDFF. Hepatic steatosis grades S0-S3 were classified. Statistical correlation, agreement and consistency between methods were evaluated. The diagnostic efficiency of TE-CAP was evaluated. The authors used the cutoff value of TE-CAP to detect hepatic steatosis in another 356 children. RESULTS: The Area Under Curve (AUC) of TE-CAP for grade ≥ S1, ≥ S2, and ≥ S3 steatosis were 0.975, 0.984, and 0.997, respectively. For detecting ≥ S1 steatosis, TE-CAP had a sensitivity of 96 % and a specificity of 97 %. For detecting ≥ S2 steatosis, TE-CAP had a sensitivity of 97 % and a specificity of 93 %. For detecting ≥ S3 steatosis, TE-CAP had a sensitivity of 1 and a specificity of 94 %. TE-CAP and MRI-PDFF had a linear correlation (r = 0. 0.87, p < 0.001). The hepatic steatosis was identified in 40.2 % (143/356) of children in which the obesity and overweight were 69.8 % (113/162) and 40.0 % (18/45). CONCLUSION: TE-CAP showed excellent diagnostic accuracy in pediatric hepatic steatosis.

Obesity Is Associated with Fatty Liver and Fat Changes in the Kidneys in Humans as Assessed by MRI.

BACKGROUND: Obesity is associated with metabolic syndrome and fat accumulation in various organs such as the liver and the kidneys. Our goal was to assess, using magnetic resonance imaging (MRI) Dual-Echo phase sequencing, the association between liver and kidney fat deposition and their relation to obesity. METHODS: We analyzed MRI scans of individuals who were referred to the Chaim Sheba Medical Center between December 2017 and May 2020 to perform a study for any indication. For each individual, we retrieved from the computerized charts data on sex, and age, weight, height, body mass index (BMI), systolic and diastolic blood pressure (BP), and comorbidities (diabetes mellitus, hypertension, dyslipidemia). RESULTS: We screened MRI studies of 399 subjects with a median age of 51 years, 52.4% of whom were women, and a median BMI 24.6 kg/m2. We diagnosed 18% of the participants with fatty liver and 18.6% with fat accumulation in the kidneys (fatty kidneys). Out of the 67 patients with fatty livers, 23 (34.3%) also had fatty kidneys, whereas among the 315 patients without fatty livers, only 48 patients (15.2%) had fatty kidneys (p < 0.01). In comparison to the patients who did not have a fatty liver or fatty kidneys (n = 267), those who had both (n = 23) were more obese, had higher systolic BP, and were more likely to have diabetes mellitus. In comparison to the patients without a fatty liver, those with fatty livers had an adjusted odds ratio of 2.91 (97.5% CI; 1.61-5.25) to have fatty kidneys. In total, 19.6% of the individuals were obese (BMI ≥ 30), and 26.1% had overweight (25 < BMI < 30). The obese and overweight individuals were older and more likely to have diabetes mellitus and hypertension and had higher rates of fatty livers and fatty kidneys. Fat deposition in both the liver and the kidneys was observed in 15.9% of the obese patients, in 8.3% of the overweight patients, and in none of those with normal weight. Obesity was the only risk factor for fatty kidneys and fatty livers, with an adjusted OR of 6.3 (97.5% CI 2.1-18.6). CONCLUSIONS: Obesity is a major risk factor for developing a fatty liver and fatty kidneys. Individuals with a fatty liver are more likely to have fatty kidneys. MRI is an accurate modality for diagnosing fatty kidneys. Reviewing MRI scans of any indication should include assessment of fat fractions in the kidneys in addition to that of the liver.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

Diagnosis and non-invasive assessment of MASLD in type 2 diabetes and obesity.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently the most common chronic liver disease and an important cause of cirrhosis and hepatocellular carcinoma. It is strongly associated with type 2 diabetes and obesity. Because of the huge number of patients at risk of MASLD, it is imperative to use non-invasive tests appropriately. AIMS: To provide a narrative review on the performance and limitations of non-invasive tests, with a special emphasis on the impact of diabetes and obesity. METHODS: We searched PubMed and Cochrane databases for articles published from 1990 to August 2023. RESULTS: Abdominal ultrasonography remains the primary method to diagnose hepatic steatosis, while magnetic resonance imaging proton density fat fraction is currently the gold standard to quantify steatosis. Simple fibrosis scores such as the Fibrosis-4 index are well suited as initial assessment in primary care and non-hepatology settings to rule out advanced fibrosis and future risk of liver-related complications. However, because of its low positive predictive value, an abnormal test should be followed by specific blood (e.g. Enhanced Liver Fibrosis score) or imaging biomarkers (e.g. vibration-controlled transient elastography and magnetic resonance elastography) of fibrosis. Some non-invasive tests of fibrosis appear to be less accurate in patients with diabetes. Obesity also affects the performance of abdominal ultrasonography and transient elastography, whereas magnetic resonance imaging may not be feasible in some patients with severe obesity. CONCLUSIONS: This article highlights issues surrounding the clinical application of non-invasive tests for MASLD in patients with type 2 diabetes and obesity.

Near-infrared fluorescent probe for the imaging of viscosity in fatty liver mice and valuation of drug efficacy.

Fatty liver disease affects at least 25 percent of the population worldwide and is a severe metabolic syndrome. Viscosity is closely related to fatty liver disease, so it is urgent to develop an effective tool for monitoring viscosity. Herein, a NIR fluorescent probe called MBC-V is developed for imaging viscosity, consisting of dimethylaniline and malonitrile-benzopyran. MBC-V is non-fluorescent in low viscosity solutions due to intramolecular rotation. In high viscosity solution, the intramolecular rotation of MBC-V is suppressed and the fluorescence is triggered. MBC-V has long emission wavelength at 720 nm and large Stokes shift about 160 nm. Moreover, MBC-V can detect changes in cell viscosity in fatty liver cells, and can image the therapeutic effects of drug in fatty liver cells. By taking advantage of NIR emission, MBC-V can be used as an imaging tool for fatty liver disease and a way to evaluate the therapeutic effect of drug for fatty liver disease.

Metabolic-associated fatty liver voxel-based quantification on CT images using a contrast adapted automatic tool.

BACKGROUND & AIMS: Metabolic-dysfunction associated fatty liver disease (MAFLD) is highly prevalent and can lead to liver complications and comorbidities, with non-invasive tests such as vibration-controlled transient elastography (VCTE) and invasive liver biopsies being used for diagnosis The aim of the present study was to develop a new fully automatized method for quantifying the percentage of fat in the liver based on a voxel analysis on computed tomography (CT) images to solve previously unconcluded diagnostic deficiencies either in contrast (CE) or non-contrast enhanced (NCE) assessments. METHODS: Liver and spleen were segmented using nn-UNet on CE- and NCE-CT images. Radiodensity values were obtained for both organs for defining the key benchmarks for fatty liver assessment: liver mean, liver-to-spleen ratio, liver-spleen difference, and their average. VCTE was used for validation. A classification task method was developed for detection of suitable patients to fulfill maximum reproducibility across cohorts and highlight subjects with other potential radiodensity-related diseases. RESULTS: Best accuracy was attained using the average of all proposed benchmarks being the liver-to-spleen ratio highly useful for CE and the liver-to-spleen difference for NCE. The proposed whole-organ automatic segmentation displayed superior potential when compared to the typically used manual region-of-interest drawing as it allows to accurately obtain the percent of fat in liver, among other improvements. Atypical patients were successfully stratified through a function based on biochemical data. CONCLUSIONS: The developed method tackles the current drawbacks including biopsy invasiveness, and CT-related weaknesses such as lack of automaticity, dependency on contrast agent, no quantification of the percentage of fat in liver, and limited information on region-to-organ affectation. We propose this tool as an alternative for individualized MAFLD evaluation by an early detection of abnormal CT patterns based in radiodensity whilst abording detection of non-suitable patients to avoid unnecessary exposure to CT radiation. Furthermore, this work presents a surrogate aid for assessing fatty liver at a primary assessment of MAFLD using elastography data.

MAFLD identifies patients with significant hepatic fibrosis better than MASLD.

BACKGROUND AND AIMS: Diagnostic criteria for metabolic dysfunction-associated steatotic liver disease (MASLD) have been proposed but not yet validated. This study aimed to compare the diagnostic accuracy of the MASLD definition with the existing criteria for metabolic dysfunction-associated fatty liver disease (MAFLD) in identifying patients with significant fibrosis. METHODS: The analysis included a total of 8317 individuals who had complete biochemical and liver ultrasonography data from the National Health and Nutrition Examination Survey (2017-2020). In this study, significant fibrosis (≥ F2) was determined by a median liver stiffness of ≥ 8.0 kPa. To identify independent factors associated with significant fibrosis, multivariable logistic regression analyses were applied. RESULTS: MAFLD (OR 3.44; 95% CI 2.88-4.12; P < 0.0001) has a trend for stronger and independent association with significant fibrosis compared to MASLD (OR 2.63; 95% CI 2.22-3.11; P < 0.0001). Non-MASLD MAFLD is independently associated with a 14.28-fold higher odds of significant fibrosis compared to non-MAFLD MASLD. The sensitivity for detecting significant fibrosis for MAFLD and MASLD was 76.23% vs 69.94%, respectively. The performance of MAFLD remains consistent in a sub-analysis of patients with no or mild alcohol intake. CONCLUSIONS: The definition of MAFLD provides a more precise identification of individuals who have both fatty liver and significant fibrosis, assessed by non-invasive tests.

Correlation of alanine aminotransferase levels and a histological diagnosis of steatohepatitis with ultrasound-diagnosed metabolic-associated fatty liver disease in patients from a centre in Nigeria.

BACKGROUND: Metabolic-associated fatty liver disease (MAFLD) is defined as the occurrence of hepatic fat accumulation in patients with negligible alcohol consumption or any other cause of hepatic steatosis. This study aimed to correlate the ultrasound-based diagnosis of MAFLD with the histological diagnosis of nonalcoholic steatohepatitis (NASH) and alanine aminotransferase (ALT) levels in patients with MAFLD. METHODS: This was a hospital-based cross-sectional study of 71 patients with MAFLD diagnosed by ultrasound. Percutaneous liver biopsy was performed for histological evidence of NASH in all patients, regardless of liver function test (LFT) values, provided that they had no contraindications. Liver histology was graded using the NASH Clinical Research Network MAFLD Activity Score. The data obtained were entered into SPSS version 21 and analysed using descriptive and inferential statistics. The significance level was set at < 0.05. RESULTS: A total of 71 patients (26 males and 45 females) with MAFLD were included. Thirty-nine (76.5%) patients with MAFLD and normal ALT levels had NASH, while 14 (82.4%) had elevated ALT levels. There was no statistically significant difference in the histological grade of NASH between patients with normal and elevated ALT levels. A weak correlation was found between the severity of steatosis on ultrasound scan and NASH incidence (p = 0.026). The sensitivity and specificity of ALT levels for predicting NASH according to the area under the receiver operating characteristics (AUROC 0.590) at an ALT cut-off value of 27.5 IU/L were 55.8% and 64.7%, respectively. CONCLUSION: NASH can occur in patients with MAFLD, irrespective of alanine transaminase (ALT) levels, and ultrasound grading of the severity of steatosis cannot accurately predict NASH. Liver biopsy remains the investigation of choice.

Fatty Liver Index (FLI) is the best score to predict MASLD with 50% lower cut-off value in women than in men.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is defined by the presence of hepatic steatosis, detected on ultrasonography (US) imaging or histology, and at least one of criteria for Metabolic Syndrome diagnosis. Simple non-invasive tests (NITs) have been proposed as an acceptable alternative when US and biopsy are not available or feasible but have not been validated for MASLD. In this observational study, we investigated the reliability of NITs for MASLD detection and whether sex-differences in screening methods should be considered. METHODS: We included 1069 individuals (48% males and 52% females) who underwent their first clinical examination for Metabolic Syndrome in the period between January 2015 and December 2022. Liver steatosis was detected through US and anthropometric and clinical parameters were recorded. RESULTS: Liver steatosis was detected in 648 patients and MASLD was diagnosed in 630 subjects (355 males; 275 females). Women with MASLD showed better metabolic profile and lower prevalence of Metabolic Syndrome criteria than men. Among NITs, Fatty Liver Index (FLI) showed the best ability for detection of MASLD, with a cut-off value of 44 (AUC = 0.82). When considering the two sexes for MASLD detection via FLI, despite no substantial differences regarding FLI correlations with metabolic biomarkers except for age, women showed marked lower FLI cut-off value (32; AUC = 0.80) than men (60; AUC = 0.80). CONCLUSIONS: In this study, we found that FLI is the best non-invasive predictor of both liver steatosis and MASLD. The finding that in women FLI cut-off value for MASLD detection is 50% lower than in men suggests the need of a sex-specific personalized program of screening and prevention of dysmetabolism-related liver diseases, despite outwardly healthy biomarkers profile.

Fatty liver disease is caused by the accumulation of fat into the liver and it is associated to increased risk of chronic diseases. Diagnosis of fatty liver is based on biopsy or ultrasound assessment but when these procedures are not available or feasible also some non-invasive scores have been showed to be reliable measures of this condition. In this study we compared the use of ultrasound and non-invasive scores to assess liver steatosis and associated metabolic disease, finding that Fatty Liver Index (FLI) is the best score for these diagnosis. Surprisingly, in women FLI cut-off value is 50% lower than in men, suggesting that different sex-specific factors may come into play in the development and evolution of liver steatosis. Thus, we suggest the need of a sex-specific personalized program of screening and prevention of dysmetabolism-related liver diseases.

Liver steatosis in patients with transfusion-dependent thalassaemia.

We evaluated the prevalence and the clinical associations of liver steatosis (LS) in patients with transfusion-dependent thalassaemia (TDT). We considered 301 TDT patients (177 females, median age = 40.61 years) enrolled in the Extension-Myocardial Iron Overload in Thalassaemia Network, and 25 healthy subjects. Magnetic resonance imaging was used to quantify iron overload and hepatic fat fraction (FF) by T2* technique and cardiac function by cine images. The glucose metabolism was assessed by the oral glucose tolerance test (OGTT). Hepatic FF was significantly higher in TDT patients than in healthy subjects (median value: 1.48% vs. 0.55%; p = 0.013). In TDT, hepatic FF was not associated with age, gender, serum ferritin levels or liver function parameters, but showed a weak inverse correlation with high-density lipoprotein cholesterol. The 36.4% of TDT patients showed LS (FF >3.7%). Active hepatitis C virus (HCV) infection, increased body mass index and hepatic iron were independent determinants of LS. A hepatic FF >3.53% predicted the presence of an abnormal OGTT. Hepatic FF was not correlated with cardiac iron, biventricular volumes or ejection fractions, but was correlated with left ventricular mass index. In TDT, LS is a frequent finding, associated with iron overload, increased weight and HCV, and conveying an increased risk for the alterations of glucose metabolism.

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers.

Hypertriglyceridemia is a common cause of acute pancreatitis (AP). Fatty liver, a manifestation of metabolic syndrome, is related to the severity of AP. The present study aimed to construct an accurate predictive model for severe AP (SAP) by combining the fatty liver infiltration on a computerized tomography (CT) scan with a series of blood biomarkers in patients with hypertriglyceridemia-associated AP (HTG-AP). A total of 213 patients diagnosed with HTG-AP were included in the present retrospective study. Clinical information and imageological findings were retrospectively analyzed. The model was constructed from independent risk factors using univariate analysis, the least absolute shrinkage and selection operator method. Subsequently, the data from the training group of 111 patients with HTG-AP was analyzed using logistic regression analysis. The efficacy of the model was verified using an external validation group of 102 patients through the receiver operating characteristic curve (ROC). Independent predictors, including serum calcium, C-reactive protein, lactate dehydrogenase and liver-to-spleen CT attenuation ratio (L/S ratio), were incorporated into the nomogram model for SAP in HTG-AP. The model achieved a sensitivity of 91.3% and a specificity of 88.6% in the training group. Compared with the Ranson model, the established nomogram model exhibited a better discriminative ability in the training group [area under the curve (AUC): 0.957] and external validation group (AUC: 0.930), as well as better calibration and clinical benefits. The present study demonstrates that the constructed nomogram based on CT findings and blood biomarkers is useful for the accurate prediction of SAP in HTG-AP.

Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. METHODS: Realistic hepatic steatosis models encompassing clinical FF range (0-60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5-100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. RESULTS: The virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95-1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. CONCLUSION: Our study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.

Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD.

PURPOSE: Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis. METHODS: A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLD was assessed via receiver operating characteristic (ROC) curve analysis. RESULTS: Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = - 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = - 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = - 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10-3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively. CONCLUSION: According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

Poor accuracy and sustainability of the first-step FIB4 EASL pathway for stratifying steatotic liver disease risk in the general population.

BACKGROUND AND AIMS: The European Association for the Study of the Liver introduced a clinical pathway (EASL CP) for screening significant/advanced fibrosis in people at risk of steatotic liver disease (SLD). We assessed the performance of the first-step FIB4 EASL CP in the general population across different SLD risk groups (MASLD, Met-ALD and ALD) and various age classes. METHODS: We analysed a total of 3372 individuals at risk of SLD from the 2017-2018 National Health and Nutrition Examination Survey (NHANES17-18), projected to 152.3 million U.S. adults, 300,329 from the UK Biobank (UKBB) and 57,644 from the Biobank Japan (BBJ). We assessed liver stiffness measurement (LSM) ≥8 kPa and liver-related events occurring within 3 and 10 years (3/10 year-LREs) as outcomes. We defined MASLD, MetALD, and ALD according to recent international recommendations. RESULTS: FIB4 sensitivity for LSM ≥ 8 kPa was low (27.7%), but it ranged approximately 80%-90% for 3-year LREs. Using FIB4, 22%-57% of subjects across the three cohorts were identified as candidates for vibration-controlled transient elastography (VCTE), which was mostly avoidable (positive predictive value of FIB4 ≥ 1.3 for LSM ≥ 8 kPa ranging 9.5%-13% across different SLD categories). Sensitivity for LSM ≥ 8 kPa and LREs increased with increasing alcohol intake (ALD>MetALD>MASLD) and age classes. For individuals aged ≥65 years, using the recommended age-adjusted FIB4 cut-off (≥2) substantially reduced sensitivity for LSM ≥ 8 kPa and LREs. CONCLUSIONS: The first-step FIB4 EASL CP is poorly accurate and feasible for individuals at risk of SLD in the general population. It is crucial to enhance the screening strategy with a first-step approach able to reduce unnecessary VCTEs and optimise their yield.

iATT liver fat quantification for steatosis grading by referring to MRI proton density fat fraction: a multicenter study.

BACKGROUND: Several preliminary reports have suggested the utility of ultrasound attenuation coefficient measurements based on B-mode ultrasound, such as iATT, for diagnosing steatotic liver disease. Nonetheless, evidence supporting such utility is lacking. This prospective study aimed to investigate whether iATT is highly concordant with magnetic resonance imaging (MRI)-based proton density fat fraction (MRI-PDFF) and could well distinguish between steatosis grades. METHODS: A cohort of 846 individuals underwent both iATT and MRI-PDFF assessments. Steatosis grade was defined as grade 0 with MRI-PDFF < 5.2%, grade 1 with 5.2% MRI-PDFF < 11.3%, grade 2 with 11.3% MRI-PDFF < 17.1%, and grade 3 with MRI-PDFF of 17.1%. The reproducibility of iATT and MRI-PDFF was evaluated using the Bland-Altman analysis and intraclass correlation coefficients, whereas the diagnostic performance of each steatosis grade was examined using receiver operating characteristic analysis. RESULTS: The Bland-Altman analysis indicated excellent reproducibility with minimal fixed bias between iATT and MRI-PDFF. The area under the curve for distinguishing steatosis grades 1, 2, and 3 were 0.887, 0.882, and 0.867, respectively. A skin-to-capsula distance of ≥ 25 mm was identified as the only significant factor causing the discrepancy. No interaction between MRI-logPDFF and MRE-LSM on iATT values was observed. CONCLUSIONS: Compared to MRI-PDFF, iATT showed excellent diagnostic accuracy in grading steatosis. iATT could be used as a diagnostic tool instead of MRI in clinical practice and trials. Trial registration This study was registered in the UMIN Clinical Trials Registry (UMIN000047411).

Recent advances in noninvasive assessment of liver steatosis.

Due to the steatosis epidemic, noninvasive quantification of liver fat content is of great interest. Magnetic resonance (MR) techniques, including proton MR spectroscopy (MRS) and MR chemical shift imaging can quantify liver fat by measuring, directly or indirectly (the latter), the proton density fat fraction (PDFF). They have shown excellent diagnostic accuracy and are currently the reference standard for the noninvasive assessment of liver steatosis and are used in clinical trials for evaluating the change in liver fat over time. Using ultrasound (US), three different quantitative parameters can be obtained to estimate liver fat: attenuation coefficient, backscatter coefficient, and speed of sound. Controlled attenuation parameter (CAP), which estimates the attenuation of the US beam, was the first algorithm available and is performed with a non-imaging system. Currently, several other algorithms are available on B-mode imaging ultrasound systems, and they have shown an accuracy similar to or higher than the CAP. This article reports the current knowledge about their application in patients with metabolic dysfunction-associated steatotic liver disease.

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia.

OBJECTIVE: To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients. MATERIALS AND METHODS: This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed. RESULTS: 84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver. CONCLUSION: MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.