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Ayares G, Diaz LA, Idalsoaga F, Alkhouri N, Noureddin M, Bataller R, Loomba R, Arab JP, Arrese M. MetALD: New Perspectives on an Old Overlooked Disease. Liver Int 2025; 45:e70017. [PMID: 40179033 PMCID: PMC11967760 DOI: 10.1111/liv.70017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 01/02/2025] [Accepted: 01/24/2025] [Indexed: 04/05/2025]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated liver disease (ALD) are the major contributors to the liver disease burden globally. The rise in these conditions is linked to obesity, type 2 diabetes, metabolic syndrome and increased alcohol consumption. MASLD and ALD share risk factors, pathophysiology and histological features but differ in their thresholds for alcohol use, and the ALD definition does not require the presence of metabolic dysfunction. A recent multi-society consensus overhauled the nomenclature of liver steatosis and introduced the term MetALD to describe patients with metabolic dysfunction who drink more than those with MASLD and less than those with ALD. This new terminology aims to enhance the understanding and management of liver disease but poses challenges, such as the need to accurately measure alcohol consumption in research and clinical practice settings. Recent studies show that MetALD has significant implications for patient management, as it is associated with increased mortality risks and more severe liver outcomes compared to MASLD alone. MetALD patients face increased risks of liver disease progression, cancer and cardiovascular disease. The diagnosis of MetALD involves the adequate quantification of alcohol use through standardised questionnaires and/or biomarkers as well as proper assessment of liver disease stage and progression risk using non-invasive tools including serologic markers, imaging, elastography techniques and genetic testing. Effective management requires addressing both metabolic and alcohol-related factors to improve outcomes. This review intends to provide a comprehensive overview of MetALD, covering pathogenesis, potential diagnostic approaches, management strategies and emerging therapies.
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Affiliation(s)
- Gustavo Ayares
- Departamento de GastroenterologíaEscuela de Medicina, Pontificia Universidad Católica de ChileSantiagoChile
- Escuela de Medicina, Universidad Finis TerraeSantiagoChile
| | - Luis Antonio Diaz
- Departamento de GastroenterologíaEscuela de Medicina, Pontificia Universidad Católica de ChileSantiagoChile
- MASLD Research Center, Division of Gastroenterology and HepatologyUniversity of California San DiegoCaliforniaUSA
| | - Francisco Idalsoaga
- Departamento de GastroenterologíaEscuela de Medicina, Pontificia Universidad Católica de ChileSantiagoChile
- Division of Gastroenterology Department of MedicineSchulich School of Medicine, Western University & London Health Sciences CentreLondonOntarioCanada
| | - Naim Alkhouri
- Department of HepatologyArizona Liver HealthChandlerArizonaUSA
| | | | - Ramon Bataller
- Liver UnitHospital Clinic and Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS)BarcelonaSpain
| | - Rohit Loomba
- MASLD Research Center, Division of Gastroenterology and HepatologyUniversity of California San DiegoCaliforniaUSA
| | - Juan Pablo Arab
- Departamento de GastroenterologíaEscuela de Medicina, Pontificia Universidad Católica de ChileSantiagoChile
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal MedicineVirginia Commonwealth University School of MedicineVirginiaUSA
| | - Marco Arrese
- Departamento de GastroenterologíaEscuela de Medicina, Pontificia Universidad Católica de ChileSantiagoChile
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Cai C, Zhang Z, Alberti G, Pereira A, De Barbieri F, García C, Wine E, Gana JC. Early childhood adiposity, lifestyle and gut microbiome are linked to steatotic liver disease development in adolescents. Int J Obes (Lond) 2025:10.1038/s41366-025-01737-1. [PMID: 40075127 DOI: 10.1038/s41366-025-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 01/14/2025] [Accepted: 02/24/2025] [Indexed: 03/14/2025]
Abstract
BACKGROUND/OBJECTIVES To examine the relationship between early childhood adiposity, adolescent lifestyles, gut microbiota and steatotic liver disease (SLD) development in adolescents using data from a prospective, longitudinal cohort study. METHODS We included 69 adolescents (14-17 years old) with SLD and 69 adolescents without SLD, matched for BMI-z scores, sex, and age, from the 13-year longitudinal cohort the "Growth and Obesity Cohort Study". Anthropometric data between the ages of 4 and 17 and lifestyle parameters (including diet and physical activity) at 14-17 years old were evaluated. Fecal samples were collected and microbiome composition and function were assessed using 16S ribosomal RNA amplicon sequencing. RESULTS Principal component analysis demonstrated dietary intake factors and childhood adiposity factors expanding the distribution variation between case and control groups, respectively. Lower odds of developing SLD during adolescence was associated with higher levels of daily fiber intake during adolescence (adjusted odds ratio = 0.91) and lower childhood adiposity (triceps skinfold at 5 years of age, suprailiac skinfold at 8 and 11 years of age, and waist-to-hip ratio at age 5-9 years). SLD was associated with a lower abundance of specific microbial species, such as Bacteroides vulgatus, which was higher in the control group compared to the case group (control/case abundance ratio = 18.71). B. vulgatus abundance also positively correlated with dietary fiber intake and inversely correlated with childhood adiposity. CONCLUSIONS Adiposity in early childhood and a low dietary fiber intake may contribute to the pathogenesis of SLD during adolescence, possibly through alterations to the intestinal microbiome; these findings could inform early disease markers and targets for intervention.
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Affiliation(s)
- Chenxi Cai
- State Key Laboratory of Vaccines for Infectious Diseases, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Zhengxiao Zhang
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
| | - Gigliola Alberti
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ana Pereira
- Instituto de Nutrición y Tecnología de los Alimentos, INTA, Universidad de Chile, Santiago, Chile
| | - Florencia De Barbieri
- Radiology Department. School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristián García
- Radiology Department. School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Eytan Wine
- Division of Pediatric Gastroenterology, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
| | - Juan Cristóbal Gana
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Dag N, Igci G, Yagin FH, Hanci MS, Kutlu R. Interobserver Reproducibility of Ultrasound Attenuation Imaging Technology in Liver Fat Quantification. JOURNAL OF CLINICAL ULTRASOUND : JCU 2025; 53:405-412. [PMID: 39436234 DOI: 10.1002/jcu.23877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Revised: 10/03/2024] [Accepted: 10/08/2024] [Indexed: 10/23/2024]
Abstract
PURPOSE This study aims to investigate the interobserver variability in the quantitative assessment of liver fat content using ultrasound attenuation imaging technology (USAT). METHODS This prospective, single-center study included 96 adult patients who were either diagnosed with or suspected of having metabolic dysfunction-associated steatotic liver disease. Independent observers, blinded to each other's assessments, evaluated hepatic steatosis visually and through USAT measurements. Separate measurements were taken at five intercostal and subcostal sites, and the median values of these measurements were recorded. The correlation between USAT measurements and visual steatosis grades was examined using Spearman's correlation test. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate the interobserver variability of USAT measurements. RESULTS Interobserver agreement for USAT measurements was excellent for the intercostal examination and good for the subcostal examination (p < 0.001). Body mass index did not significantly affect the level of interobserver agreement. Interobserver variability in Bland-Altman plots of USAT measurements was within the 95% limits of agreement. USAT measurements correlated very strongly with the visual degree of hepatic steatosis, both intercostal and subcostal (p < 0.001). USAT measurements were also significantly different between different visual degrees of hepatic steatosis (p < 0.001). CONCLUSION In the assessment of hepatic steatosis, USAT measurements obtained from the intercostal space showed excellent agreement in terms of interobserver reproducibility.
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Affiliation(s)
- Nurullah Dag
- Faculty of Medicine, Department of Radiology, Inonu University, Malatya, Türkiye
| | - Gulnur Igci
- Faculty of Medicine, Department of Radiology, Inonu University, Malatya, Türkiye
| | - Fatma Hilal Yagin
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Inonu University, Malatya, Türkiye
| | - Muhammed Salih Hanci
- Faculty of Medicine, Department of Radiology, Inonu University, Malatya, Türkiye
| | - Ramazan Kutlu
- Faculty of Medicine, Department of Radiology, Inonu University, Malatya, Türkiye
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Mohit K, Gupta R, Kumar B. Contrastive Learned Self-Supervised Technique for Fatty Liver and Chronic Liver Identification. Biomed Signal Process Control 2025; 100:106950. [DOI: 10.1016/j.bspc.2024.106950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Hu M, Yang J, Gao B, Wu Z, Wu Y, Hu D, Shen Q, Chen L. Prediction of MASLD using different screening indexes in Chinese type 2 diabetes mellitus. Diabetol Metab Syndr 2025; 17:10. [PMID: 39780236 PMCID: PMC11716454 DOI: 10.1186/s13098-024-01571-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2024] [Accepted: 12/28/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND Formerly known as non-alcoholic fatty liver disease (NAFLD), metabolic dysfunction-associated steatotic liver disease (MASLD) has now become the most widespread chronic liver disease worldwide. The primary goal of this study is to assess the ability of different indexes (including VAI, TyG, HOMA-IR, BMI, LAP, WHtR, TyG-BMI, TyG-WC, and TyG-WHtR) to predict MASLD in individuals diagnosed with type 2 diabetes mellitus (T2DM), particularly within the Chinese population. METHODS This cross-sectional study involved 1,742 patients with T2DM, recruited from the Metabolic Management Centers (MMC) at Suzhou Municipal Hospital. Abdominal ultrasonography was employed for MASLD diagnosis in patients with T2DM. The predictive accuracy of various screening indexes for MASLD in the Chinese T2DM population was evaluated using logistic regression and receiver operating characteristic (ROC) curve analyses. RESULTS Among the 1,742 participants, 996 were diagnosed with MASLD. After adjusting for potential confounding factors, positive associations with the risk of MASLD were found for all the nine indexes. The lipid accumulation product (LAP) exhibited the greatest predictive value for detecting MASLD, with an area under the curve (AUC) of 0.786(95%CI 0.764,0.807), followed by BMI(AUC = 0.785), VAI(AUC = 0.744), TyG(AUC = 0.720), WHtR(AUC = 0.710) and HOMA-IR(AUC = 0.676). The composite Indexes (TyG-BMI, TyG-WC, TyG-WHtR) also showed considerable predictive ability with AUCs of 0.765, 0.752 and 0.748, respectively. CONCLUSION Our results indicated that all nine indexes have favorable correlations with the risk of MASLD, and most of them have a good performance in predicting MASLD. According to our study, LAP was a reliable index for predicting MASLD among Chinese T2DM patients. The exploration of non-invasive screenings will provide significant support for the early detection and diagnosis of MASLD.
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Affiliation(s)
- Mengmeng Hu
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Jingyu Yang
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Beibei Gao
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Zhoulu Wu
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Ying Wu
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Dandan Hu
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Qiong Shen
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China
| | - Lei Chen
- Department of Endocrinology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 126 Daoqian Street, Suzhou, 215000, China.
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Drazinos P, Gatos I, Katsakiori PF, Tsantis S, Syrmas E, Spiliopoulos S, Karnabatidis D, Theotokas I, Zoumpoulis P, Hazle JD, Kagadis GC. Comparison of deep learning schemes in grading non-alcoholic fatty liver disease using B-mode ultrasound hepatorenal window images with liver biopsy as the gold standard. Phys Med 2025; 129:104862. [PMID: 39626614 DOI: 10.1016/j.ejmp.2024.104862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 10/11/2024] [Accepted: 11/27/2024] [Indexed: 01/07/2025] Open
Abstract
BACKGROUND/INTRODUCTION To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cortex and liver parenchyma (LP) areas indicated by an expert radiologist. METHODS A total of 112 consecutively enrolled, biopsy-validated NAFLD patients underwent a regular abdominal B-mode US examination. For each patient, a radiologist obtained a B-mode US image containing RK cortex and LP and marked a point between the RK and LP, around which a window was automatically cropped. The cropped image dataset was augmented using up-sampling, and the augmented and non-augmented datasets were sorted by HS grade. Each dataset was split into training (70%) and testing (30%), and fed separately as input to InceptionV3, MobileNetV2, ResNet50, DenseNet201, and NASNetMobile pre-trained DLS. A receiver operating characteristic (ROC) analysis of hepatorenal index (HRI) measurements by the radiologist from the same cropped images was used for comparison with the performance of the DLS. RESULTS With the test data, the DLS reached 89.15 %-93.75 % accuracy when comparing HS grades S0-S1 vs. S2-S3 and 79.69 %-91.21 % accuracy for S0 vs. S1 vs. S2 vs. S3 with augmentation, and 80.45-82.73 % accuracy when comparing S0-S1 vs. S2-S3 and 59.54 %-63.64 % accuracy for S0 vs. S1 vs. S2 vs. S3 without augmentation. The performance of radiologists' HRI measurement after ROC analysis was 82 %, 91.56 %, and 96.19 % for thresholds of S ≥ S1, S ≥ S2, and S = S3, respectively. CONCLUSION All networks achieved high performance in HS assessment. DenseNet201 with the use of augmented data seems to be the most efficient supplementary tool for NAFLD diagnosis and grading.
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Affiliation(s)
- Petros Drazinos
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece; Diagnostic Echotomography SA, Kifissia, GR 14561, Greece
| | - Ilias Gatos
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Paraskevi F Katsakiori
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Stavros Tsantis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Efstratios Syrmas
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Stavros Spiliopoulos
- Second Department of Radiology, School of Medicine, University of Athens, Athens, GR 12461, Greece
| | - Dimitris Karnabatidis
- Department of Radiology, School of Medicine, University of Patras, Patras, GR 26504, Greece
| | | | | | - John D Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - George C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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Alberti G, Cantillo T, Pereira A, De Barbieri F, García C, Villarroel L, Gana JC. Prevalence of Fatty Pancreas and its relation with anthropometric values on the Growth and Obesity Cohort Study. J Pediatr (Rio J) 2024:S0021-7557(24)00145-1. [PMID: 39657903 DOI: 10.1016/j.jped.2024.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 09/26/2024] [Accepted: 09/27/2024] [Indexed: 12/12/2024] Open
Abstract
OBJECTIVE Nonalcoholic Fatty Pancreas Disease (NAFPD) is characterized by excessive lipid accumulation within the pancreas in the absence of alcohol intake, potentially leading to pancreatic dysfunction and metabolic complications, including type 2 diabetes mellitus, acute and chronic pancreatitis, and pancreatic carcinoma. The authors aim to estimate the prevalence of NAFPD and its association with anthropometric parameters in a cohort of Chilean adolescents. METHOD The authors conducted a cross-sectional analysis of the "Growth and Obesity Chilean Cohort Study" (GOCS), a longitudinal study involving nearly 1000 children, followed yearly since 2006. All participants underwent anthropometric measurements and abdominal ultrasonography. RESULTS A total of 741 adolescents were included; 30 exhibited ultrasonography findings compatible with fatty pancreas (4 %). Adolescents with NAFPD had higher BMI z-score (2.33 (1.52-2.69) vs 0.67 (-0.2-1.4), p < 0.001), waist circumference (WC) (90.9 (81.53-98.58) vs 72.2 (67.55-79.83), p < 0.001), waist-to-height ratio (0.55 (0.48-0.6) vs 0.44 (0.41-0.49), p < 0.001), triponderal index (17.35 (15.14-19.25) vs 13.62 (12.07-15.54), p < 0.001), subcutaneous fat (32.4 (21.77-44.95) vs 16.2 (9.3 - 25.3), p < 0.001), visceral fat (45.15 (36.92-62.08) vs 35.5 (28.55-44.25), p < 0.001), systolic blood pressure (p = 0.009), and diastolic blood pressure but only in boys (p = 0.004) compared with controls. The prevalence of liver steatosis was significantly higher in the NAFPD group (63.3% vs 5.2 %, p < 0.001). After adjusting for sex and BMI, only the association with waist circumference and liver steatosis remains statistically significant. CONCLUSION In adolescents, NAFPD has a prevalence of 4 % and is associated with a higher BMI z-score, WC, superficial fat, and blood pressure levels. Liver steatosis exhibited a strong association with NAFPD.
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Affiliation(s)
- Gigliola Alberti
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Ana Pereira
- Instituto de Nutrición y Tecnología de los Alimentos (INTA), Universidad de Chile, Santiago, Chile
| | - Florencia De Barbieri
- Radiology Department, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian García
- Radiology Department, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Luis Villarroel
- Department of Public Health, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Juan Cristóbal Gana
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Homsana A, Southisavath P, Kling K, Hattendorf J, Vorasane S, Paris DH, Sayasone S, Odermatt P, Probst-Hensch N. Steatotic liver disease among lean and non-lean individuals in Southern Lao PDR: a cross-sectional study of risk factors. Ann Med 2024; 56:2329133. [PMID: 38502916 PMCID: PMC10953781 DOI: 10.1080/07853890.2024.2329133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/24/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Steatotic liver disease (SLD) prevalence is rising worldwide, linked to insulin resistance and obesity. SLD prevalence can surpass 10% even among those with normal weight. In Lao People's Democratic Republic (Lao PDR), where Opisthorchis viverrini (OV) trematode infection and type 2 diabetes mellitus (T2DM) are common, infection related liver morbidity such as cholangiocarcinoma (CCA) is high, but data on SLD prevalence is lacking. The objective of this study was to estimate the prevalence and explore determinants of SLD in rural southern Lao PDR for lean and non-lean populations. METHOD A cross-sectional community-based study assessed SLD prevalence using abdominal ultrasonography (US). Factors investigated for association with SLD were identified by interview, serological tests (Hepatitis B surface antigen (HBsAg); lipids and HbA1c), anthropometrical measurements, and parasitological assessments (OV infection). Uni- and multivariable logistic regression analyses with SLD as endpoint were conducted separately for lean (body mass index (BMI) <23.0 kg/m2) and non-lean (BMI ≥ 23.0 kg/m2) participants. RESULT 2,826 participants were included. SLD prevalence was 27.1% (95% confidence interval (95% CI) 24.0%-30.4%), higher among non-lean (39.8%) than lean individuals (17.4%). Lean individuals with OV infection had a statistically significant association with lower odds of SLD (adjusted odds ratio (aOR) 0.49, 95% CI 0.33 - 0.73). T2DM showed a significant positive association with SLD in both lean (aOR 3.58, 95% CI 2.28 - 5.63) and non-lean individuals (aOR 3.31, 95% CI 2.31 - 4.74) while dyslipidemia was significantly associated only in the non-lean group (aOR 1.83, 95% CI 1.09 - 3.07). Females participants exhibited elevated odds of SLD in both lean (aOR 1.43, 95% CI 1.02 - 2.01) and non-lean SLD (aOR 1.50, 95% CI 1.12 - 2.01). CONCLUSION SLD prevalence is notably high among Laotian adults in rural areas, particularly in females and in non-lean individuals. Lean individuals with OV infection exhibited lower SLD prevalence. SLD was more prevalent in individuals with T2DM, independent of BMI. SLD adds to the burden of infection-related liver morbidity in Lao PDR.
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Affiliation(s)
- Anousin Homsana
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane Capital, Lao PDR
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Phonesavanh Southisavath
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane Capital, Lao PDR
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Department of Radiology, Mahosot Hospital, Ministry of Health, Vientiane Capital, Lao PDR
| | - Kerstin Kling
- Immunization Unit, Robert Koch Institute, Berlin, Germany
| | - Jan Hattendorf
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Savina Vorasane
- Department of Radiology, Mahosot Hospital, Ministry of Health, Vientiane Capital, Lao PDR
| | - Daniel Henry Paris
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Somphou Sayasone
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane Capital, Lao PDR
| | - Peter Odermatt
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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Hériard-Dubreuil B, Besson A, Mamou J, Gay J, Foucher J, De Ledinghen V, Cohen-Bacrie C. Ultraportable Quantitative Ultrasound for Hepatic Steatosis Assessment. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1842-1848. [PMID: 39317626 DOI: 10.1016/j.ultrasmedbio.2024.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/18/2024] [Accepted: 08/09/2024] [Indexed: 09/26/2024]
Abstract
OBJECTIVE This study aimed to evaluate the performances of quantitative ultrasound (QUS) for the detection and assessment of hepatic steatosis when implemented using an ultraportable ultrasound scanner. METHODS Seven established QUS parameters were investigated. Ultrasound signals were acquired using a new ultraportable ultrasound device, the Hepatoscope. The feasibility of QUS using the Hepatoscope was first assessed in vitro. Clinical reliability, accuracy and staging capabilities were evaluated in 60 patients referred to a hepatology consultation for known chronic liver disease and enrolled in a prospective clinical investigation using the controlled attenuation parameter (CAP) as ground truth. RESULTS QUS parameters showed moderate (intra-class correlation coefficient [ICC] >0.50) to excellent (ICC >0.90) reliability. Two parameters, namely Lizzi-Feleppa mid-band fit and attenuation, were both reliable (ICC = 0.89 and 0.86, respectively) and correlated with the CAP (squared Pearson correlation coefficient of R2 = 0.65 and R2 = 0.6, respectively). For steatosis detection (S0 vs. ≥S1), the two parameters yielded an area under the receiving operating characteristic curve of 0.90 and 0.86, respectively (95% confidence interval: [0.81-0.99] and [0.76-0.96], respectively). CONCLUSION QUS can be reliably and accurately implemented on ultraportable ultrasound scanners. The combination of ultraportability and quantitative assessment of liver fat is promising for large-scale screening and monitoring of hepatic steatosis.
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Affiliation(s)
| | | | - Jonathan Mamou
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Joël Gay
- E-Scopics, Aix-en-Provence, France
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Rigor J, Martins ME, Passos B, Oliveira R, Martins-Mendes D. Noninvasive tools for the assessment of fibrosis in metabolic dysfunction-associated steatotic liver disease. Minerva Med 2024; 115:660-670. [PMID: 39283245 DOI: 10.23736/s0026-4806.24.09290-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously nonalcoholic fatty liver disease (NAFLD), is the number one chronic liver disorder worldwide. Progression to advanced fibrosis marks the emergence of a significant risk of liver-related negative outcomes. However, only a minority of patients will present at this stage. Since widespread liver biopsy in unfeasible at such high disease prevalence, there was a need to develop noninvasive tests (NITs) that could easily and reliably be applied to patients with MASLD, regardless of clinical setting. The NITs include simple scores, like the fibrosis-4 (FIB-4) Index, patented serum tests, like the Enhanced Liver Fibrosis test (ELF™), and imaging-based modalities, like the vibration-controlled transient elastography (VCTE). Guidelines suggests a stepwise approach that utilizes more than one NIT, with FIB-4 <1.30 being used as a first step to rule out patients that do not need further testing. Subsequent choice of NIT will be influenced by setting, cost, and local availability. While these NITs are accurate, they are not perfect. As such, research is ongoing. A promising avenue is that of omics, a group of technologies that provide concomitant results on a large number of molecules (and other variables). With the advance of artificial intelligence, new NITs may arise from large demographic, biochemical, and radiological data sets.
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Affiliation(s)
- Joana Rigor
- Internal Medicine Department, Unidade Local de Saúde de Póvia de Varzim/Vila do Conde, Vila do Conde, Portugal -
- RISE-UFP, Network of Health Investigation, Fernando Pessoa University, Porto, Portugal -
| | - Maria E Martins
- Internal Medicine Department, Unidade Local de Saúde de Póvia de Varzim/Vila do Conde, Vila do Conde, Portugal
| | - Beatriz Passos
- Internal Medicine Department, Unidade Local de Saúde de Póvia de Varzim/Vila do Conde, Vila do Conde, Portugal
| | - Raquel Oliveira
- Internal Medicine Department, Unidade Local de Saúde de Póvia de Varzim/Vila do Conde, Vila do Conde, Portugal
| | - Daniela Martins-Mendes
- RISE-UFP, Network of Health Investigation, Fernando Pessoa University, Porto, Portugal
- School of Medicine and Biomedical Sciences, Fernando Pessoa University, Porto, Portugal
- FP-I3ID, Fernando Pessoa University, Porto, Portugal
- Department of Biomedicine, Faculty of Medicine, University of Porto, Porto, Portugal
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Homsana A, Southisavath P, Kling K, Hattendorf J, Vorasane S, Paris DH, Probst-Hensch N, Sayasone S, Odermatt P. Burden and risk factors of suspected cholangiocarcinoma in high Opisthorchis viverrini endemic rural communities in southern Lao PDR. PLoS Negl Trop Dis 2024; 18:e0012617. [PMID: 39602377 PMCID: PMC11602099 DOI: 10.1371/journal.pntd.0012617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 10/08/2024] [Indexed: 11/29/2024] Open
Abstract
INTRODUCTION Cholangiocarcinoma (CCA) is a major contributor to hepatobiliary mortality in the Lao People's Democratic Republic (Lao PDR). Infection with the carcinogenic trematode Opisthorchis viverrini (OV), acquired through consumption of insufficiently-cooked river fish, is a known risk factor for the development of CCA. Together with OV, other risk factors contribute to the pathogenesis of CCA. We conducted this study to identify the burden of CCA and identify risk factors in high-risk communities in Lao PDR. METHOD A cross-sectional study was performed in Champasack and Savannakhet provinces, southern Lao PDR, where OV infection is highly endemic. We assessed hepatobiliary morbidity with abdominal ultrasound (US). In addition, multiple risk factors known or suspected to be associated with CCA were assessed such as OV infection (examined by Kato-Katz technique for stool examination), lifestyle risks (e.g. smoking and alcohol consumption by face-to-face questionnaire), co-morbidity (e.g. diabetes mellitus) and hepatitis B infection status, both serologically tested. RESULTS In 3,400 participants, the overall prevalence of suspected CCA was 7.2% (95% confidence interval [95% CI] 5.4-9.6). The suspected CCA prevalence increased with age, and was higher in men at all ages. Almost all participants (88.3%) were infected with OV. In the multivariate regression analysis, suspected CCA was positively associated with OV infection (adjusted odds ratio [aOR] 3.4, 95% CI 1.7-6.5), and a history of cholecystectomy (aOR 2.7, 95% CI 1.5-4.9). CONCLUSION Our CCA screening in high OV prevalence rural areas of Lao PDR uncovers a high public health burden, primarily driven by elevated OV infection rates. Urgent interventions are needed to curb OV infection in these communities. Age and gender disparities in suspected CCA prevalence highlight the need for targeted efforts. Beyond OV, notable factors like a history of cholecystectomy offer valuable insights for preventive strategies. This research enhances our understanding of hepatobiliary morbidity and informs public health initiatives in Lao PDR.
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Affiliation(s)
- Anousin Homsana
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao PDR
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Phonesavanh Southisavath
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao PDR
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Department of Radiology, Mahosot Hospital, Ministry of Health, Vientiane, Lao PDR
| | - Kerstin Kling
- Immunization Unit, Robert Koch Institute, Berlin, Germany
| | - Jan Hattendorf
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Savina Vorasane
- Department of Radiology, Mahosot Hospital, Ministry of Health, Vientiane, Lao PDR
| | - Daniel Henry Paris
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Somphou Sayasone
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao PDR
| | - Peter Odermatt
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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12
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Choorakuttil RM, Chaubal RN, Pratap T, Chelladurai A, Nirmalan PK. Distribution of Normative Percentiles of Liver Stiffness Measurement Using Ultrasound Shear Wave Elastography in an Adult Asian Indian Population. Indian J Radiol Imaging 2024; 34:596-602. [PMID: 39318556 PMCID: PMC11419765 DOI: 10.1055/s-0044-1782163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024] Open
Abstract
Objective The aim of this study was to determine the normative percentiles for liver stiffness measurement (LSM) using shear wave elastography in an adult Asian Indian population as part of the preventive radiology initiative of the Indian Radiological and Imaging Association (IRIA). Methods LSMs were ascertained by two-dimensional (2D) shear wave elastography using the Mindray Resona series of ultrasound machines. The image quality was assessed using the motion stability index (M-STB) and reliability (RLB) map. Ten acquisitions were documented, and an interquartile range-to-median (IQR/M) ratio ≤30% kilopascal (kPa) units was considered a good-quality measurement. A subgroup of the study population without comorbidities was chosen to derive the normative percentile distribution of LSM using a generalized least squares multivariable fractional polynomial regression model that adjusted for sex and body mass index (BMI). The effectiveness of the estimated percentiles was assessed on the entire study population using the greater than 90th percentile value of the LSM as the cutoff for abnormality. Results The study included 852 people who underwent shear wave elastography from June 2022 to July 2023. The magnitude of compensated advanced chronic liver disease (cACLD) and clinically significant portal hypertension (CSPH) was 6.81% (95% confidence interval [CI]: 5.30-8.7) and 4.91% (95% CI: 3.67-6.60), respectively. The normative percentiles were estimated from 282 persons without associated comorbidity and risk factors. The mean age (standard deviation [SD]) of the normal individuals was 40.90 ± 12.92 years, and 210 (71.47%) were males. The mean age (SD) of the 570 persons excluded from the normative percentiles analysis was 47.94 (12.49) years and 72.11% were males. The sex- and BMI-adjusted age-specific 90th percentiles of LSM were 8.76, 8.78, 8.96, 8.97, 9.25, and 9.45 kPa for 18 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 to 70 years, respectively. Conclusion The sex- and BMI-adjusted age-specific 90th percentiles for LSM using shear wave elastography in Asian Indian adults are almost similar to the greater than 9 kPa cutoff proposed by the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement guidelines to discriminate cACLD and CSPH from normal individuals.
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Affiliation(s)
- Rijo M. Choorakuttil
- Department of Preventive Radiology and Integrated Diagnostics, AMMA Center for Diagnosis and Preventive Medicine Pvt. Ltd., Kochi, Kerala, India
| | - Rajas N. Chaubal
- Department of Clinical Radiology, Thane Ultrasound Center, Thane, Mumbai, Maharashtra, India
| | - Thara Pratap
- Department of Clinical Radiology, VPS Lakeshore Hospital & Research Center, Kochi, Kerala
| | - Amarnath Chelladurai
- Department of Radiodiagnosis Stanley Medical College, Chennai, Tamil Nadu, India
| | - Praveen K. Nirmalan
- Department of Research, AMMA Center for Diagnosis and Preventive Medicine Pvt. Ltd., Kochi, Kerala, India
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Byenfeldt M, Kihlberg J, Nasr P, Grönlund C, Lindam A, Bartholomä WC, Lundberg P, Ekstedt M. Altered probe pressure and body position increase diagnostic accuracy for men and women in detecting hepatic steatosis using quantitative ultrasound. Eur Radiol 2024; 34:5989-5999. [PMID: 38459346 PMCID: PMC11364715 DOI: 10.1007/s00330-024-10655-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/14/2023] [Accepted: 01/16/2024] [Indexed: 03/10/2024]
Abstract
OBJECTIVES To evaluate the diagnostic performance of ultrasound guided attenuation parameter (UGAP) for evaluating liver fat content with different probe forces and body positions, in relation to sex, and compared with proton density fat fraction (PDFF). METHODS We prospectively enrolled a metabolic dysfunction-associated steatotic liver disease (MASLD) cohort that underwent UGAP and PDFF in the autumn of 2022. Mean UGAP values were obtained in supine and 30° left decubitus body position with normal 4 N and increased 30 N probe force. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS Among 60 individuals (mean age 52.9 years, SD 12.9; 30 men), we found the best diagnostic performance with increased probe force in 30° left decubitus position (AUC 0.90; 95% CI 0.82-0.98) with a cut-off of 0.58 dB/cm/MHz. For men, the best performance was in supine (AUC 0.91; 95% CI 0.81-1.00) with a cut-off of 0.60 dB/cm/MHz, and for women, 30° left decubitus position (AUC 0.93; 95% CI 0.83-1.00), with a cut-off 0.56 dB/cm/MHz, and increased 30 N probe force for both genders. No difference was in the mean UGAP value when altering body position. UGAP showed good to excellent intra-reproducibility (Intra-class correlation 0.872; 95% CI 0.794-0.921). CONCLUSION UGAP provides excellent diagnostic performance to detect liver fat content in metabolic dysfunction-associated steatotic liver diseases, with good to excellent intra-reproducibility. Regardless of sex, the highest diagnostic accuracy is achieved with increased probe force with men in supine and women in 30° left decubitus position, yielding different cut-offs. CLINICAL RELEVANCE STATEMENT The ultrasound method ultrasound-guided attenuation parameter shows excellent diagnostic accuracy and performs with good to excellent reproducibility. There is a possibility to alter body position and increase probe pressure, and different performances for men and women should be considered for the highest accuracy. KEY POINTS • There is a possibility to alter body position when performing the ultrasound method ultrasound-guided attenuation parameter. • Increase probe pressure for the highest accuracy. • Different performances for men and women should be considered.
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Affiliation(s)
- Marie Byenfeldt
- Department of Radiology in Östersund, Östersund, Sweden.
- Department of Radiation Science, Umeå University, Umeå, Sweden.
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Johan Kihlberg
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Department of Radiology in Linköping, Linköping, Sweden
| | - Patrik Nasr
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | | | - Anna Lindam
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Wolf C Bartholomä
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Department of Radiology in Linköping, Linköping, Sweden
| | - Peter Lundberg
- Department of Radiation Physics, Linköping University, Linköping, Sweden
- Department of Medical and Health Science in Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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14
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Yang M, Chen X, Shen Q, Xiong Z, Liu T, Leng Y, Jiao Y. Development and validation of a predictive nomogram for the risk of MAFLD in postmenopausal women. Front Endocrinol (Lausanne) 2024; 15:1334924. [PMID: 39165508 PMCID: PMC11334217 DOI: 10.3389/fendo.2024.1334924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 07/09/2024] [Indexed: 08/22/2024] Open
Abstract
Background and aim Metabolic-associated fatty liver disease (MAFLD) has gradually become one of the main health concerns regarding liver diseases. Postmenopausal women represent a high-risk group for MAFLD; therefore, it is of great importance to identify and intervene with patients at risk at an early stage. This study established a predictive nomogram model of MAFLD in postmenopausal women and to enhance the clinical utility of the new model, the researchers limited variables to simple clinical and laboratory indicators that are readily obtainable. Methods Data of 942 postmenopausal women from January 2023 to October 2023 were retrospectively collected and divided into two groups according to the collection time: the training group (676 cases) and the validation group (226 cases). Significant indicators independently related to MAFLD were identified through univariate logistic regression and stepwise regression, and the MAFLD prediction nomogram was established. The C-index and calibration curve were used to quantify the nomogram performance, and the model was evaluated by measuring the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results Of 37 variables, 11 predictors were identified, including occupation (worker), body mass index, waist-to-hip ratio, number of abortions, anxiety, hypertension, hyperlipidemia, diabetes, hyperuricemia, and diet (meat and processed meat). The C-index of the training group predicting the related risk factors was 0.827 (95% confidence interval [CI] 0.794-0.860). The C-index of the validation group was 0.787 (95% CI 0.728-0.846). Calibration curves 1 and 2 (BS1000 times) were close to the diagonal, showing a good agreement between the predicted probability and the actual incidence in the two groups. The AUC of the training group was 0.827, the sensitivity was 0.784, and the specificity was 0.735. The AUC of the validation group was 0.787, the sensitivity was 0.674, and the specificity was 0.772. The DCA curve showed that the nomogram had a good net benefit in predicting MAFLD in postmenopausal women. Conclusions A predictive nomogram for MAFLD in postmenopausal women was established and verified, which can assist clinicians in evaluating the risk of MAFLD at an early stage.
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Affiliation(s)
- Ming Yang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
- Department of Liver, Spleen and Gastroenterology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Xingyu Chen
- Department of Liver, Spleen and Gastroenterology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Qiaohui Shen
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Zhuang Xiong
- Department of Liver, Spleen and Gastroenterology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Tiejun Liu
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
- Department of Liver, Spleen and Gastroenterology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Yan Leng
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
- Department of Liver, Spleen and Gastroenterology, First Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China
| | - Yue Jiao
- Department of Intensive Care Unit, Changchun Tongyuan Hospital, Changchun, China
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15
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Qu B, Li Z. Exploring non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease. World J Gastroenterol 2024; 30:3447-3451. [PMID: 39091712 PMCID: PMC11290396 DOI: 10.3748/wjg.v30.i28.3447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/01/2024] [Accepted: 07/08/2024] [Indexed: 07/24/2024] Open
Abstract
The population with metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly common worldwide. Identification of people at risk of progression to advanced stages is necessary to timely offer interventions and appropriate care. Liver biopsy is currently considered the gold standard for the diagnosis and staging of MAFLD, but it has associated risks and limitations. This has spurred the exploration of non-invasive diagnostics for MAFLD, especially for steatohepatitis and fibrosis. These non-invasive approaches mostly include biomarkers and algorithms derived from anthropometric measurements, serum tests, imaging or stool metagenome profiling. However, they still need rigorous and widespread clinical validation for the diagnostic performance.
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Affiliation(s)
- Biao Qu
- Department of Clinical Pharmacology, The Second Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Zheng Li
- Jiangsu Engineering Research Center of Cardiovascular Drugs Targeting Endothelial Cells, College of Health Sciences, School of Life Sciences, Jiangsu Normal University, Xuzhou 221000, Jiangsu Province, China
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16
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Joo JY, Yoo IH, Yang HR. Serologic Biomarkers for Hepatic Fibrosis in Obese Children with Nonalcoholic Steatohepatitis. Pediatr Gastroenterol Hepatol Nutr 2024; 27:236-245. [PMID: 39035406 PMCID: PMC11254650 DOI: 10.5223/pghn.2024.27.4.236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 07/23/2024] Open
Abstract
Purpose The prevalence of nonalcoholic steatohepatitis (NASH) is increasing with the increasing prevalence of childhood obesity. Although NASH has a high risk of progression to liver fibrosis and cirrhosis, few studies have reported noninvasive markers for predicting hepatic fibrosis in children. This study aimed to evaluate and compare the diagnostic accuracies of serologic biomarkers and scoring systems for hepatic fibrosis in obese children with NASH. Methods A total of 96 children were diagnosed with NASH based on liver biopsy findings and divided into two groups according to the degree of liver fibrosis: mild (stage 0-1) or advanced (stage 2-4). Clinical and laboratory parameters and serum levels of hyaluronic acid and type IV collagen were measured. The aspartate aminotransferase/platelet ratio index (APRI) and fibrosis-4 (FIB-4) score were calculated. Results Among the noninvasive markers, only serum type IV collagen level and FIB-4 were significantly different between the two groups. The area under the receiver operating curve of each biomarker and scoring system was 0.80 (95% confidence interval [CI]: 0.70-0.90) for type IV collagen at an optimal cutoff of 148 ng/mL (sensitivity 69.8%, specificity 84.6%), followed by 0.69 (95% CI: 0.57-0.83) for APRI, 0.68 (95% CI: 0.56-0.80) for FIB-4, and 0.65 (95% CI: 0.53-0.77) for hyaluronic acid. Conclusion Type IV collagen as a single noninvasive serologic biomarker for hepatic fibrosis and FIB-4 as a hepatic fibrosis score are beneficial in predicting advanced hepatic fibrosis and determining proper diagnosis and treatment strategies before fibrosis progresses in obese children with NASH.
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Affiliation(s)
- Jung Yeon Joo
- Department of Pediatrics, College of Medicine, Chosun University, Gwangju, Korea
| | - In Hyuk Yoo
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hye Ran Yang
- Department of Pediatrics, Seoul National University Bundang Hospital, Sungnam, Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
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Su X, Xu Q, Li Z, Ren Y, Jiao Q, Wang L, Wang Y. Role of the angiopoietin-like protein family in the progression of NAFLD. Heliyon 2024; 10:e27739. [PMID: 38560164 PMCID: PMC10980950 DOI: 10.1016/j.heliyon.2024.e27739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/04/2024] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most frequent cause of chronic liver disease, with a range of conditions including non-alcoholic fatty liver, non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma (HCC). Currently recognized as the liver component of the metabolic syndrome, NAFLD is intimately linked to metabolic diseases. Angiopoietin-like proteins (ANGPTLs) comprise a class of proteins that resemble angiopoietins structurally. It is closely related to obesity, insulin resistance and lipid metabolism, and may be the critical factor of metabolic syndrome. In recent years, many studies have found that there is a certain correlation between ANGPTLs and the occurrence and progression of NAFLD disease spectrum. This article reviews the possible mechanisms and roles of ANGPTL protein in the pathogenesis and progression of NAFLD.
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Affiliation(s)
- Xin Su
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, China
| | - Qinchen Xu
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, China
| | - Zigan Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, China
| | - Yidan Ren
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China
| | - Qinlian Jiao
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China
| | - Lina Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, China
| | - Yunshan Wang
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China
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18
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Alyavi AL, Sobirova GN, Abdullaev AO, Shadmanova DA. Ways to overcome difficulties in diagnosing non-alcoholic fatty liver disease. EXPERIMENTAL AND CLINICAL GASTROENTEROLOGY 2024:175-181. [DOI: 10.31146/1682-8658-ecg-218-10-175-181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
The purpose of the study is to evaluate the status and capabilities of modern types of diagnostics of non-alcoholic fatty liver disease as part of a meta-analysis of scientific data. Materials and methods. The literature search was carried out in electronic databases Cochrane Library, PubMed.gov, Elsevier.com, Google Scholar. The analysis of the data obtained was focused on works published between 2010 and 2023 (the bias in the form of later studies was used in isolated cases when it came to fundamental scientometric data). Results. After reviewing 693 scientific papers for duplication and inconsistency, 38 sources were selected. Conclusions. The analysis of scientific data revealed that despite the understanding of the pathogenetic causes of non-alcoholic fatty liver disease and the complexity of this disease, liver biopsy still remains the gold standard for assessing liver health. In this regard, there is a need to introduce accessible non-imaging tools and accurate biomarkers, with the help of which it will be possible not only to make an adequate diagnosis, but also to analyze new treatments for NAFLD in clinical trials.
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Affiliation(s)
- A. L. Alyavi
- State Institution “Republican Specialized Scientific and Practical Medical Center for Therapy and Medical Rehabilitation” (RSNPMCT and MR) Tashkent Medical Academy (TMA)
| | - G. N. Sobirova
- State Institution “Republican Specialized Scientific and Practical Medical Center for Therapy and Medical Rehabilitation” (RSNPMCT and MR) Tashkent Medical Academy (TMA)
| | - A. O. Abdullaev
- State Institution “Republican Specialized Scientific and Practical Medical Center for Therapy and Medical Rehabilitation” (RSNPMCT and MR) Tashkent Medical Academy (TMA)
| | - D. A. Shadmanova
- State Institution “Republican Specialized Scientific and Practical Medical Center for Therapy and Medical Rehabilitation” (RSNPMCT and MR) Tashkent Medical Academy (TMA)
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Zafar U, Ahmad MN, Nadeem N, Muhammad Zohaib Uddin M, Zafar B, Baig S, Zafar F, Pervez H, Akram S. Correlation of Grades of Non-alcoholic Fatty Liver on Ultrasound With Blood Parameters. Cureus 2024; 16:e53075. [PMID: 38414673 PMCID: PMC10896709 DOI: 10.7759/cureus.53075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Introduction Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver condition worldwide. NAFLD has been associated with metabolic syndrome and its symptoms, such as type 2 diabetes, hypertension, dyslipidemia, and obesity. Ultrasound is widely used to grade hepatic steatosis, being the most cost-effective, non-invasive, and readily available modality without radiation exposure. The study aimed to assess the correlation of NAFLD grade as seen on ultrasound with blood parameters in a Pakistani population. Materials and methods The included patients were those who were diagnosed with fatty liver disease on ultrasound and whose laboratory tests were available within two weeks of the ultrasound. Two seasoned radiologists rated the severity of NAFLD after looking over ultrasound scans. Consecutive sampling technique was used to minimize selection bias. The degree and direction of the linear relationship between the NAFLD grade and each biochemical parameter were measured using the Pearson correlation coefficient. Results There were 207 patients in all who had been identified with NAFLD on ultrasound, the majority of whom had grade II NAFLD and were in their sixth decade of life. According to Pearson's analysis, the grade of NAFLD had larger positive associations with triglycerides, total cholesterol, low-density lipoprotein, and fasting blood sugar. High density lipoprotein and C-reactive protein were found to have a negative correlation with the grade of NAFLD. Conclusion The findings of the study highlight the correlation between NAFLD grade on ultrasonography and specific blood parameters, implying that managing these biochemical indicators may help to improve hepatic steatosis.
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Affiliation(s)
- Uffan Zafar
- Radiology, Aga Khan University Hospital, Karachi, PAK
| | | | - Naila Nadeem
- Radiology, Aga Khan University Hospital, Karachi, PAK
| | | | - Burhan Zafar
- Radiology, Aga Khan University Hospital, Karachi, PAK
| | - Shazia Baig
- Radiology, Aga Khan University Hospital, Karachi, PAK
| | - Fariha Zafar
- Epidemiology and Public Health, Quaid-e-Azam Medical College, Bahawalpur, PAK
| | - Hafsa Pervez
- Internal Medicine, Dow University of Health Sciences, Civil Hospital Karachi, Karachi, PAK
| | - Saba Akram
- Pathology and Laboratory Medicine, Aga Khan University Hospital, Karachi, PAK
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20
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Quesada-Vázquez S, Castells-Nobau A, Latorre J, Oliveras-Cañellas N, Puig-Parnau I, Tejera N, Tobajas Y, Baudin J, Hildebrand F, Beraza N, Burcelin R, Martinez-Gili L, Chilloux J, Dumas ME, Federici M, Hoyles L, Caimari A, Del Bas JM, Escoté X, Fernández-Real JM, Mayneris-Perxachs J. Potential therapeutic implications of histidine catabolism by the gut microbiota in NAFLD patients with morbid obesity. Cell Rep Med 2023; 4:101341. [PMID: 38118419 PMCID: PMC10772641 DOI: 10.1016/j.xcrm.2023.101341] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 07/18/2023] [Accepted: 11/22/2023] [Indexed: 12/22/2023]
Abstract
The gut microbiota contributes to the pathophysiology of non-alcoholic fatty liver disease (NAFLD). Histidine is a key energy source for the microbiota, scavenging it from the host. Its role in NAFLD is poorly known. Plasma metabolomics, liver transcriptomics, and fecal metagenomics were performed in three human cohorts coupled with hepatocyte, rodent, and Drosophila models. Machine learning analyses identified plasma histidine as being strongly inversely associated with steatosis and linked to a hepatic transcriptomic signature involved in insulin signaling, inflammation, and trace amine-associated receptor 1. Circulating histidine was inversely associated with Proteobacteria and positively with bacteria lacking the histidine utilization (Hut) system. Histidine supplementation improved NAFLD in different animal models (diet-induced NAFLD in mouse and flies, ob/ob mouse, and ovariectomized rats) and reduced de novo lipogenesis. Fecal microbiota transplantation (FMT) from low-histidine donors and mono-colonization of germ-free flies with Enterobacter cloacae increased triglyceride accumulation and reduced histidine content. The interplay among microbiota, histidine catabolism, and NAFLD opens therapeutic opportunities.
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Affiliation(s)
| | - Anna Castells-Nobau
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Nutrition, Eumetabolism, and Health Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Jèssica Latorre
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain
| | - Núria Oliveras-Cañellas
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Nutrition, Eumetabolism, and Health Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Irene Puig-Parnau
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Nutrition, Eumetabolism, and Health Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Noemi Tejera
- Microbes in the Food Chain, Institute Strategic Program, Microbes and Gut Health, Institute Strategic Program - Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Yaiza Tobajas
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, Reus, Spain
| | - Julio Baudin
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, Reus, Spain
| | - Falk Hildebrand
- Microbes in the Food Chain, Institute Strategic Program, Microbes and Gut Health, Institute Strategic Program - Quadram Institute Bioscience, Norwich Research Park, Norwich, UK; Digital Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk NR4 7UZ, UK
| | - Naiara Beraza
- Microbes in the Food Chain, Institute Strategic Program, Microbes and Gut Health, Institute Strategic Program - Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Rémy Burcelin
- Institut National de la Santé et de la Recherche Médicale (INSERM), Toulouse, France; Université Paul Sabatier (UPS), Unité Mixte de Recherche (UMR), Toulouse, France; Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Team 2: 'Intestinal Risk Factors, Diabetes, Dyslipidemia, and Heart Failure', F-31432 Toulouse Cedex 4, France
| | - Laura Martinez-Gili
- Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Julien Chilloux
- Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Imperial College London, Du Cane Road, London W12 0NN, UK; Section of Genomic and Environmental Medicine, National Heart & Lung Institute, Imperial College London, Dovehouse Street, London SW3 6LY, UK; European Genomic Institute for Diabetes, CNRS UMR 8199, INSERM UMR 1283, Institut Pasteur de Lille, Lille University Hospital, University of Lille, 59045 Lille, France; McGill Genome Centre, McGill University, 740 Doctor Penfield Avenue, Montréal, QC H3A 0G1, Canada
| | - Massimo Federici
- Department of Systems Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy
| | - Lesley Hoyles
- Department of Biosciences, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Antoni Caimari
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, Reus, Spain
| | - Josep M Del Bas
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, Reus, Spain
| | - Xavier Escoté
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, Reus, Spain.
| | - José-Manuel Fernández-Real
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Nutrition, Eumetabolism, and Health Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain.
| | - Jordi Mayneris-Perxachs
- Department of Diabetes, Endocrinology, and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Nutrition, Eumetabolism, and Health Group, Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain.
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21
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Hardy R, Klepich J, Mitchell R, Hall S, Villareal J, Ilin C. Improving nonalcoholic fatty liver disease classification performance with latent diffusion models. Sci Rep 2023; 13:21619. [PMID: 38062049 PMCID: PMC10703886 DOI: 10.1038/s41598-023-48062-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of 1.90 compared to 1.67 for GANs, and a minimum FID score of 69.45 compared to 100.05 for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of 0.904 on a NAFLD prediction task.
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Affiliation(s)
- Romain Hardy
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Joe Klepich
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Ryan Mitchell
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | - Steve Hall
- School of Information, U.C. Berkeley, Berkeley, CA, USA
| | | | - Cornelia Ilin
- School of Information, U.C. Berkeley, Berkeley, CA, USA.
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22
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Boeriu A, Dobru D, Fofiu C. Non-Invasive Diagnostic of NAFLD in Type 2 Diabetes Mellitus and Risk Stratification: Strengths and Limitations. Life (Basel) 2023; 13:2262. [PMID: 38137863 PMCID: PMC10744403 DOI: 10.3390/life13122262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/26/2023] [Accepted: 11/25/2023] [Indexed: 12/24/2023] Open
Abstract
The progressive potential of liver damage in type 2 diabetes mellitus (T2DM) towards advanced fibrosis, end-stage liver disease, and hepatocarcinoma has led to increased concern for quantifying liver injury and individual risk assessment. The combination of blood-based markers and imaging techniques is recommended for the initial evaluation in NAFLD and for regular monitoring to evaluate disease progression. Continued development of ultrasonographic and magnetic resonance imaging methods for accurate quantification of liver steatosis and fibrosis, as well as promising tools for the detection of high-risk NASH, have been noted. In this review, we aim to summarize available evidence regarding the usefulness of non-invasive methods for the assessment of NAFLD in T2DM. We focus on the power and limitations of various methods for diagnosis, risk stratification, and patient monitoring that support their implementation in clinical setting or in research field.
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Affiliation(s)
- Alina Boeriu
- Gastroenterology Department, University of Medicine Pharmacy, Sciences, and Technology “George Emil Palade” Targu Mures, 540142 Targu Mures, Romania;
- Gastroenterology Department, Mures County Clinical Hospital, 540103 Targu Mures, Romania
| | - Daniela Dobru
- Gastroenterology Department, University of Medicine Pharmacy, Sciences, and Technology “George Emil Palade” Targu Mures, 540142 Targu Mures, Romania;
- Gastroenterology Department, Mures County Clinical Hospital, 540103 Targu Mures, Romania
| | - Crina Fofiu
- Gastroenterology Department, University of Medicine Pharmacy, Sciences, and Technology “George Emil Palade” Targu Mures, 540142 Targu Mures, Romania;
- Internal Medicine Department, Bistrita County Clinical Hospital, 420094 Bistrita, Romania
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23
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Zalcman M, Barth RA, Rubesova E. Real-time ultrasound-derived fat fraction in pediatric population: feasibility validation with MR-PDFF. Pediatr Radiol 2023; 53:2466-2475. [PMID: 37667050 DOI: 10.1007/s00247-023-05752-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in children. To avoid limitations of liver biopsy and MRI, quantitative ultrasound has become a research focus. Ultrasound-derived fat fraction (UDFF) is based on a combination of backscatter coefficient and attenuation parameter. OBJECTIVE The objectives of the study were to determine (1) agreement between UDFF/MRI proton density fat fraction (MR-PDFF) and (2) whether BMI and age are predictive for UDFF. MATERIALS AND METHODS This cross-sectional prospective study included a convenience sample of 46 children referred for clinically indicated abdominal MRI. MR-PDFF and five acquisitions of UDFF were collected. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to assess agreement between MR-PDFF and UDFF. Receiver operating characteristic curves were calculated for UDFF prediction of liver steatosis (MR-PDFF ≥ 6%). Multivariable regression was performed to assess BMI and age as predictors for UDFF. RESULTS Twenty-two participants were male, 24 were female, and the mean age was 14 ± 3 (range: 7-18) years. Thirty-six out of 46 participants had normal liver fat fraction <6%, and 10/46 had liver steatosis. UDFF was positively associated with MR-PDFF (ICC 0.92 (95% CI, 0.89-0.96). The mean bias between UDFF and MR-PDFF was 0.64% (95% LOA, -5.3-6.6%). AUROC of UDFF for steatosis was of 0.95 (95% CI, 0.89-0.99). UDFF cutoff of 6% had a sensitivity of 90% (95% CI, 55-99%) and a specificity of 94% (95% CI, 81-0.99%). BMI was an independent predictor of UDFF (correlation: 0.55 (95% CI, 0.35-0.95)). CONCLUSIONS UDFF shows strong agreement with MR-PDFF in children. A UDFF cutoff of 6% provides good sensitivity and specificity for detection of MR-PDFF of ≥ 6%.
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Affiliation(s)
- Max Zalcman
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA.
| | - Richard A Barth
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA
| | - Erika Rubesova
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA
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24
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Hwang SM, Cho KY. Noninvasive assessment of paediatric hepatic steatosis by using attenuation imaging. Eur Radiol 2023; 33:8353-8365. [PMID: 37195431 PMCID: PMC10189215 DOI: 10.1007/s00330-023-09731-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 04/20/2023] [Accepted: 04/26/2023] [Indexed: 05/18/2023]
Abstract
OBJECTIVES To evaluate the diagnostic performance of attenuation imaging (ATI) with an ultrasound scanner (US) in the detection of paediatric hepatic steatosis. METHODS Ninety-four prospectively enrolled children were classified into normal weight and overweight/obese (OW/OB) groups according to body mass index (BMI). US findings, including hepatic steatosis grade and ATI value, were examined by two radiologists. Anthropometric and biochemical parameters were obtained, and nonalcoholic fatty liver disease (NAFLD) scores, including the Framingham steatosis index (FSI) and hepatic steatosis index (HSI), were calculated. RESULTS After screening, 49 OW/OB and 40 normal weight children aged 10-18 years old (55 males and 34 females) participated in this study. The ATI value was significantly higher in the OW/OB group than in the normal weight group and showed a significant positive correlation with BMI, serum alanine transferase (ALT), uric acid, and NAFLD scores (p < 0.05). In the multiple linear regression adjusted for age, sex, BMI, ALT, uric acid, and HSI, ATI showed a significant positive association with BMI and ALT (p < 0.05). The receiver operating characteristic analysis showed a very good ability of ATI to predict hepatic steatosis. The intraclass correlation coefficient (ICC) of interobserver variability was 0.92, and the ICCs of intraobserver variability were 0.96 and 0.93 (p < 0.05). According to the two-level Bayesian latent class model analysis, the diagnostic performance of ATI showed the best performance for predicting hepatic steatosis among other known noninvasive NAFLD predictors. CONCLUSIONS This study suggests that ATI is an objective and possible surrogate screening test for detecting hepatic steatosis in paediatric patients with obesity. CLINICAL RELEVANCE STATEMENT Using ATI as a quantitative tool in hepatic steatosis allows clinicians to estimate the extent of the condition and track changes over time. This is helpful for monitoring disease progression and guiding treatment decisions, especially in paediatric practice. KEY POINTS • Attenuation imaging is a noninvasive US-based method for the quantification of hepatic steatosis. • Attenuation imaging values were significantly higher in the OW/OB and steatosis groups than in the normal weight and no steatosis groups, respectively, with a meaningful correlation with known clinical indicators of nonalcoholic fatty liver disease. • Attenuation imaging performs better than other noninvasive predictive models used to diagnose hepatic steatosis.
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Affiliation(s)
- Sook Min Hwang
- Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Seoul, 07441, Korea
| | - Ky Young Cho
- Department of Pediatrics, Hallym University Kangnam Sacred Heart Hospital, 1 Singil-ro, Yeongdeungpo-gu, Seoul, 07441, Korea.
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25
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Frey KL, McLeod MC, Cannon RM, Sheikh SS, Purvis JW, Locke JE, Orandi BJ. Non-invasive evaluation of hepatic macrosteatosis in deceased donors. Am J Surg 2023; 226:692-696. [PMID: 37558520 DOI: 10.1016/j.amjsurg.2023.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/11/2023]
Abstract
INTRODUCTION Liver allocation changes have led to increased travel and expenditures, highlighting the need to efficiently identify marginal livers suitable for transplant. We evaluated the validity of existing non-invasive liver quality tests and a novel machine learning-based model at predicting deceased donor macrosteatosis >30%. METHODS We compared previously-validated non-invasive tests and a novel machine learning-based model to biopsies in predicting macrosteatosis >30%. We also tested them in populations enriched for macrosteatosis. RESULTS The Hepatic Steatosis Index area-under-the-curve (AUC) was 0.56. At the threshold identified by Youden's J statistic, sensitivity, specificity, positive, and negative predictive values were 49.6%, 58.9%, 14.0%, and 89.7%. Other tests demonstrated comparable results. Machine learning produced the highest AUC (0.71). Even in populations enriched for macrosteatosis, no test was sufficiently predictive. CONCLUSION Commonly used clinical scoring systems and a novel machine learning-based model were not clinically useful, highlighting the importance of pre-procurement biopsies to facilitate allocation.
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Affiliation(s)
- Kayla L Frey
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA
| | - M Chandler McLeod
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA
| | - Robert M Cannon
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA
| | - Saulat S Sheikh
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA
| | - Joshua W Purvis
- University of Alabama at Birmingham, Department of Anesthesia, Birmingham, AL, USA
| | - Jayme E Locke
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA
| | - Babak J Orandi
- University of Alabama at Birmingham, Department of Surgery, Division of Transplantation, Birmingham, AL, USA; Weill Cornell Medicine, Department of Medicine, New York, NY, USA.
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26
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Wang X, Bamber JC, Esquivel-Sirvent R, Ormachea J, Sidhu PS, Thomenius KE, Schoen S, Rosenzweig S, Pierce TT. Ultrasonic Sound Speed Estimation for Liver Fat Quantification: A Review by the AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2327-2335. [PMID: 37550173 DOI: 10.1016/j.ultrasmedbio.2023.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 08/09/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a significant cause of diffuse liver disease, morbidity and mortality worldwide. Early and accurate diagnosis of NALFD is critical to identify patients at risk of disease progression. Liver biopsy is the current gold standard for diagnosis and prognosis. However, a non-invasive diagnostic tool is desired because of the high cost and risk of complications of tissue sampling. Medical ultrasound is a safe, inexpensive and widely available imaging tool for diagnosing NAFLD. Emerging sonographic tools to quantitatively estimate hepatic fat fraction, such as tissue sound speed estimation, are likely to improve diagnostic accuracy, precision and reproducibility compared with existing qualitative and semi-quantitative techniques. Various pulse-echo ultrasound speed of sound estimation methodologies have been investigated, and some have been recently commercialized. We review state-of-the-art in vivo speed of sound estimation techniques, including their advantages, limitations, technical sources of variability, biological confounders and existing commercial implementations. We report the expected range of hepatic speed of sound as a function of liver steatosis and fibrosis that may be encountered in clinical practice. Ongoing efforts seek to quantify sound speed measurement accuracy and precision to inform threshold development around meaningful differences in fat fraction and between sequential measurements.
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Affiliation(s)
- Xiaohong Wang
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey C Bamber
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
| | | | | | - Paul S Sidhu
- Department of Radiology, King's College Hospital, London, UK
| | - Kai E Thomenius
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | - Scott Schoen
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA
| | | | - Theodore T Pierce
- Center for Ultrasound Research and Translation, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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27
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Kim YY, Cho SB, Lee JS, Lee HW, Choi JY, Kim SU. Utility of fusion imaging for the evaluation of ultrasound quality in hepatocellular carcinoma surveillance. Ultrasonography 2023; 42:580-588. [PMID: 37722723 PMCID: PMC10555691 DOI: 10.14366/usg.23106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 09/20/2023] Open
Abstract
PURPOSE This study evaluated the quality of surveillance ultrasound (US) for hepatocellular carcinoma (HCC) utilizing fusion imaging. METHODS This research involved a secondary analysis of a prospectively recruited cohort. Under institutional review board approval, participants referred for surveillance US who had undergone liver computed tomography (CT) or magnetic resonance imaging (MRI) within the past year were screened between August 2022 and January 2023. After patient consent was obtained, the US visualization score in the Liver Imaging Reporting and Data System was assessed with fusion imaging at the time of examination. This score was compared to that of conventional US using the extended McNemar test. Multivariable logistic regression analysis was used to identify factors independently associated with a US visualization score of B or C. Factors limiting visualization of focal lesions were recorded during fusion imaging. RESULTS Among the 105 participants (mean age, 59±11 years; 66 men), US visualization scores of B and C were assigned to 57 (54.3%) and 17 (16.2%) participants, respectively, by conventional US and 54 (51.4%) and 32 (30.5%) participants, respectively, by fusion imaging. The score distribution differed significantly between methods (P=0.010). Male sex was independently associated with US visualization scores of B or C (adjusted odds ratio, 3.73 [95% confidence interval, 1.30 to 10.76]; P=0.015). The most common reason (64.5%) for lesion nondetection was a limited sonic window. CONCLUSION Conventional US may underestimate the limitations of the sonic window relative to real-time fusion imaging with pre-acquired CT or MRI in the surveillance of HCC.
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Affiliation(s)
- Yeun-Yoon Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seo-Bum Cho
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jae Seung Lee
- Department of Internal Medicine and Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Won Lee
- Department of Internal Medicine and Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jin-Young Choi
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Up Kim
- Department of Internal Medicine and Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A. Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology 2023; 309:e230659. [PMID: 37787678 DOI: 10.1148/radiol.230659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.
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Affiliation(s)
- Pedro Vianna
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Sara-Ivana Calce
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Pamela Boustros
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Cassandra Larocque-Rigney
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Laurent Patry-Beaudoin
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Yi Hui Luo
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emre Aslan
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - John Marinos
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Talal M Alamri
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Kim-Nhien Vu
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jessica Murphy-Lavallée
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jean-Sébastien Billiard
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emmanuel Montagnon
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Hongliang Li
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Samuel Kadoury
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Bich N Nguyen
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Shanel Gauthier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Benjamin Therien
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Irina Rish
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Eugene Belilovsky
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Wolf
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Michaël Chassé
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Cloutier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - An Tang
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
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Nagata T, Funakoshi S, Morihara D, Shakado S, Yokoyama K, Takata K, Tanaka T, Fukunaga A, Yamauchi R, Fukuda H, Matsuoka H, Imakiire S, Sakisaka H, Matsuoka S, Kuno N, Abe K, Ishibashi H, Ashizuka S, Hirai F. Malnutrition and inflammation status in nonobese patients with inflammatory bowel disease are associated with nonalcoholic fatty liver disease: a retrospective study. Intest Res 2023; 21:471-480. [PMID: 37559192 PMCID: PMC10626015 DOI: 10.5217/ir.2023.00035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/19/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND/AIMS The frequency and details of nonalcoholic fatty liver disease (NAFLD) complications in patients with inflammatory bowel disease (IBD) remain unclear. This study aimed to clarify characteristics of NAFLD in patients with IBD. METHODS We retrospectively identified and enrolled patients with IBD diagnosed with or without NAFLD by undergoing abdominal computed tomography (CT) at our institution between 2005 and 2020. The primary endpoint was the complication rate of NAFLD in patients with IBD. Secondary endpoints were the clinical characteristics of nonobese patients with IBD and comorbid NAFLD and their association with nutritional and inflammatory parameters. RESULTS Twenty-one (21.9%) of 96 eligible patients with IBD also had NAFLD. In nonobese patients (defined as patients with a body mass index <25 kg/m2), C-reactive protein (CRP; P<0.001) and alanine aminotransferase (P=0.018) levels were higher and the albumin level (P=0.005) and prognostic nutritional index (PNI; P=0.002) values were lower in patients with NAFLD than in those without NAFLD. The PNI value was positively correlated (P<0.001) and the CRP level was negatively correlated (P=0.001) with the hepatosplenic ratio. However, in the NAFLD combined group, PNI (P<0.05) and CRP values (P<0.001) were improved over time after CT imaging by continuing IBD treatment. CONCLUSIONS Worsening nutritional and inflammatory status in IBD patients is associated with complications of NAFLD. Diagnosis of NAFLD in IBD patients using CT imaging might be useful not only for early detection of NAFLD but also in assessing the need for therapeutic intervention for IBD.
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Affiliation(s)
- Takahiro Nagata
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Sadahiro Funakoshi
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Daisuke Morihara
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Satoshi Shakado
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Keiji Yokoyama
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Kazuhide Takata
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Takashi Tanaka
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Atsushi Fukunaga
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Ryo Yamauchi
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Hiromi Fukuda
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Hiroki Matsuoka
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - So Imakiire
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Hideto Sakisaka
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Satoshi Matsuoka
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Nobuaki Kuno
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Koichi Abe
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Hideki Ishibashi
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Shinya Ashizuka
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Fumihito Hirai
- Department of Gastroenterology and Medicine, Fukuoka University Faculty of Medicine, Fukuoka, Japan
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Yazdani L, Rafati I, Gesnik M, Nicolet F, Chayer B, Gilbert G, Volniansky A, Olivié D, Giard JM, Sebastiani G, Nguyen BN, Tang A, Cloutier G. Ultrasound Shear Wave Attenuation Imaging for Grading Liver Steatosis in Volunteers and Patients With Non-alcoholic Fatty Liver Disease: A Pilot Study. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2264-2272. [PMID: 37482477 DOI: 10.1016/j.ultrasmedbio.2023.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/25/2023]
Abstract
OBJECTIVE The aims of the work described here were to assess shear wave attenuation (SWA) in volunteers and patients with non-alcoholic fatty liver disease (NAFLD) and compare its diagnostic performance with that of shear wave dispersion (SWD), magnetic resonance imaging (MRI) proton density fat fraction (PDFF) and biopsy. METHODS Forty-nine participants (13 volunteers and 36 NAFLD patients) were enrolled. Ultrasound and MRI examinations were performed in all participants. Biopsy was also performed in patients. SWA was used to assess histopathology grades as potential confounders. The areas under curves (AUCs) of SWA, SWD and MRI-PDFF were assessed in different steatosis grades by biopsy. Youden's thresholds of SWA were obtained for steatosis grading while using biopsy or MRI-PDFF as the reference standard. RESULTS Spearman's correlations of SWA with histopathology (steatosis, inflammation, ballooning and fibrosis) were 0.89, 0.73, 0.62 and 0.31, respectively. Multiple linear regressions of SWA confirmed the correlation with steatosis grades (adjusted R2 = 0.77, p < 0.001). The AUCs of MRI-PDFF, SWA and SWD were respectively 0.97, 0.99 and 0.94 for S0 versus ≥S1 (p > 0.05); 0.94, 0.98 and 0.78 for ≤S1 versus ≥S2 (both MRI-PDFF and SWA were higher than SWD, p < 0.05); and 0.90, 0.93 and 0.68 for ≤S2 versus S3 (both SWA and MRI-PDFF were higher than SWD, p < 0.05). SWA's Youden thresholds (Np/m/Hz) (sensitivity, specificity) for S0 versus ≥S1, ≤S1 versus ≥S2 and ≤S2 versus S3 were 1.05 (1.00, 0.92), 1.37 (0.96, 0.96) and 1.51 (0.83, 0.87), respectively. These values were 1.16 (1.00, 0.81), 1.49 (0.91, 0.82) and 1.67 (0.87, 0.92) when considering MRI-PDFF as the reference standard. CONCLUSION In this pilot study, SWA increased with increasing steatosis grades, and its diagnostic performance was higher than that of SWD but equivalent to that of MRI-PDFF.
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Affiliation(s)
- Ladan Yazdani
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
| | - Iman Rafati
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
| | - Marc Gesnik
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Frank Nicolet
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Boris Chayer
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON, Canada; Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QQ, Canada
| | - Anton Volniansky
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QQ, Canada
| | - Damien Olivié
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QQ, Canada
| | | | - Giada Sebastiani
- Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, QC, Canada
| | - Bich N Nguyen
- Service of Pathology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
| | - An Tang
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QQ, Canada; Laboratory of Clinical Image Processing, CRCHUM, Montréal, QC, Canada
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada; Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada; Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, QQ, Canada.
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Buitinga M, Veeraiah P, Haans F, Schrauwen-Hinderling VB. Ectopic lipid deposition in muscle and liver, quantified by proton magnetic resonance spectroscopy. Obesity (Silver Spring) 2023; 31:2447-2459. [PMID: 37667838 DOI: 10.1002/oby.23865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 09/06/2023]
Abstract
Advances in the development of noninvasive imaging techniques have spurred investigations into ectopic lipid deposition in the liver and muscle and its implications in the development of metabolic diseases such as type 2 diabetes. Computed tomography and ultrasound have been applied in the past, though magnetic resonance-based methods are currently considered the gold standard as they allow more accurate quantitative detection of ectopic lipid stores. This review focuses on methodological considerations of magnetic resonance-based methods to image hepatic and muscle fat fractions, and it emphasizes anatomical and morphological aspects and how these may influence data acquisition, analysis, and interpretation.
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Affiliation(s)
- Mijke Buitinga
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Nutrition and Movement Sciences (NUTRIM), Maastricht University, Maastricht, The Netherlands
| | - Pandichelvam Veeraiah
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Scannexus (Ultra-High Field Imaging Center), Maastricht, The Netherlands
- Faculty of Health Medicine and Life Sciences (FHML), Maastricht, The Netherlands
| | - Florian Haans
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Vera B Schrauwen-Hinderling
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Nutrition and Movement Sciences (NUTRIM), Maastricht University, Maastricht, The Netherlands
- Institute for Clinical Diabetology, German Diabetes Center and Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
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Liao Y, Liu L, Yang J, Zhou X, Teng X, Li Y, Wan Y, Yang J, Shi Z. Analysis of clinical features and identification of risk factors in patients with non-alcoholic fatty liver disease based on FibroTouch. Sci Rep 2023; 13:14812. [PMID: 37684380 PMCID: PMC10491815 DOI: 10.1038/s41598-023-41596-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
Our aim was to explore the correlation between ultrasound attenuation parameter (UAP) and liver stiffness measurement (LSM) based on FibroTouch (China) and clinical features in patients with non-alcoholic fatty liver disease (NAFLD), so as to provide a certain basis for the clinical application of FibroTouch in NAFLD. Hepatic steatosis and fibrosis in patients with NAFLD were graded according to FibroTouch, and the relationship between steatosis and fibrosis levels and clinical characteristics was retrospectively analyzed. Hepatic steatosis was positively related with weight, BMI, waist, hyperlipidemia, hyperuricemia, FBG, UA, TG, ALT, AST, GGT, LSM and hepatic fibrosis grading, and was negatively related with gender (male), age and AST/ALT ratio. Hepatic fibrosis was positively related with age, BMI, waist, hypertension, FBG, ALT, AST, GGT, NFS, APRI, FIB-4, UAP and hepatic steatosis grading, and was negatively related with blood platelet (PLT) counts. Moreover, BMI, waist, TG, ALT and LSM were independent risk factors of hepatic steatosis, while decreased PLT counts, AST and UAP were independent risk factors of hepatic fibrosis. Body mass parameters, metabolic risk factors and liver function indicators increase the risk of hepatic steatosis and fibrosis in patients with NAFLD, and UAP and LSM can interact with each other.
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Affiliation(s)
- Yan Liao
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China.
| | - Lei Liu
- Department of Pancreatic Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiayao Yang
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Xiaoli Zhou
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Xiaoli Teng
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Yixi Li
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Ying Wan
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Jian Yang
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
| | - Zhaohong Shi
- Department of Gastroenterology, Wuhan No.1 Hospital, Wuhan, China
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Gawrieh S, Lake JE, Debroy P, Sjoquist JA, Robison M, Tann M, Akisik F, Bhamidipalli SS, Saha CK, Zachary K, Robbins GK, Gupta SK, Chung RT, Chalasani N, Corey KE. Burden of fatty liver and hepatic fibrosis in persons with HIV: A diverse cross-sectional US multicenter study. Hepatology 2023; 78:578-591. [PMID: 36805976 PMCID: PMC10496090 DOI: 10.1097/hep.0000000000000313] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 01/02/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND AIMS The current prevalence of fatty liver disease (FLD) due to alcohol-associated (AFLD) and nonalcoholic (NAFLD) origins in US persons with HIV (PWH) is not well defined. We prospectively evaluated the burden of FLD and hepatic fibrosis in a diverse cohort of PWH. APPROACH RESULTS Consenting participants in outpatient HIV clinics in 3 centers in the US underwent detailed phenotyping, including liver ultrasound and vibration-controlled transient elastography for controlled attenuation parameter and liver stiffness measurement. The prevalence of AFLD, NAFLD, and clinically significant and advanced fibrosis was determined. Univariate and multivariate logistic regression models were used to evaluate factors associated with the risk of NAFLD. Of 342 participants, 95.6% were on antiretroviral therapy, 93.9% had adequate viral suppression, 48.7% (95% CI 43%-54%) had steatosis by ultrasound, and 50.6% (95% CI 45%-56%) had steatosis by controlled attenuation parameter ≥263 dB/m. NAFLD accounted for 90% of FLD. In multivariable analysis, old age, higher body mass index, diabetes, and higher alanine aminotransferase, but not antiretroviral therapy or CD4 + cell count, were independently associated with increased NAFLD risk. In all PWH with fatty liver, the frequency of liver stiffness measurement 8-12 kPa was 13.9% (95% CI 9%-20%) and ≥12 kPa 6.4% (95% CI 3%-11%), with a similar frequency of these liver stiffness measurement cutoffs in NAFLD. CONCLUSIONS Nearly half of the virally-suppressed PWH have FLD, 90% of which is due to NAFLD. A fifth of the PWH with FLD has clinically significant fibrosis, and 6% have advanced fibrosis. These data lend support to systematic screening for high-risk NAFLD in PWH.
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Affiliation(s)
- Samer Gawrieh
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Jordan E. Lake
- Division of Infectious Diseases, Department of Medicine, University of Texas Health Science Center at Houston, Houston, TX
| | - Paula Debroy
- Division of Infectious Diseases, Department of Medicine, University of Texas Health Science Center at Houston, Houston, TX
| | - Julia A. Sjoquist
- Liver Center, Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Montreca Robison
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Mark Tann
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN
| | - Fatih Akisik
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN
| | - Surya S. Bhamidipalli
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN
| | - Chandan K. Saha
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN
| | - Kimon Zachary
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Gregory K. Robbins
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Samir K. Gupta
- Division of Infectious Diseases, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Raymond T. Chung
- Liver Center, Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Naga Chalasani
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Kathleen E. Corey
- Liver Center, Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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Bulakci M, Ercan CC, Karapinar E, Aksakal MZT, Aliyev S, Bicen F, Sahin AY, Salmaslioglu A. Quantitative evaluation of hepatic steatosis using attenuation imaging in a pediatric population: a prospective study. Pediatr Radiol 2023; 53:1629-1639. [PMID: 36881143 DOI: 10.1007/s00247-023-05615-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Obesity and fatty-liver disease are increasingly common in children. Hepatic steatosis is becoming the most common cause of chronic liver disease during childhood. There is a need for noninvasive imaging methods that are easily accessible, safe and do not require sedation in the diagnosis and follow-up of the disease. OBJECTIVE In this study, the diagnostic role of ultrasound attenuation imaging (ATI) in the detection and staging of fatty liver in the pediatric age group was investigated using the magnetic resonance imaging (MRI)-proton density fat fraction as the reference. MATERIALS AND METHODS A total of 140 children with both ATI and MRI constituted the study group. Fatty liver was classified as mild (S1, defined as ≥ 5% steatosis), moderate (S2, defined as ≥ 10% steatosis), or severe (S3, defined as ≥ 20% steatosis) according to MRI-proton density fat fraction values. MRI studies were performed on the same 1.5-tesla (T) MR device without sedation and contrast agent. Ultrasound examinations were performed independently by two radiology residents blinded to the MRI data. RESULTS While no steatosis was detected in half of the cases, S1 steatosis was found in 31 patients (22.1%), S2 in 29 patients (20.7%) and S3 in 10 patients (7.1%). A strong correlation was found between attenuation coefficient and MRI-proton density fat fraction values (r = 0.88, 95% CI 0.84-0.92; P < 0.001). The area under the receiver operating characteristic curve values of ATI were calculated as 0.944 for S > 0, 0.976 for S > 1 and 0.970 for S > 2, based on 0.65, 0.74 and 0.91 dB/cm/MHz cut-off values, respectively. The intraclass correlation coefficient values for the inter-observer agreement and test-retest reproducibility were calculated as 0.90 and 0.91, respectively. CONCLUSION Ultrasound attenuation imaging is a promising noninvasive method for the quantitative evaluation of fatty liver disease.
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Affiliation(s)
- Mesut Bulakci
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Topkapi Mahallesi, Turgut Ozal Caddesi, No:118, 34093, Fatih, Istanbul, Turkey.
| | - Celal Caner Ercan
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Topkapi Mahallesi, Turgut Ozal Caddesi, No:118, 34093, Fatih, Istanbul, Turkey
| | - Edanur Karapinar
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Topkapi Mahallesi, Turgut Ozal Caddesi, No:118, 34093, Fatih, Istanbul, Turkey
| | | | - Shamil Aliyev
- Department of Radiology, Faculty of Medicine, Istinye University, Istanbul, Turkey
| | - Fuat Bicen
- Department of Radiology and Neuroradiology, Klinikum Barnim GmbH, Werner Forssmann Hospital, Eberswalde, Germany
| | - Aylin Yetim Sahin
- Department of Pediatrics, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Artur Salmaslioglu
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Topkapi Mahallesi, Turgut Ozal Caddesi, No:118, 34093, Fatih, Istanbul, Turkey
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Fetzer DT, Pierce TT, Robbin ML, Cloutier G, Mufti A, Hall TJ, Chauhan A, Kubale R, Tang A. US Quantification of Liver Fat: Past, Present, and Future. Radiographics 2023; 43:e220178. [PMID: 37289646 DOI: 10.1148/rg.220178] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.
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Affiliation(s)
- David T Fetzer
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Theodore T Pierce
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Michelle L Robbin
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Guy Cloutier
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Arjmand Mufti
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Timothy J Hall
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Anil Chauhan
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Reinhard Kubale
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - An Tang
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
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Yao Y, Zhang Z, Peng B, Tang J. Bio-Inspired Network for Diagnosing Liver Steatosis in Ultrasound Images. Bioengineering (Basel) 2023; 10:768. [PMID: 37508795 PMCID: PMC10376777 DOI: 10.3390/bioengineering10070768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Using ultrasound imaging to diagnose liver steatosis is of great significance for preventing diseases such as cirrhosis and liver cancer. Accurate diagnosis under conditions of low quality, noise and poor resolutions is still a challenging task. Physiological studies have shown that the visual cortex of the biological visual system has selective attention neural mechanisms and feedback regulation of high features to low features. When processing visual information, these cortical regions selectively focus on more sensitive information and ignore unimportant details, which can effectively extract important features from visual information. Inspired by this, we propose a new diagnostic network for hepatic steatosis. In order to simulate the selection mechanism and feedback regulation of the visual cortex in the ventral pathway, it consists of a receptive field feature extraction module, parallel attention module and feedback connection. The receptive field feature extraction module corresponds to the inhibition of the non-classical receptive field of V1 neurons on the classical receptive field. It processes the input image to suppress the unimportant background texture. Two types of attention are adopted in the parallel attention module to process the same visual information and extract different important features for fusion, which improves the overall performance of the model. In addition, we construct a new dataset of fatty liver ultrasound images and validate the proposed model on this dataset. The experimental results show that the network has good performance in terms of sensitivity, specificity and accuracy for the diagnosis of fatty liver disease.
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Affiliation(s)
- Yuan Yao
- General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610044, China
| | - Zhenguang Zhang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China
| | - Bo Peng
- School of Computing and Artificial Intelligent, Southwest Jiaotong University, Chengdu 611756, China
| | - Jin Tang
- Tiaodenghe Community Health Service Center, Chengdu 610066, China
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Collin R, Magnin B, Gaillard C, Nicolas C, Abergel A, Buchard B. Prospective study comparing hepatic steatosis assessment by magnetic resonance imaging and four ultrasound methods in 105 successive patients. World J Gastroenterol 2023; 29:3548-3560. [PMID: 37389233 PMCID: PMC10303516 DOI: 10.3748/wjg.v29.i22.3548] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/04/2023] [Accepted: 05/12/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is becoming a major health problem, resulting in hepatic, metabolic and cardio-vascular morbidity.
AIM To evaluate new ultrasonographic tools to detect and measure hepatic steatosis.
METHODS We prospectively included 105 patients referred to our liver unit for NAFLD suspicion or follow-up. They underwent ultrasonographic measurement of liver sound speed estimation (SSE) and attenuation coefficient (AC) using Aixplorer MACH 30 (Supersonic Imagine, France), continuous controlled attenuation parameter (cCAP) using Fibroscan (Echosens, France) and standard liver ultrasound with hepato-renal index (HRI) calculation. Hepatic steatosis was then classified according to magnetic resonance imaging proton density fat fraction (PDFF). Receiver operating curve (ROC) analysis was performed to evaluate the diagnostic performance in the diagnosis of steatosis.
RESULTS Most patients were overweight or obese (90%) and had metabolic syndrome (70%). One third suffered from diabetes. Steatosis was identified in 85 patients (81%) according to PDFF. Twenty-one patients (20%) had advanced liver disease. SSE, AC, cCAP and HRI correlated with PDFF, with respective Spearman correlation coefficient of -0.39, 0.42, 0.54 and 0.59 (P < 0.01). Area under the receiver operating characteristic curve (AUROC) for detection of steatosis with HRI was 0.91 (0.83-0.99), with the best cut-off value being 1.3 (Se = 83%, Sp = 98%). The optimal cCAP threshold of 275 dB/m, corresponding to the recent EASL-suggested threshold, had a sensitivity of 72% and a specificity of 80%. Corresponding AUROC was 0.79 (0.66-0.92). The diagnostic accuracy of cCAP was more reliable when standard deviation was < 15 dB/m with an AUC of 0.91 (0.83-0.98). An AC threshold of 0.42 dB/cm/MHz had an AUROC was 0.82 (0.70-0.93). SSE performed moderately with an AUROC of 0.73 (0.62-0.84).
CONCLUSION Among all ultrasonographic tools evaluated in this study, including new-generation tools such as cCAP and SSE, HRI had the best performance. It is also the simplest and most available method as most ultrasound scans are equipped with this module.
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Affiliation(s)
- Remi Collin
- Gastroenterology and Endoscopy Unit, Dupuytren University Hospital, Limoges 87000, France
- Department of Hepatology and Gastroenterology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
| | - Benoit Magnin
- Department of Radiology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
| | - Constance Gaillard
- Department of Radiology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
| | - Carine Nicolas
- Department of Hepatology and Gastroenterology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
| | - Armand Abergel
- Department of Hepatology and Gastroenterology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
| | - Benjamin Buchard
- Department of Hepatology and Gastroenterology, Clermont-Ferrand University Hospital, Clermont-Ferrand 63000, France
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Schreiner AD, Sattar N. Identifying Patients with Nonalcoholic Fatty Liver Disease in Primary Care: How and for What Benefit? J Clin Med 2023; 12:4001. [PMID: 37373694 DOI: 10.3390/jcm12124001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Despite its increasing prevalence, nonalcoholic fatty liver disease (NAFLD) remains under-diagnosed in primary care. Timely diagnosis is critical, as NAFLD can progress to nonalcoholic steatohepatitis, fibrosis, cirrhosis, hepatocellular carcinoma, and death; furthermore, NAFLD is also a risk factor linked to cardiometabolic outcomes. Identifying patients with NAFLD, and particularly those at risk of advanced fibrosis, is important so that healthcare practitioners can optimize care delivery in an effort to prevent disease progression. This review debates the practical issues that primary care physicians encounter when managing NAFLD, using a patient case study to illustrate the challenges and decisions that physicians face. It explores the pros and cons of different diagnostic strategies and tools that physicians can adopt in primary care settings, depending on how NAFLD presents and progresses. We discuss the importance of prescribing lifestyle changes to achieve weight loss and mitigate disease progression. A diagnostic and management flow chart is provided, showing the key points of assessment for primary care physicians. The advantages and disadvantages of advanced fibrosis risk assessments in primary care settings and the factors that influence patient referral to a hepatologist are also reviewed.
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Affiliation(s)
- Andrew D Schreiner
- Department of Medicine, Medical University of South Carolina, 171 Ashley Ave, Charleston, SC 29425, USA
| | - Naveed Sattar
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK
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Lei P, Hu N, Wu Y, Tang M, Lin C, Kong L, Zhang L, Luo P, Chan LW. Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases. IRADIOLOGY 2023; 1:167-189. [DOI: 10.1002/ird3.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/18/2023] [Indexed: 01/04/2025]
Abstract
AbstractThe liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.
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Affiliation(s)
- Pinggui Lei
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Na Hu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Yuhui Wu
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Maowen Tang
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Chong Lin
- Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang Guizhou China
| | - Luoyi Kong
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Lingfeng Zhang
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
| | - Peng Luo
- School of Public Health Guizhou Medical University Guiyang Guizhou China
| | - Lawrence Wing‐Chi Chan
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Kowloon Hong Kong SAR China
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Zeng KY, Bao WYG, Wang YH, Liao M, Yang J, Huang JY, Lu Q. Non-invasive evaluation of liver steatosis with imaging modalities: New techniques and applications. World J Gastroenterol 2023; 29:2534-2550. [PMID: 37213404 PMCID: PMC10198053 DOI: 10.3748/wjg.v29.i17.2534] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/26/2023] [Accepted: 04/11/2023] [Indexed: 05/23/2023] Open
Abstract
In the world, nonalcoholic fatty liver disease (NAFLD) accounts for majority of diffuse hepatic diseases. Notably, substantial liver fat accumulation can trigger and accelerate hepatic fibrosis, thus contributing to disease progression. Moreover, the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases. Therefore, early detection and quantified measurement of hepatic fat content are of great importance. Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis. However, liver biopsy has several limitations, namely, its invasiveness, sampling error, high cost and moderate intraobserver and interobserver reproducibility. Recently, various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content, including ultrasound- or magnetic resonance-based methods. These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content, which is useful for longitudinal follow-up. In this review, we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.
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Affiliation(s)
- Ke-Yu Zeng
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Wu-Yong-Ga Bao
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yun-Han Wang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Min Liao
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jie Yang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jia-Yan Huang
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Qiang Lu
- Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
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Adi O, Fong CP, Sallehuddin RM, Ahmad AH, Sum KM, Yusof ZM, Via G, Tavazzi G. Airway ultrasound to detect subglottic secretion above endotracheal tube cuff. Ultrasound J 2023; 15:23. [PMID: 37148375 PMCID: PMC10164205 DOI: 10.1186/s13089-023-00318-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/31/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Subglottic secretion had been proven as one of the causes of microaspiration and increased risk of ventilator-associated pneumonia (VAP). The role of ultrasound to detect subglottic secretion has not yet been established. PURPOSE The purpose of this study is to determine the sensitivity and specificity of upper airway ultrasound (US) in the detection of subglottic secretions as compared to computed tomography (CT) scanning. MATERIAL AND METHODS A prospective observational study was carried out in adult trauma patients requiring mechanical ventilation and cervical CT scan. All patients had an endotracheal tube cuff-pressure maintained between 20 and 30 cm H2O. Airway US was performed at the bedside immediately before the patient was transferred to the CT scan suite. The sensitivity, specificity, and positive/negative predictive values (PPV, NPV) of the upper airway US detection of subglottic secretions were then calculated and compared with CT findings. RESULTS Fifty participants were consecutively included. Subglottic secretions were detected in 31 patients using upper airway US. The sensitivity and specificity of upper airway US in detecting subglottic secretion were 96.7% and 90%, respectively (PPV 93.5%, NPV 94.7%). 18 (58%) patients with subglottic secretions developed VAP during their ICU stay (p = 0.01). The area under the receiver operating curve (AUROC) was 0.977 (95% CI 0.936-1.00). CONCLUSIONS Upper airway US is a useful tool for detecting subglottic secretions with high sensitivity and specificity. CLINICAL IMPLICATIONS This study shows: 1. Upper airway US may aid in detecting subglottic secretions, which are linked to VAP. 2. Detecting subglottic secretions at the bedside aids in determining the best frequency of subglottic aspiration to clean the subglottic trachea. 3. Upper airway US may also aid in detecting the correct ETT position. Trial registration Clinicaltrials.gov. CLINICALTRIALS gov identifier NCT04739878 Date of registration 2nd May 2021 URL of trial registry record https://clinicaltrials.gov/ct2/show/NCT04739878 .
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Affiliation(s)
- Osman Adi
- Resuscitation and Emergency Critical Care Unit (RECCU), Trauma and Emergency Department, Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia.
| | - Chan Pei Fong
- Resuscitation and Emergency Critical Care Unit (RECCU), Trauma and Emergency Department, Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia
| | | | - Azma Haryaty Ahmad
- Resuscitation and Emergency Critical Care Unit (RECCU), Trauma and Emergency Department, Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia
| | - Kok Meng Sum
- Department of Anesthesiology and Intensive Care, Beacon Hospital, No. 1, Jalan 215, Off Jalan Templer, Section 51, 46050, Petaling Jaya, Selangor, Malaysia
| | - Zulrushdi Md Yusof
- Department of Radiology, Raja Permaisuri Bainun Hospital, Jalan Raja Ashman (Jalan Hospital), Ipoh, Perak, Malaysia
| | - Gabriele Via
- Cardiac Anesthesia and Intensive Care - Cardiocentro Ticino, Lugano, Switzerland
| | - Guido Tavazzi
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, DEA Piano-1, Fondazione IRCCS Policlinico S. Matteo, Viale Golgi 19, 27100, Pavia, Italy
- Department of Anesthesia and Intensive Care Unit, Fondazione IRCCS Policlinico S. Matteo, Pavia, Italy
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Haq A, Fraum TJ, Tao Y, Dehdashti F, LeBlanc M, Hoegger MJ, Luo J, Weilbaecher K, Peterson LL. Frequency of Hepatic Metastatic Disease in Patients with Stage IV Breast Cancer Is Similar for Steatotic and Non-Steatotic Livers. Breast Cancer (Auckl) 2023; 17:11782234231166476. [PMID: 37181949 PMCID: PMC10170590 DOI: 10.1177/11782234231166476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 03/13/2023] [Indexed: 05/16/2023] Open
Abstract
Background Breast cancer is the most common non-cutaneous malignancy and the second leading cause of cancer mortality in the United States. Breast cancer is a heterogeneous disease; diagnosis at an early stage renders it potentially curable, whereas advanced metastatic disease carries a worse prognosis. Objectives To investigate whether hepatic steatosis (HS) is associated with liver metastases in patients with newly diagnosed stage IV female breast cancer patients (either de novo metastatic breast cancer or recurrent metastatic breast cancer) using non-contrast computed tomography (CT) as a marker of HS. Design Retrospective analysis. Methods We retrospectively identified 168 patients with stage IV breast cancer with suitable imaging from a prospectively maintained oncologic database. Three radiologists manually defined hepatic regions of interest on non-contrast CT images, and attenuation data were extracted. HS was defined as a mean attenuation <48 Hounsfield units. The frequency of hepatic metastatic disease was calculated for patient with and without HS. Relationships between HS and various patient (age, body mass index, race) and tumor (hormone receptor status, HER2 status, tumor grade) characteristics were also analyzed. Results There were 4 patients with liver metastasis in the HS group (41 patients) versus 20 patients with liver metastases in the non-HS group (127 patients). The difference in frequencies of liver metastases among patients with (9.8%) versus without (15.7%) hepatic steatosis (odds ratio = 1.72 [0.53-7.39]) was not statistically significant (P = .45). Body mass index was significantly higher (P = .01) among patients with hepatic steatosis (32.2 ± 7.3 vs 28.8 ± 7.1 kg/m2). Otherwise, there were no significant differences between patients with versus without HS with respect to regarding age, race, hormone receptor status, HER2 status, or tumor grade. Conclusion The frequency of hepatic metastatic disease in patients with stage IV breast cancer is similar for steatotic and non-steatotic livers.
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Affiliation(s)
- Adeel Haq
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tyler J Fraum
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Yu Tao
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Farrokh Dehdashti
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Maverick LeBlanc
- Department of Radiology, Ochsner Medical Center, Jefferson, LA, USA
| | - Mark J Hoegger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jingqin Luo
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Katherine Weilbaecher
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Lindsay L Peterson
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
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Lee SJ, Kim YR, Lee YH, Yoon KH. US Attenuation Imaging for the Evaluation and Diagnosis of Fatty Liver Disease. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:666-675. [PMID: 37324990 PMCID: PMC10265227 DOI: 10.3348/jksr.2022.0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/30/2022] [Accepted: 10/14/2022] [Indexed: 06/17/2023]
Abstract
Purpose This study aimed to determine whether the attenuation coefficient (AC) from attenuation imaging (ATI) was correlated with visual US assessment in patients with hepatic steatosis. Moreover, it aimed to assess whether the patient's blood chemistry results and CT attenuation were correlated with AC. Materials and Methods Patients who underwent abdominal US with ATI between April 2018 and December 2018 were included in this study. Patients with chronic liver disease or cirrhosis were excluded. The correlation between AC and other parameters, such as visual US assessment, blood chemistry results, liver attenuation, and liver-to-spleen (L/S) ratio, were analyzed. AC values according to visual US assessment grades were compared using analysis of variance. Results A total of 161 patients were included in this study. The correlation coefficient between US assessment and AC was 0.814 (p < 0.001). The mean AC values for the normal, mild, moderate, and severe grades were 0.56, 0.66, 0.74, and 0.85, respectively (p < 0.001). Alanine aminotransferase levels were significantly correlated with AC (r = 0.317, p < 0.001). The correlation coefficients between liver attenuation and AC and between L/S ratio and AC were -0.702 and -0.626, respectively (p < 0.001). Conclusion Visual US assessment and AC showed a strong positive correlation with the discriminative value between the groups. Computed tomography attenuation and AC showed a strong negative correlation.
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Şendur HN, Cerit MN, Ibrahimkhanli N, Şendur AB, Özhan Oktar S. Interobserver Variability in Ultrasound-Based Liver Fat Quantification. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:833-841. [PMID: 35778902 DOI: 10.1002/jum.16048] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/28/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES To assess interobserver variability in ultrasound-based quantitative liver fat content measurements and to determine how much time these quantitative ultrasound (QUS) techniques require. METHODS One hundred patients with known or suspected of having nonalcoholic fatty liver disease were included in this prospective study. Two observers who were blinded to each other measurements performed tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) techniques independently. Both observers assessed hepatic steatosis visually and obtained 5 measurements for each QUS technique and the median values of the measurements were recorded. Spearman's correlation test was used to assess the correlation between QUS measurements and visual hepatic stetaosis grades. Intraclass correlation coefficient (ICC) test was used to assess interobserver variability in QUS measurements. RESULTS The median values of TAI measurements for the observers 1 and 2 were 0.75 and 0.74 dB/cm/MHz, respectively. The median values of TSI measurements for the observers 1 and 2 were 93.53 and 92.58, respectively. The interobserver agreement in TAI (ICC: 0.970) and TSI (ICC: 0.938) measurements were excellent. The mean of the required time period for TAI technique were 55.1 ± 7.8 and 59.9 ± 6.6 seconds for the observers 1 and 2, respectively. The mean of the required time period for TSI technique were 49.1 ± 5.8 and 54.1 ± 5.4 seconds for the observers 1 and 2, respectively. CONCLUSION The current study revealed that both TAI and TSI techniques are highly reproducible and can be implemented into daily practice with little additional time requirement.
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Affiliation(s)
- Halit Nahit Şendur
- Department of Radiology, Gazi University Faculty of Medicine, Mevlana Bulvarı No:29 06560 Yenimahalle, Ankara, Turkey
| | - Mahi Nur Cerit
- Department of Radiology, Gazi University Faculty of Medicine, Mevlana Bulvarı No:29 06560 Yenimahalle, Ankara, Turkey
| | - Nemat Ibrahimkhanli
- Department of Radiology, Gazi University Faculty of Medicine, Mevlana Bulvarı No:29 06560 Yenimahalle, Ankara, Turkey
| | | | - Suna Özhan Oktar
- Department of Radiology, Gazi University Faculty of Medicine, Mevlana Bulvarı No:29 06560 Yenimahalle, Ankara, Turkey
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Kim HN, Jeon HJ, Choi HG, Kwon IS, Rou WS, Lee JE, Lee TH, Kim SH, Lee BS, Shin KS, Lee HJ, Eun HS. CT-based Hounsfield unit values reflect the degree of steatohepatitis in patients with low-grade fatty liver disease. BMC Gastroenterol 2023; 23:77. [PMID: 36932382 PMCID: PMC10022198 DOI: 10.1186/s12876-023-02717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 03/09/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND/AIMS Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide. Ultrasound, the most used tool for diagnosing NAFLD, is operator-dependent and shows suboptimal performance in patients with mild steatosis. However, few studies have been conducted on whether alternative noninvasive methods are useful for diagnosing mild hepatic steatosis. Also, little is known about whether noninvasive tests are useful for grading the severity of hepatic steatosis or the degree of intrahepatic inflammation. Therefore, we aimed to evaluate whether the HSI, the FLI and HU values in CT could be used to discriminate mild hepatic steatosis and to evaluate the severity of hepatic steatosis or the degree of intrahepatic inflammation in patients with low-grade fatty liver disease using liver biopsy as a reference standard. METHODS Demographic, laboratory, CT imaging, and histological data of patients who underwent liver resection or biopsy were analyzed. The performance of the HSI, HU values and the FLI for diagnosing mild hepatic steatosis was evaluated by calculating the area under the receiver operating characteristic curve. Whether the degree of hepatic steatosis and intrahepatic inflammation could be predicted using the HSI, HU values or the FLI was also analyzed. Moreover, we validate the results using magnetic resonance imaging proton density fat fraction as an another reference standard. RESULTS The AUROC for diagnosing mild hepatic steatosis was 0.810 (p < 0.001) for the HSI, 0.732 (p < 0.001) for liver HU value, 0.802 (p < 0.001) for the difference between liver and spleen HU value (L-S HU value) and 0.813 (p < 0.001) for the FLI. Liver HU and L-S HU values were negatively correlated with the percentage of hepatic steatosis and NAFLD activity score (NAS) and significantly different between steatosis grades and between NAS grades. The L-S HU value was demonstrated the good performance for grading the severity of hepatic steatosis and the degree of intrahepatic inflammation. CONCLUSIONS The HU values on CT are feasible for stratifying hepatic fat content and evaluating the degree of intrahepatic inflammation, and the HSI and the FLI demonstrated good performance with high sensitivity and specificity in diagnosing mild hepatic steatosis.
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Affiliation(s)
- Ha Neul Kim
- Department of Medical Sciences, Chungnam National University, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
- Brain Korea 21 FOUR Project for Medical Science, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hong Jae Jeon
- Department of Internal Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-Ro, Sejong, 30099, Republic of Korea
- Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Hei Gwon Choi
- Brain Korea 21 FOUR Project for Medical Science, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Research Institute of Medical Sciences, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - In Sun Kwon
- Statistical Consultation of Clinical Trials Center, Chungnam National University Hospital, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Woo Sun Rou
- Department of Internal Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-Ro, Sejong, 30099, Republic of Korea
- Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Jeong Eun Lee
- Department of Radiology, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
- Department of Radiology, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Tae Hee Lee
- Department of Biomedical Laboratory Science, Daegu Health College, Chang-Ui Building, 15 Yeongsong-Ro, Buk-Gu, Daegu, 41453, Republic of Korea
| | - Seok Hyun Kim
- Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
- Department of Internal Medicine, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Byung Seok Lee
- Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
- Department of Internal Medicine, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Kyung Sook Shin
- Department of Radiology, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
- Department of Radiology, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea
| | - Hyun Jung Lee
- Department of Pathology, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea.
- Department of Pathology, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea.
| | - Hyuk Soo Eun
- Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea.
- Department of Internal Medicine, Chungnam National University Hospital, 282 Munwha-Ro, Jung-Gu, Daejeon, 35015, Republic of Korea.
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Correlation between CT Abdominal Anthropometric Measurements and Liver Density in Individuals with Non-Alcoholic Fatty Liver Disease. Medicina (B Aires) 2023; 59:medicina59030500. [PMID: 36984501 PMCID: PMC10053809 DOI: 10.3390/medicina59030500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/21/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
Background: With a growing frequency, nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. NAFLD has a strong correlation with other metabolic disorders, such as obesity, particularly abdominal obesity, even though the underlying causes or risk factors are not entirely understood. This study aims to investigate correlations between abdominal anthropometric measurements and the presence and intensity of liver steatosis as assessed by unenhanced computed tomography (CT). Methods: One hundred and nineteen patients (male/female, 66/53; mean age 54.54 +/− 12.90 years) underwent abdominal non–contrast-enhanced CT. CT images were examined to determine the attenuation of liver parenchyma, subcutaneous fat depth, and waist circumference (WC). Results: Among all patients, WC (r = −0.78, p < 0.0001), infraumbilical subcutaneous fat thicknesses (r = −0.51, p < 0.0001), right paraumbilical subcutaneous fat thicknesses (r = −0.62, p < 0.0001), and left paraumbilical subcutaneous fat thicknesses (r = −0.53, p < 0.0001) had a high inverse correlation with the liver attenuation values. The presence of T2D (OR: 2.40, p = 0.04), WC (OR: 11.45, p < 0.001), right paraumbilical (OR: 10.09, p < 0.001), left paraumbilical (OR: 2.81, p = 0.01), and infraumbilical (OR: 3.06, p = 0.007) were strongly independent predictors of NAFLD risk. Moreover, regarding the laboratory parameters, only the higher value of GGT (OR: 2.84, p = 0.009) is a predictor of NAFLD risk. Conclusions: Our data show that higher baseline values of all abdominal anthropometric measurements are correlated with liver attenuation and act as predictors of NAFLD risk.
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Ultrasound-based hepatic fat quantification: current status and future directions. Clin Radiol 2023; 78:187-200. [PMID: 36411088 DOI: 10.1016/j.crad.2022.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 09/22/2022] [Accepted: 10/06/2022] [Indexed: 11/19/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a spectrum of disease from fatty accumulation (steatosis), necro-inflammation though to fibrosis. It is of increasing global prevalence as a hepatic manifestation of the metabolic syndrome. Although accurate histopathology and magnetic resonance imaging techniques for hepatic fat quantification exist, these are limited by invasiveness and availability, respectively. Ultrasonography is potentially ideal for assessing and monitoring hepatic steatosis given the examination is rapid and readily available. Traditional ultrasound methods include qualitative B-mode for imaging markers, such as increased hepatic parenchymal echogenicity compared to adjacent renal cortex are commonplace; however, there is acknowledged significant interobserver variability and they are suboptimal for detecting mild steatosis. Recently quantitative ultrasound metrics have been investigated as biomarkers for hepatic steatosis. These methods rely on changes in backscatter, attenuation, and speed of sound differences encountered in a steatotic liver. Prospective studies using quantitative ultrasound parameters show good diagnostic performance even at low steatosis grades and in NAFLD. This review aims to define the clinical need for ultrasound-based assessments of liver steatosis, to describe briefly the physics that underpins the various techniques available, and to assess the evidence base for the effectiveness of the techniques that are available commercially from various ultrasound vendors.
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Tahmasebi A, Wang S, Wessner CE, Vu T, Liu JB, Forsberg F, Civan J, Guglielmo FF, Eisenbrey JR. Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023. [PMID: 36807314 DOI: 10.1002/jum.16194] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD. METHODS One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated. RESULTS A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%. CONCLUSIONS An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.
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Affiliation(s)
- Aylin Tahmasebi
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Shuo Wang
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Corinne E Wessner
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Trang Vu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jesse Civan
- Department of Medicine, Division of Gastroenterology and Hepatology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flavius F Guglielmo
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - John R Eisenbrey
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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Barboza TK, Susta L, zur Linden A, Gardhouse S, Beaufrère H. Association of plasma metabolites and diagnostic imaging findings with hepatic lipidosis in bearded dragons (Pogona vitticeps) and effects of gemfibrozil therapy. PLoS One 2023; 18:e0274060. [PMID: 36735707 PMCID: PMC9897564 DOI: 10.1371/journal.pone.0274060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/21/2022] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES To evaluate the association between plasma metabolites, biochemical analytes, diagnostic imaging findings, and the histologic diagnosis of hepatic lipidosis in bearded dragons. To assess the effects of gemfibrozil therapy on hepatic lipid accumulation and associated diagnostic tests. ANIMALS Fourteen bearded dragons (Pogona vitticeps) with varying severity of hepatic lipid accumulation (with and without hepatic lipidosis) were included. PROCEDURES Animals underwent coelomic ultrasound, computed tomography (CT) scans, and coelioscopic hepatic biopsies. Clinical pathology tests included lipidologic tests, hepatic biomarkers, and mass spectrometry-based metabolomics. Animals were medicated with gemfibrozil 6mg/kg orally once a day for 2 months in a randomized blinded clinical trial prior to repeating previous diagnostic testing. RESULTS Hounsfield units on CT were negatively associated with increased hepatic vacuolation, while ultrasound and gross evaluation of the liver were not reliable. Beta-hydroxybutyric-acid (BHBA) concentrations were significantly associated with hepatic lipidosis. Metabolomics and lipidomics data found BHBA and succinic acid to be potential biomarkers for diagnosing hepatic lipidosis in bearded dragons. Succinic acid concentrations were significantly lower in the gemfibrozil treatment group. There was a tendency for improvement in the biomarkers and reduced hepatic fat in bearded dragons with hepatic lipidosis when treated with gemfibrozil, though the improvement was not statistically significant. CONCLUSIONS These findings provide information on the antemortem assessment of hepatic lipidosis in bearded dragons and paves the way for further research in diagnosis and treatment of this disease.
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Affiliation(s)
- Trinita K. Barboza
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Leonardo Susta
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Alex zur Linden
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Sara Gardhouse
- Health Sciences Center, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Hugues Beaufrère
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
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Three segments sampling strategy for the assessment of liver steatosis using magnetic resonance imaging proton density fat fraction. Eur J Radiol 2023; 159:110653. [PMID: 36563563 DOI: 10.1016/j.ejrad.2022.110653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/28/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE This research aims to determine the best liver segments representing the whole-liver fat fraction (FF) in magnetic resonance imaging (MRI)-based measurement of the proton density fat fraction (PDFF). METHOD This retrospective study included 989 adult subjects who underwent MRI-PDFF from March 2018 to January 2021. Three regions of interest (ROI) were measured and averaged for each hepatic segment and the volume-weighted hepatic FF was calculated. Intrahepatic fat variability was assessed by standard deviation between all ROIs. Univariate and multivariate linear regression analyses were done for the factors associated with intrahepatic fat variability among clinical characteristics, blood parameters and the volume-weighted FF. The arithmetic means of specific hepatic segments that were the closest to the volume-weighted FF were identified in all subjects and those with moderate or severe fatty liver. RESULTS The volume-weighted FF was 8.18% and variability was 1.33%. Volume-weighted FF was the only associated factor with intrahepatic variability. The arithmetic mean of segments V, VI, and IV was closest to the volume-weighted FF in all subjects and in subjects with moderate or severe fatty liver. CONCLUSIONS There was considerable heterogeneity in hepatic steatosis between each segment of the liver, and the variability was significantly affected by the volume-weighted FF. The mean hepatic FF from segments V, VI, and IV could be used to estimate the volume-weighted FF of the whole liver, not only in the general population but also in patients with moderate or severe fatty liver.
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