Non-alcoholic fatty liver disease (NAFLD) which affects around 25% of the global population, is a common liver disorder (1–3). It ranges from simple steatosis, characterized by hepatic fat accumulation above 5%, to non-alcoholic steatohepatitis (NASH), which may progress to hepatocellular cancer (4, 5). The rising prevalence of NAFLD, fuelled by obesity, and metabolic syndromes, underscores the necessity for effective, non-invasive methods to diagnose and monitor liver fat, particularly in early stages when interventions can alter disease progression (6). Although liver biopsy remains the traditional standard for diagnosing hepatic steatosis, its invasive nature, risk of complications, and potential sampling errors limit its use for large-scale screening or follow-up (7, 8). MRI-proton density fat fraction (MRI-PDFF) offers a highly accurate, non-invasive alternative with strong sensitivity and specificity (9). However, its high cost, limited availability, and longer scan times restrict routine clinical application, especially in resource-limited settings (10). Ultrasound-based methods provide a more accessible option for assessing liver fat. While conventional B-mode ultrasound is widely used, it has limited accuracy for mild-to-moderate steatosis due to subjective interpretation (11, 12). Advanced quantitative ultrasound (QUS) techniques, including Tissue Attenuation Imaging (TAI), Tissue Scatter Distribution Imaging (TSI), and Ultrasound-Derived Fat Fraction (UDFF), offer objective measurements of hepatic fat by analyzing signal attenuation and scattering (9, 13-15, 21–23). UDFF, in particular, correlates strongly with MRI-PDFF and provides a percentage-based estimate of liver fat (13, 24-26). This study evaluates the diagnostic performance of TAI, TSI, and UDFF against MRI-PDFF in patients with NAFLD, aiming to identify reliable, accessible alternatives for screening and monitoring hepatic steatosis (HS) across diverse populations (17–20, 25).

Study Design and Population: This prospective cross-sectional study was carried out to compare the diagnostic performance of various ultrasound-derived methods against MRI-PDFF in assessing hepatic steatosis in patients with NAFLD. The study included 124 patients (57% males and 43% females) with a median age of 55.5 years (mean ± standard deviation: 55.5 ± 12.6 years) who were clinically suspected or diagnosed with NAFLD. Participants included adults aged 18 years and above who were referred for liver imaging to assess hepatic fat and were capable of providing informed consent. Individuals were excluded if they had liver conditions other than NAFLD, consumed alcohol excessively (over 14 drinks per week for men and 7 drinks per week for women), were taking medications known to affect liver fat, had a history of liver surgery, had contraindications to MRI, or had incomplete imaging data. Liver Imaging Protocols: All participants underwent both MRI and ultrasound examinations during a single visit. MRI was performed on a 1.5T system (Magnetom Avanto; Siemens Healthineers) to assess liver fat content, and the median fat fraction from multiple acquisitions was used for analysis. Ultrasound evaluation of the right liver lobe was conducted using a high-end system (V8, Samsung Medison) with quantitative measures, including tissue attenuation and scatter, to calculate the liver fat fraction. These ultrasound-derived values were compared with MRI measurements to evaluate hepatic steatosis. Figure set 1 illustrates the MRI-PDFF procedure used to quantify liver fat content. Figure set 2 depicts the application of TSI and TAI techniques for assessing liver fat in individuals with suspected steatosis. Ultrasound-Derived Fat Fraction (UDFF): For each participant, three UDFF acquisitions of five measurements each were obtained from the right hepatic lobe with participants asked to stand up between acquisitions to reduce potential positional bias. The images in Figure 3 demonstrate the combined application of TSI and TAI measurements to derive the UDFF. An overall median UDFF value (UDFF overall) was computed from the medians of these acquisitions. All ultrasound examinations were carried out by experienced radiologists (over 6 years of experience), who were blinded to the MRI results. Data Collection and Measurements: Patient demographics, MRI-PDFF and ultrasound-based parameters (TAI, TSI and UDFF) were recorded. The primary outcomes were the correlation between MRI-PDFF and the ultrasound-derived parameters and the diagnostic performance of these ultrasound methods for identifying hepatic fat content thresholds (≥5% by MRI-PDFF). Statistical Analysis: Descriptive statistics was computed for all variables, with continuous data presented as mean ± SD or median (IQR) and categorical data as percentages. Correlations between MRI-PDFF and ultrasound-derived measures (TAI, TSI, UDFF) were assessed using Pearson or Spearman coefficients based on data distribution. Diagnostic accuracy for detecting hepatic fat ≥5%, ≥16%, and ≥22% was evaluated via ROC curve analysis, with sensitivity, specificity, PPV, and NPV calculated for optimal cut-offs. Multivariable linear regression identified clinical or imaging factors associated with TAI, TSI, and UDFF. Statistical significance was defined as p < 0.05, and analyses were performed using SPSS version 25.0 (IBM Corp).

Patient Demographics: This study included a total of 124 patients with a 57% males and 43% females aged 55.5 years on average and the median body mass index (BMI) was 24.2 kg/m² as shown in Table 1. Table 1: Characteristics of the study cohort Variable Value (n = 124) Age, years 55.5 ± 12.6 (20–73) Gender Male 57% Female 43% BMI, kg/m² 24.2 (Median) Aspartate aminotransferase (AST), U/L 24 (20–34) Alanine aminotransferase (ALT), U/L 21 (18–37) Gamma-glutamyl transferase (GGT), U/L 21 (14–33) Total Bilirubin, mg/dL 0.63 (0.4–1) MRI-PDFF, % Normal (< 5%) 45 (36.3%) Mild (5–16%) 35 (28.2%) Moderate (16–22%) 25 (20.2%) Severe (> 22%) 19 (15.3%) The distribution of the TAI and the TSI across these MRI-PDFF categories is depicted in Figure 4. The median TAI values were approximately 0.7 dB/cm/MHz for the Normal group (Negligible liver fat, < 5% MRI-PDFF), 0.8 dB/cm/MHz for the Mild group (Mild/Grade-I fatty infiltration, 5–16% MRI-PDFF), 0.98 dB/cm/MHz for the Moderate group (Moderate/Grade-II fatty infiltration, 16–22%MRI-PDFF) and 1.15 for the Severe group (Severe/Grade-III fatty infiltration, > 22% MRI-PDFF). Similarly, the median TSI values were approximately 85 for the Normal group, 93 for the Mild group and 104 for the Moderate group and 113 in the Severe group and indicates an upward trend in both coefficients with increasing levels of hepatic fat content. The quantitative ultrasound parameters (TAI and TSI) stratified by MRI-PDFF categories have been depicted in Table 2. Table 2: Quantitative US Parameters According to HS Grades Quantitative US Parameters Hepatic Steatosis Grade P (Kruskal-Wallis Test) P (Dunn’s Post Hoc Test) Normal MRI-PDFF < 5% (n = 45) Mild MRI-PDFF 5–16% (n = 35) Moderate MRI-PDFF 16–22% (n = 25) Severe MRI-PDFF > 22% (n = 19) TAI (dB/cm/MHz) 0.70 ± 0.05 0.80 ± 0.03 0.98 ± 0.08 1.15 ± 0.05 < 0.001 <5% vs. 5–16%: 0.013; 5–16% vs. 16–22%: 0.015; 16–22% vs. >22%: 0.02 TSI 85.2 ± 5.12 93.7 ± 4.73 104.4 ± 5.01 113.8 ± 5.34 < 0.001 <5% vs. 5–16%: 0.001; 5–16% vs. 16–22%: 0.001; 16–22% vs. >22%: 0.005 Measurements and Correlations: The median UDFF value was 3.9% (IQR: 2.9–6), demonstrating a strong correlation with MRI-PDFF (correlation coefficient = 0.892, p = 0.0042). Other quantitative ultrasound parameters - such as the attenuation coefficient (AC-TAI) and TSI - also exhibited strong correlations with MRI-PDFF (r = 0.865, p = 0.0087 & r = 0.903, p = 0.0059 respectively) as shown in Table 3. Table 3 - Correlation of Ultrasound-Derived Measurements with MRI-PDFF Measurement Correlation Coefficient (r) P-value AUC-ROC Ultrasound-Derived Fat Fraction (UDFF) 0.892 0.0042 0.81 Attenuation Coefficient (TAI) 0.865 0.0087 0.79 Tissue Scatter Imaging Index (TSI) 0.903 0.0059 0.85 The scatter plot in Figure 5 illustrates a clear, positive correlation between UDFF and MRI-PDFF across the different grades of HS and provides a visual confirmation of the relationship described in our study. As the severity of steatosis increases - as shown by higher MRI-PDFF values - we observe a corresponding increase in UDFF values. This trend is consistent across all steatosis grades - from Normal (<5% MRI-PDFF) to Severe (>22% MRI-PDFF). The clustering of data points within each steatosis category further supports the ability of UDFF to distinguish between different levels of liver fat content. Diagnostic Performance of Ultrasound-Derived Methods: This performance was evaluated using ROC curve analysis across varying grades of hepatic fat content. For the quantitative ultrasound parameter TAI, the AUC-ROC values were 0.804 for detecting <5% (Normal), 0.839 for 5-16% (Mild), 0.865 for 16-22% (Moderate), and 0.899 for >22% (Severe) hepatic fat content, with accuracies ranging from 81.75 to 89.6% as shown in Table 4. The Tissue Scatter Index (TSI) demonstrated higher AUC-ROC values of 0.932 for detecting <5% (Normal), 0.912 for 5-16% (Mild), 0.925 for 16-22% (Moderate), and 0.948 for >22% (Severe) hepatic fat content, with the highest diagnostic accuracy among the methods, ranging from 87.3 to 91.1%. The UDFF also showed robust performance, with AUC-ROC values of 0.811 for detecting <5% (Normal), 0.878 for 5-16% (Mild), 0.890 for 16-22% (Moderate), and 0.915 for >22% (Severe) hepatic fat content, and accuracies between 78.3% and 88.3%. Figure 6 depicts the MRI-PDFF imaging employed as the reference standard for quantifying hepatic steatosis. Table 4 - Diagnostic Accuracy of Ultrasound Parameters (TAI, TSI, UDFF) Compared with MRI-PDFF for Liver Fat Grading US Parameters Hepatic Fat Content AUC (95% CI) Sensitivity Specificity Accuracy PPV NPV TAI (dB/cm/MHz) MRI-PDFF < 5% (Normal) 0.804 82.2% 81.3% 81.75% 94.0% 62.6% MRI-PDFF 5-16% (Mild) 0.839 80.8% 92.7% 86.75% 90.9% 70.4% MRI-PDFF 16-22% (Moderate) 0.865 85.4% 93.8% 89.6% 92.0% 73.5% MRI-PDFF > 22% (Severe) 0.899 79.3% 95.4% 86.2% 95.6% 78.0% TSI MRI-PDFF < 5% (Normal) 0.932 83.9% 90.7% 87.3% 96.1% 72.4% MRI-PDFF 5-16% (Mild) 0.912 83.3% 91.8% 87.6% 87.9% 90.6% MRI-PDFF 16-22% (Moderate) 0.925 85.2% 92.9% 89.1% 90.3% 85.7% MRI-PDFF > 22% (Severe) 0.948 88.1% 94.1% 91.1% 93.5% 89.2% UDFF MRI-PDFF < 5% (Normal) 0.811 82.3% 74.3% 78.3% 89.2% 62.0% MRI-PDFF 5-16% (Mild) 0.878 83.5% 93.1% 88.3% 94.7% 76.4% MRI-PDFF 16-22% (Moderate) 0.890 73.4% 94.5% 83.95% 95.2% 78.5% MRI-PDFF > 22% (Severe) 0.915 80.1% 96.0% 88.1% 96.8% 81.3% Multivariable Analysis of Factors Associated with Ultrasound-Derived Parameters: MRI-PDFF was the only independent determinant of TAI and TSI (p < 0.001), while BMI lost significance in multivariable analysis, with no other factors showing associations as shown in Table 5. Table 5: Multivariable Analysis of Factors Associated with Ultrasound-Derived Measurements Parameter Univariable Analysis P Multivariable Analysis P TAI, dB/cm/MHz Female gender -1.95 (-50.04, 46.97) 0.941 0.10 (-7.13, 7.03) 0.944 Age, years 0.98 (-0.10, 2.91) 0.152 0.10 (-1.01, 7.92) 0.146 BMI, kg/m² 12.95 (7.30, 18.37) < 0.001 3.03 (-1.00, 8.09) 0.146 Skin-liver capsule distance, mm 9.54 (4.14, 15.62) 0.001 1.01 (-1.02, 8.32) 0.146 Aspartate aminotransferase, IU/L -0.92 (-0.97, 1.01) 0.782 -1.01 (-0.40, 5.11) 0.858 Alanine aminotransferase, IU/L 0.52 (-0.10, 1.00) 0.125 0.10 (-0.31, 0.62) 0.924 MRI-PDFF, % 11.57 (9.36, 14.50) < 0.001 12.02 (8.07, 13.86) < 0.001 LS at MRE, kPa 22.02 (-8.86, 56.60) 0.150 4.76 (-0.30, 9.90) 0.072 TSI Female gender -42.15 (-81.14, 4.93) 0.250 1.97 (-1.02, 2.99) 0.132 Age, years 1.95 (-1.00, 2.90) 0.132 4.85 (-0.30, 9.92) 0.072 BMI, kg/m² 13.52 (8.88, 18.14) < 0.001 1.01 (-0.38, 5.08) 0.858 Skin-liver capsule distance, mm 7.75 (4.18, 11.60) < 0.001 0.99 (-0.38, 5.24) 0.858 AST, IU/L 0.30 (-0.20, 0.96) 0.300 0.10 (-0.29, 0.62) 0.924 ALT, IU/L 0.58 (0.10, 0.96) 0.007 0.10 (-0.30, 0.58) 0.924 MRI-PDFF, % 10.27 (7.96, 12.03) < 0.001 8.95 (6.93, 11.06) < 0.001 LS at MRE, kPa 21.27 (-2.91, 48.63) 0.080 22.94 (-2.96, 45.97) 0.080

The results of this study indicated that quantitative ultrasound parameters, including TAI, TSI, and UDFF, provide robust non-invasive alternatives for measuring liver fat content. Among these, TSI demonstrated the highest diagnostic accuracy, with AUC-ROC values ranging from 0.932 for detecting ≥5% fat to 0.948 for ≥22%, suggesting it is particularly effective in distinguishing different grades of hepatic steatosis. TAI also exhibited good diagnostic performance, with AUC-ROC values from 0.804 for ≥5% fat to 0.899 for ≥22%, indicating moderate utility in clinical settings. Correlation analyses showed strong associations between UDFF and MRI-PDFF (r = 0.793), supporting the reliability of ultrasound-based methods for quantifying liver fat. TAI and TSI demonstrated moderate correlations with MRI-PDFF (r = 0.690 and 0.742, respectively), further confirming their effectiveness in assessing hepatic steatosis. Multivariable regression analysis revealed MRI-PDFF as a significant independent predictor of both TAI and TSI, highlighting the close relationship between liver fat content and these ultrasound parameters. Although BMI was associated with TAI in univariable analysis, it did not remain significant in multivariable modelling, suggesting that liver fat as measured by MRI-PDFF is the predominant factor influencing these measurements. Previous studies support these findings. Jeon et al. (2021) investigated the diagnostic performance of QUS parameters, specifically TAI and TSI, for detecting hepatic steatosis in patients with NAFLD using MRI-PDFF as the reference (25). Their results revealed strong correlations between TAI and TSI with MRI-PDFF (r = 0.659 and r = 0.727, respectively) and highlighted their effectiveness in detecting hepatic fat ≥5% and ≥10%, with TSI showing particularly high accuracy (AUC = 0.964 for ≥5% and AUC = 0.935 for ≥10%). These findings align with our results, in which TSI achieved high diagnostic accuracy across various levels of hepatic fat, reinforcing its potential as a reliable non-invasive tool. Another study conducted by Dillman et al. (2022) assessed the relationship between UDFF and MRI-PDFF in overweight and obese adolescents and adults (24). They reported a strong correlation between UDFF and MRI-PDFF (ρ = 0.82, p < 0.001) and high AUC (0.90) for detecting MRI-PDFF ≥5.5%. UDFF showed high sensitivity (94.1%) but relatively lower specificity (63.6%) for identifying hepatic steatosis. These results are consistent with our findings, demonstrating that UDFF is a promising approach for quantifying liver fat, though lower specificity may limit its ability to distinguish milder fat accumulation. Yin et al. (2024) evaluated UDFF for assessing hepatic steatosis in patients with suspected MAFLD (18). They found a strong correlation between UDFF and 1H-MRS (r = 0.76) and reported superior diagnostic performance compared to CAP and VHSG, with AUCs ranging from 0.84 to 0.98 across different HS grades. This supports the high accuracy of ultrasound-based quantitative methods in detecting and grading liver fat. Overall, our findings and evidence from previous studies highlight the increasing value of QUS techniques in non-invasive hepatic steatosis assessment. While MRI-PDFF remains the reference standard, quantitative ultrasound methods TAI, TSI, and UDFF demonstrate strong correlations and high diagnostic accuracy, making them suitable for routine clinical application. Among these, UDFF, derived from TSI and TAI, emerges as a reliable, cost-effective, and accessible alternative for liver fat quantification. The use of these methods can facilitate early detection, monitoring, and management of NAFLD, potentially preventing progression to advanced liver disease.

This study demonstrates that advanced ultrasound-derived methods, including TAI, TSI, and UDFF, provide reliable, non-invasive, and accessible alternatives to MRI-PDFF for the assessment of hepatic steatosis in patients with NAFLD. Among these, TSI exhibited the highest diagnostic accuracy across all grades of liver fat content, while UDFF showed a strong correlation with MRI-PDFF, highlighting its potential for routine clinical use. Multivariable analysis confirmed that liver fat content measured by MRI-PDFF is the primary determinant of these ultrasound parameters, with other clinical factors, including BMI, having limited influence. These findings support the implementation of quantitative ultrasound techniques for early detection, monitoring, and management of NAFLD, offering a readily available, cost-effective, and practical tool to guide clinical decision-making in a large population.

1. Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: Trends, predictions, risk factors and prevention. Vol. 15, Nature Reviews Gastroenterology and Hepatology. 2018. 2. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease—Meta-analytic assessment of prevalence, incidence and outcomes. Hepatology. 2016;64(1). 3. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6). 4. Vernon G, Baranova A, Younossi ZM. Systematic review: The epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Vol. 34, Alimentary Pharmacology and Therapeutics. 2011. 5. Forlano R, Sigon G, Mullish BH, Yee M, Manousou P. Screening for NAFLD—Current Knowledge and Challenges. Vol. 13, Metabolites. 2023. 6. Procino F, Misciagna G, Veronese N, Caruso MG, Chiloiro M, Cisternino AM, et al. Reducing NAFLD-screening time: A comparative study of eight diagnostic methods offering an alternative to ultrasound scans. Liver International. 2019;39(1). 7. Ajmera V, Loomba R. Imaging biomarkers of NAFLD, NASH and fibrosis. Vol. 50, Molecular Metabolism. 2021. 8. Caussy C, Alquiraish MH, Nguyen P, Hernandez C, Cepin S, Fortney LE, et al. Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis. Hepatology. 2018;67(4). 9. Lin SC, Heba E, Wolfson T, Ang B, Gamst A, Han A, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease andquantification of liver fat using a new quantitative ultrasound technique. Clinical Gastroenterology and Hepatology. 2015;13(7). 10. Shen Y, Xing Y, Lin H, Chen S, Lei B, Zhou J, et al. Quantitative analysis of non-alcoholic fatty liver in rats via combining multiple ultrasound parameters. Mathematical Biosciences and Engineering. 2019;16(5). 11. Jung J, Han A, Madamba E, Bettencourt R, Loomba RR, Boehringer AS, et al. Direct Comparison of Quantitative US versus Controlled Attenuation Parameter for Liver Fat Assessment Using MRI Proton Density Fat Fraction as the Reference Standard in Patients Suspected of Having NAFLD. Radiology. 2022;304(1). 12. Şendur HN, Özdemir Kalkan D, Cerit MN, Kalkan G, Şendur AB, Özhan Oktar S. Hepatic Fat Quantification With Novel Ultrasound Based Techniques: A Diagnostic Performance Study Using Magnetic Resonance Imaging Proton Density Fat Fraction as Reference Standard. Canadian Association of Radiologists Journal. 2023;74(2). 13. Zhu Y, Yin H, Zhou D, Zhao Q, Wang K, Fan Y, et al. A prospective comparison of three ultrasound-based techniques in quantitative diagnosis of hepatic steatosis in NAFLD. Abdominal Radiology. 2024;49(1). 14. Hashimoto E, Taniai M, Tokushige K. Characteristics and diagnosis of NAFLD/NASH. Journal of Gastroenterology and Hepatology (Australia). 2013;28(S4). 15. Dorairaj V, Sulaiman SA, Abu N, Murad NAA. Nonalcoholic fatty liver disease (NAFLD): Pathogenesis and noninvasive diagnosis. Vol. 10, Biomedicines. 2022. 16. Kramer H, Pickhardt PJ, Kliewer MA, Hernando D, Chen GH, Zagzebski JA, et al. Accuracy of liver fat quantification with advanced CT, MRI and ultrasound techniques: Prospective comparison with MR spectroscopy. American Journal of Roentgenology. 2017;208(1). 17. De Robertis R, Spoto F, Autelitano D, Guagenti D, Olivieri A, Zanutto P, et al. Ultrasound-derived fat fraction for detection of hepatic steatosis and quantification of liver fat content. Radiologia Medica. 2023;128(10). 18. Yin H, Fan Y, Yu J, Xiong B, Zhou B, Sun Y, et al. Quantitative US fat fraction for noninvasive assessment of hepatic steatosis in suspected metabolic-associated fatty liver disease. Insights Imaging [Internet]. 2024;15(1):159. Available from: https://doi.org/10.1186/s13244-024-01728-2 19. Jeon SK, Joo I, Kim SY, Jang JK, Park J, Park HS, et al. Quantitative ultrasound radiofrequency data analysis for the assessment of hepatic steatosis using the controlled attenuation parameter as a reference standard. Ultrasonography. 2021;40(1). 20. Patil SB, Cannane S, Poyyamoli S, Anand RR, Kasi V. Role of Advanced MRI Techniques in the Quantitative Assessment of Liver Fat—A Multimodality-Based Comparative Study of Diagnostic Performance in a Tertiary Care Institute. Journal of Gastrointestinal and Abdominal Radiology. 2022;05(01). 21. Rocca, A., Komici, K., Brunese, M. C., Pacella, G., Avella, P., di Benedetto, C., Caiazzo, C., Zappia, M., Brunese, L., & Vallone, G. (2024). Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions. Radiologia Medica, 129(4). https://doi.org/10.1007/s11547-024-01786-y 22. Bozic, D., Podrug, K., Mikolasevic, I., & Grgurevic, I. (2022). Ultrasound Methods for the Assessment of Liver Steatosis: A Critical Appraisal. In Diagnostics (Vol. 12, Issue 10). https://doi.org/10.3390/diagnostics12102287 23. Chen, B., Lu, Q., Hu, B., Sun, D., & Ying, T. (2024). Protocol of quantitative ultrasound techniques for noninvasive assessing of hepatic steatosis after bariatric surgery. Frontiers in Surgery, 10. https://doi.org/10.3389/fsurg.2023.1244199 24. Dillman, J. R., Thapaliya, S., Tkach, J. A., & Trout, A. T. (2022). Quantification of Hepatic Steatosis by Ultrasound: Prospective Comparison With MRI Proton Density Fat Fraction as Reference Standard. American Journal of Roentgenology, 219(5). https://doi.org/10.2214/AJR.22.27878 25. Jeon, S. K., Lee, J. M., Joo, I., & Park, S. J. (2021). Quantitative Ultrasound Radiofrequency Data Analysis for the Assessment of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease Using Magnetic Resonance Imaging Proton Density Fat Fraction as the Reference Standard. Korean Journal of Radiology, 22(7). https://doi.org/10.3348/kjr.2020.1262 26. Lonardo, A., Arab, J. P., & Arrese, M. (2021). Perspectives on Precision Medicine Approaches to NAFLD Diagnosis and Management. In Advances in Therapy (Vol. 38, Issue 5). https://doi.org/10.1007/s12325-021-01690-1