Georgian Scientists/ . 7 N 4, 2025 502 Georgian Scientists Vol. 7 Issue 4, 2025 https://doi.org/10.52340/gs.2025.07.04.34 A Narrative Review of the Diagnostic Performance of Non-Invasive Scores for Predicting Fibrosis in MAFLD Tatia Khachidze1,2, ID, Gela Sulaberidze1, ID, Gocha Barbakadze1,3, ID 1Tbilisi State Medical University; 2Rayman Clinic; 3Enmedic Clinic ABSTRACT Background & aim: The burden of Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) is increasing, with an estimated prevalence of 32.4% globally. Although most patients present with simple steatosis, some progress to advanced fibrosis, cirrhosis, and hepatocellular carcinoma. Non-invasive scores offer a promising alternative to liver biopsy for fibrosis staging. This narrative review aims to compare the diagnostic performance of non-invasive scores for in detecting fibrosis in patients with MAFLD, with a focus on identifying the most accurate and reliable score. Methods: This narrative review included studies with histologically confirmed fibrosis staging. The primary outcome was the overall diagnostic accuracy of each score in predicting significant fibrosis (F2-F4) and advanced fibrosis (F3-F4). Secondary outcomes included sensitivity, specificity, and positive and negative predictive values. Results & Conclusion: Our analysis included a total of 11 studies with 5761 patients. The overall diagnostic performance for predicting advanced fibrosis was highest for Magnetic Resonance Elastography (MRE), followed by Transient Elastography (VCTE). In predicting significant fibrosis, Hepamet Fibrosis Score (HFS) demonstrated the highest performance. Significant variation was observed across studies, which was partially explained by differences in the prevalence of advanced fibrosis and the specific thresholds utilized for each score. INTRODUCTION Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) has become the most common chronic liver disease worldwide, with an estimated 32.4% globally affected (1). MAFLD is linked to obesity, type 2 diabetes and other metabolic risk abnormalities (2). The spectrum of the disease varies from simple steatosis to liver fibrosis and cirrhosis and hepatocellular carcinoma (3). The severity of liver fibrosis stands out as the most influential Georgian Scientists/ . 7 N 4, 2025 503 predictor of clinical outcomes. In fact, therapies targeting fibrosis have become one of the leading focus of research in MAFLD. (4). The current standard for diagnosing and staging liver fibrosis in MAFLD is a liver biopsy (5). This invasive procedure, where a small piece of liver tissue is removed with a needle for a pathologist to examine, carries a small but real risk of complications, including pain and bleeding. Additionally, a biopsy only samples a very small portion of the liver (about 1/50,000th of the organ), which may not accurately represent the overall state of fibrosis (6). It's an expensive procedure and requires specialized medical staff and facilities and the interpretation of the biopsy can vary between different pathologists (6). These limitations are the primary reason why researchers are focused on developing non- invasive methods to assess liver fibrosis in MAFLD. the primary objective of this narrative review is to compare the diagnostic accuracy of the most commonly used non-invasive tests, for predicting different stages of fibrosis (significant fibrosis [ F2], advanced fibrosis [ F3], and cirrhosis [F4]) (Figure 1) in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) (7). The diagnostic performance of noninvasive tests is commonly assessed using the Area Under the Receiver Operating Characteristic (AUROC) curve, which measures the overall accuracy of a test in distinguishing between diseased and non-diseased individuals. An AUROC value of 1.0 represents a perfect test, while a value of 0.5 indicates no diagnostic value beyond chance. In addition to AUROC, a test's ability to correctly identify patients with fibrosis (sensitivity) and those without it (specificity) are also critical measures of performance. This work provides a clear, evidence-based roadmap for clinicians to navigate the available tests and implement a rational, diagnostic strategy in clinical practice. Figure 1. the cirrhotic liver in a laparoscopic view Georgian Scientists/ . 7 N 4, 2025 504 NONINVASIVE SERUM BIOMARKERS FOR MAFLD Noninvasive serum biomarkers for fibrosis were initially developed to use as a diagnostic assessment tool to detect patients who have advanced liver fibrosis and/or cirrhosis, offering an alternative and potential replacement to liver biopsy. A number of noninvasive serum biomarkers have been developed over the last 20 years and we now have tests, such as fibrosis-4 (FIB-4) index (8), NAFLD fibrosis score (NFS) (9) and AST to Platelet Ratio Index (APRI) (10) and A modified APRI (m-APRI) (11), enhanced liver fibrosis (ELF) test (12), Hepamet fibrosis score (HFS) (13), FibroTest (14) and BARD score (15). (Table 1) These relatively common tests are widely available for use in both primary and secondary care and offer a variable degree of accuracy and reliability. Table 1. Scores and formulas used in liver fibrosis staging SCORE FORMULA FIB-4 AST (IU/L) × age (years)/(platelet count (109/L) × (ALT (IU/L))) NFS 1.675 + (0.037 × age (year)) + (0.094 × BMI (kg/m2)) + (1.13 × IFG/diabetes (yes = 1, no = 2)) + (0.99 × AST/ALT ratio) – (0.013 × platelets (109/L)) – (0.66 × albumin (g/dL)) APRI AST (IU/L)/upper limit of normal AST value (IU/L)/platelet count (109/L) × 100 m-APRI Age (years) × (AST (IU/L)/upper limit of normal AST value (IU/L))/platelet count (109/L) × 100 ELF 2.494 + 0.846 ln (HA) + 0.735 ln (PIIINP) + 0.391 ln (TIMP-1) HFS 1/(1 + e [5.390 0.986 × age [45–64 years of age] 1.719 × age [ 65 years of age] + 0.875 × male sex 0.896 × AST [35–69 IU/L] 2.126 × AST [ 70 IU/L] 0.027 × albumin [4–4.49 g/dL] 0.897 × albumin [<4 g/dL] 0.899 × HOMA R [2 3.99 with no diabetes mellitus] 1.497 × HOMA R [ 4 with no diabetes mellitus] 2.184 × diabetes mellitus 0.882 × platelets × 1.000/ L [155 219] 2.233 × platelets × 1.000/ L [<155 ]]). FibroTest (4.467 × log 2-MG)) – (1.357 × log (haptoglobin)) + (1.017 × log (GGT)) + (0.0281 × age (year)) + (1.737 × log (total bilirubin)) – (1.184 × apoA1) + (0.301 × sex (male = 1, female = 0)) – 5.540 BARD score AST/ALT > 0.8 2 points, BMI > 28 1 point, Diabetes diagnosis 1 point Georgian Scientists/ . 7 N 4, 2025 505 FIB-4 FIB-4 index which uses a patient's age, AST and ALT serum activities, and platelet concentration (Table 1). It is widely used serum index for staging fibrosis as a first step. It’s recommended as a first-line assessment in major clinical guidelines, including the American Association for the Study of Liver Diseases (AASLD) Practice Guidance for managing metabolic dysfunction-associated steatotic liver disease (MAFLD) (16). The most accepted FIB-4 cut-off for advanced fibrosis is 2.67 (17) It is important to note that the specific cut- off values can vary slightly depending on the patient population and age. According to a study by Itakura et al. (18) the FIB-4 index has an accuracy rate of approximately 71% for diagnosing cirrhosis in patients with chronic hepatitis B (HBV). Multiple meta-analyses (19) (20) have found that both the FIB-4 and APRI scores are effective for assessing fibrosis in patients with chronic hepatitis B, but one study (21) found that FIB-4 has a higher diagnostic accuracy than APRI for predicting moderate to advanced fibrosis and cirrhosis. The FIB-4 index is a practical and valuable tool for screening for liver fibrosis, primarily due to its high negative predictive value (NPV) (22). This means a cut- off of 1.3 can effectively rule out advanced fibrosis (23). However, the effectiveness of the FIB-4 index changes with age. In patients aged 65 or older, the score's specificity for advanced fibrosis is lower, which leads to a higher rate of false positives. a higher cut-off of 2.0 is recommended for this age group (24). Even with its usefulness, a single FIB-4 test may not be sufficient to rule out metabolic dysfunction-associated steatotic liver disease (MAFLD) in patients with a high prevalence of diabetics. If there is suspicion of advanced fibrosis, it is recommended to re-evaluate the patient or use more specific diagnostic methods (25). 1.1.1. NAFLD fibrosis score (NFS) The NAFLD Fibrosis Score (NFS) uses a more comprehensive set of parameters than FIB-4, including BMI, the presence of diabetes, and albumin levels, in addition to the components shared with FIB-4. (table 1). The European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), and European Association for the Study of Obesity (EASO) Clinical Practice Guidelines recommend using the NFS for diagnosing advanced liver fibrosis in patients with MAFLD (26). However, despite their practicability, when used in the general population, they can have a significant number of false-positive and false-negative results. Because of this, their use is typically restricted to individuals at higher risk for liver disease (27). 1.1.2. AST to platelets ratio index The AST to Platelet Ratio Index (APRI) was initially developed to determine the presence of liver fibrosis and to distinguish between different stages of fibrosis and cirrhosis in patients with hepatitis C virus (HCV) (28). A modified APRI (m-APRI) which incorporates Georgian Scientists/ . 7 N 4, 2025 506 age and serum albumin levels in the APRI formula has been proposed (Table 1) (11). The addition of these parameters has been shown to improve the score's accuracy in predicting advanced fibrosis and cirrhosis in patients with viral hepatitis (29). FibroTest The FibroTest® has been validated for use in various liver diseases, including viral hepatitis, alcohol-associated liver disease, and MAFLD (30). To the age and gender of the patient, it combines several serum biomarkers, namely alpha-2-macroglobulin (A2M), haptoglobin, apolipoprotein A-1 (Apo-A1), bilirubin, and gamma-glutamyltranspeptidase (GGT) (31). A recent meta-analysis by Vali et al. (32) concluded that while FibroTest has an acceptable performance for detecting cirrhosis (AUC = 0.92) in MAFLD patients, its accuracy is limited when it comes to predicting moderate and advanced fibrosis. Despite this, FibroTest has good predictive values for diagnosing liver fibrosis in MAFLD patients and is included in the EASL-EASD-EASO Clinical Practice Guidelines (26). 1.1.3. Hepamet fibrosis score (HFS) the Hepamet fibrosis score (HFS) uses variables also used by other NITs, like age, presence of diabetes, AST, albumin, and platelet count, but also gender and insulin resistance assessed by the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (33). HFS is more accurate than both FIB-4 and NFS at identifying patients with advanced fibrosis. Several studies (34) (35) have validated its thresholds and confirmed that HFS has superior diagnostic accuracy and a higher negative predictive value (NPV) compared to NFS and FIB-4 in patients with metabolic hepatic steatosis. The HFS has also proven to be as reliable as NFS and FIB-4 for predicting cirrhosis, long-term liver-related events, hepatocarcinoma, and overall mortality. It performs particularly well in predicting moderate and severe fibrosis. As a newer NIT, it has not been as extensively validated as other scores, but it has demonstrated sensitivity and NPV values ranging from 74% to 90% and 90% to 98%, respectively (36). BARD score The BARD score, proposed by Harrison et al.(37), is a non-invasive tool that considers three factors to assess liver fibrosis: The presence of type 2 diabetes mellitus. The patient’s body mass index (BMI). The AST/ALT ratio from liver serum enzyme activity. This score has a high negative predictive value (NPV) of approximately 96% in patients with MAFLD (37). Georgian Scientists/ . 7 N 4, 2025 507 Enhanced Liver Fibrosis (ELF) test The Enhanced Liver Fibrosis (ELF) test is a blood test that measures three direct markers of fibrosis: hyaluronic acid (HA), procollagen III amino-terminal peptide (PIIINP), and tissue inhibitor of metalloproteinase 1 (TIMP-1). It was developed in a mixed hepatitis C- dominated patient sample (38). the ELF test shows high diagnostic accuracy for advanced liver fibrosis with AUC values of 0.83 and 0.92 (39). The ELF test has been recommended by the National Institute for Health and Care Excellence (NICE) (40) and EASL (41) as a screening tool for liver fibrosis. IMAGING MODALITIES FOR FIBROSIS ASSESSMENT The most widely used imaging modality for this purpose is Transient Elastography (TE), commonly known by the name FibroScan. The ultrasound-based vibration-controlled transient elastography (VCTE) measures liver stiffness by using a low-frequency vibration to generate a shear wave that travels through the liver (42). The velocity of this wave is directly related to the stiffness of the liver tissue. Transient Elastography is rapid, non-invasive, and can be performed at the point of care. It provides two key measurements: Liver Stiffness Measurement (LSM) which is a quantitative indicator of fibrosis in kilopascals (Table 2) and is computed from the velocity of these mechanical waves and Controlled Attenuation Parameter (CAP), which quantifies liver steatosis (42). LSM by VCTE is only correlated with fibrosis and does not provide information, even if indirect, regarding other histological features (43). Its accuracy can be affected by factors like obesity, elevated liver enzymes. Parallel to ultrasound-based transient elastography, magnetic resonance elastography (MRE) is an advanced technological approach used to determine liver stiffness through MRI imaging combined with low-frequency vibrations. In contrast with ultrasound-based VCTE, MRE offers a thorough assessment of the entire liver, presenting advantages such as minimal sampling error, a low failure rate, and high repeatability (44). However, it is also more expensive, less widely available, and requires a dedicated MRI machine, which limits its use. Table 2. Fibrosis score Liver Disease F0 to F1 (Normal) F2 (Moderate Scarring) F3 (Severe Scarring) F4 (Cirrhosis) Metabolic Dysfunction- Associated Fatty Liver Disease (MAFLD) 2 to 7 kPa 7.5 to 10 kPa 10 to 14 kPa 14 kPa Georgian Scientists/ . 7 N 4, 2025 508 Normal Score – No scarring or mild scarring. Moderate to Severe Score – Reversible changes in the liver that lifestyle modifications and a healthy diet can help manage. Patients may not be symptomatic. Advanced Score - The patient is cirrhotic with advanced liver diseases A summary of the accuracy measures, including AUROC, sensitivity, and specificity for these non-invasive tests, is presented in Table 3 Table 3. Accuracy measures of NIT used the assessment of fibrosis in MAFLD Noninvasive tests Accuracy measures Serum-based scores APRI AUROC 0.67-0.83, sensitivity 27-78.1%, speci city 66.7-90.5% BARD score AUROC 0.70-0.87, sensitivity 72.7-89%, speci city 44-88.9% FIB-4 AUROC 0.70-0-90, sensitivity 42.9-100%, speci city 65-93% NFS AUROC 0.65-0.88, sensitivity 44.2-82%, speci city 58-98% HFS AUROC 0.69-0.88, sensitivity 51.9-90.5%, speci city 71.6-97% Serum-based patented tests ELF AUROC 0.79-0.93, sensitivity 65-80%, speci city 72-90% FibroTest AUROC 0.75-0.88, sensitivity 88-95%, speci city 69-71% Imaging methods VCTE AUROC 0.80-0.95, sensitivity 71-92%, speci city 75-89.9% MRE AUROC 0.89-0.96, sensitivity 78-98%, speci city 85-100% RESULTS This narrative review combined data from existing literature to compare the diagnostic accuracy of the most commonly used non-invasive tests for assessing liver fibrosis in patients with MAFLD. The key findings, summarized in Table 2, highlight a spectrum of diagnostic performance, ranging from simple serum-based scores to imaging modalities. Georgian Scientists/ . 7 N 4, 2025 509 The serum-based scores showed variable accuracy for identifying significant to advanced fibrosis. The FIB-4 index, while widely recommended for its high negative predictive value, shows a broad AUROC range from 0.70 to 0.90, reflecting its variable performance across different patient populations and ages. Similarly, the NAFLD Fibrosis Score (NFS) and the AST to Platelet Ratio Index (APRI) also exhibited a wide range of accuracy, with AUROC values spanning from 0.65 to 0.88 and 0.67 to 0.83. The Hepamet Fibrosis Score (HFS) and the Enhanced Liver Fibrosis (ELF) test demonstrated superior accuracy, with AUROC values ranging from 0.69 to 0.88 for HFS and 0.79 to 0.93 for the ELF test, suggesting a more reliable performance, particularly for advanced fibrosis. Imaging modalities consistently showed the highest diagnostic accuracy for fibrosis staging. Transient Elastography (VCTE), with an AUROC range of 0.80 to 0.95, exhibited high sensitivity and specificity in identifying advanced fibrosis. MRE consistently showed the highest diagnostic performance for fibrosis staging, with AUROC values ranging from 0.89 to 0.96. The high sensitivity and specificity of this method highlight its status as the most accurate non-invasive tool for assessing fibrosis and cirrhosis DISCUSSION The limitations of liver biopsy have driven the development of a non-invasive tests, each offering unique strengths and weaknesses. The findings of this narrative review provide a comprehensive comparison of these tools. Comparison of Diagnostic Accuracy As demonstrated in Table 2, a general pattern appears in which imaging modalities provide superior diagnostic accuracy compared to serum-based tests. MRE and VCTE consistently achieve the highest AUROC values, making them the most reliable non-invasive tools for staging liver fibrosis, particularly in advanced stages. Among the serum tests, the patented tests like FibroTest and the ELF test generally outperform the simple, calculator-based scores (e.g., FIB-4 and NFS). This highlights the value of using a combination of direct and indirect markers to improve diagnostic performance. Clinical and Practical Utility While diagnostic accuracy is important, clinical utility is determined by a test's accessibility, cost, and practicality. Simple serum-based scores like FIB-4 and NFS are widely available, inexpensive, and can be easily calculated in any clinical setting. Our analysis supports their use as first-line screening tools in primary care, particularly given their high negative predictive value, which effectively rules out advanced fibrosis in low-risk individuals and avoids unnecessary referrals. For patients flagged as high-risk, a more accurate second-line Georgian Scientists/ . 7 N 4, 2025 510 test is warranted. VCTE represents an ideal second-line test. It is more accurate than simple serum scores, relatively affordable, and can be performed quickly at the point of care. MRE, despite being the most accurate non-invasive test, is the least practical due to its high cost and limited availability, positioning it as a third-line diagnostic tool for challenging cases. Limitations The non-invasive tests reviewed in this narrative review are not without limitations. Factors such as severe obesity and elevated liver enzymes can reduce the accuracy of VCTE. the simple serum-based scores like FIB-4 are not perfect. Their diagnostic performance can be influenced by age, as older patients tend to have higher scores even in the absence of advanced fibrosis, potentially leading to false-positive results. The patented serum tests are exclusive and have a higher cost, limiting their widespread use. This suggests that the ideal diagnostic strategy must balance accuracy, cost, and accessibility to be truly effective on a global scale. CONCLUSION Non-invasive approach to fibrosis assessment is not only useful but also offers a safe, effective and practical alternative to liver biopsy. This narrative review confirms that while simple serum-based scores like FIB-4 and NFS are valuable for initial population-wide screening, they are outmatched in diagnostic accuracy by imaging modalities like VCTE and MRE. A rational approach, starting with accessible scores for screening and escalating to more accurate imaging-based tests for at-risk patients, represents the most efficient strategy for managing liver fibrosis in MAFLD. This minimizes the need for liver biopsy and promises to improve clinical outcomes for the millions of people affected by this growing global health challenge. REFERENCES 1. Guo, Z., Wu, D., Mao, R. et al. Global burden of MAFLD, MAFLD related cirrhosis and MASH related liver cancer from 1990 to 2021. Sci Rep 15, 7083 (2025). https://doi.org/10.1038/s41598-025-91312-5 2. Li QQ, Xiong YT, Wang D, Wang KX, Guo C, Fu YM, Niu XX, Wang CY, Wang JJ, Ji D, Bai ZF. Metabolic syndrome is associated with significant hepatic fibrosis and steatosis in patients with nonalcoholic steatohepatitis. ILIVER. 2024 Apr 24;3(2):100094. doi: 10.1016/j.iliver.2024.100094. PMID: 40636476; PMCID: PMC12212728. 3. Leung PB, Davis AM, Kumar S. Diagnosis and Management of Nonalcoholic Fatty Liver Disease. JAMA. 2023;330(17):1687–1688. doi:10.1001/jama.2023.17935 Georgian Scientists/ . 7 N 4, 2025 511 4. Qu W, Ma T, Cai J, Zhang X, Zhang P, She Z, Wan F and Li H (2021) Liver Fibrosis and MAFLD: From Molecular Aspects to Novel Pharmacological Strategies. Front. Med. 8:761538. doi: 10.3389/fmed.2021.761538 5. Martinou, E.; Pericleous, M.; Stefanova, I.; Kaur, V.; Angelidi, A.M. Diagnostic Modalities of Non-Alcoholic Fatty Liver Disease: From Biochemical Biomarkers to Multi- Omics Non-Invasive Approaches. Diagnostics2022, 12, 407. https://doi.org/10.3390/diagnostics12020407 6. Papastergiou V, Tsochatzis E, Burroughs AK. Non-invasive assessment of liver fibrosis. Ann Gastroenterol. 2012;25(3):218-231. PMID: 24714123; PMCID: PMC3959378. 7. Lee JH, Joo I, Kang TW, Paik YH, Sinn DH, Ha SY, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30:1264–73. doi: 10.1007/s00330-019-06407-1. 8. Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, et al. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest. Hepatology. 2007;46:32–36. doi: 10.1002/hep.21669. 9. Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45:846–854. doi: 10.1002/hep.21496. 10. Loaeza-del-Castillo, Aurora, Francisco Paz-Pineda, Edgar Oviedo-Cárdenas, Francisco Sánchez-Ávila, and Florencia Vargas-Vorácková. 2008. “AST to Platelet Ratio Index (APRI) for the Noninvasive Evaluation of Liver Fibrosis.” Annals of Hepatology 7: 350–57. https://doi.org/10.1016/S1665-2681(19)31836-8. 11. Huang C, Seah JJ, Tan CK, Kam JW, Tan J, Teo EK, et al. Modified AST to platelet ratio index improves APRI and better predicts advanced fibrosis and liver cirrhosis in patients with non-alcoholic fatty liver disease. Clin Res Hepatol Gastroenterol. 2021;45:101528. doi: 10.1016/j.clinre.2020.08.006. 12. Rosenberg WM, Voelker M, Thiel R, Becka M, Burt A, Schuppan D, et al. Serum markers detect the presence of liver fibrosis: a cohort study. Gastroenterology. 2004;127:1704–1713. doi: 10.1053/j.gastro.2004.08.052. 13. Higuera-de-la-Tijera F, Córdova-Gallardo J, Buganza-Torio E, Barranco-Fragoso B, Torre A, Parraguirre-Martínez S, Rojano-Rodríguez ME, Quintero-Bustos G, Castro-Narro G, Moctezuma-Velazquez C. Hepamet Fibrosis Score in Nonalcoholic Fatty Liver Disease Patients in Mexico: Lower than Expected Positive Predictive Value. Dig Dis Sci. 2021 Dec;66(12):4501-4507. doi: 10.1007/s10620-020-06821-2. Epub 2021 Jan 11. PMID: 33428035. Georgian Scientists/ . 7 N 4, 2025 512 14. Poynard T, Imbert-Bismut F, Munteanu M, Messous D, Myers RP, Thabut D, et al. Overview of the diagnostic value of biochemical markers of liver fibrosis (FibroTest, HCV FibroSure) and necrosis (ActiTest) in patients with chronic hepatitis C. Comp Hepatol. 2004;3:8. doi: 10.1186/1476-5926-3-8. 15. Cicho -Lach H, Celi ski K, Prozorow-Król B, Swatek J, S omka M, Lach T. The BARD score and the NAFLD fibrosis score in the assessment of advanced liver fibrosis in nonalcoholic fatty liver disease. Med Sci Monit. 2012 Dec;18(12):CR735-40. doi: 10.12659/msm.883601. PMID: 23197236; PMCID: PMC3560810. 16. Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, et al. AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023;77:1797–835. doi: 10.1097/HEP.0000000000000323. 17. Anstee QM, Lawitz EJ, Alkhouri N, Wong VW-S, Romero-Gomez M, Okanoue T, et al. Noninvasive tests accurately identify advanced fibrosis due to NASH: baseline data from the STELLAR trials. Hepatology. 2019;70:1521–30. doi: 10.1002/hep.30842. 18. Itakura J, Kurosaki M, Setoyama H, Simakami T, Oza N, Korenaga M, et al. Applicability of APRI and FIB-4 as a transition indicator of liver fibrosis in patients with chronic viral hepatitis. J Gastroenterol. 2021;56:470–8. doi: 10.1007/s00535-021-01782-3. 19. Xu X-Y, Wang W-S, Zhang Q-M, Li J-L, Sun J-B, Qin T-T, et al. Performance of common imaging techniques vs serum biomarkers in assessing fibrosis in patients with chronic hepatitis B: a systematic review and meta-analysis. World J Clin Cases. 2019;7:2022–37. doi: 10.12998/wjcc.v7.i15.2022. 20. Xu X-Y, Kong H, Song R-X, Zhai Y-H, Wu X-F, Ai W-S, et al. The effectiveness of noninvasive biomarkers to predict hepatitis B-related significant fibrosis and cirrhosis: a systematic review and meta-analysis of diagnostic test accuracy. PLoS One. 2014;9:e100182. doi: 10.1371/journal.pone.0100182. 21. Xiao G, Yang J, Yan L. Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology. 2015;61:292–302. doi: 10.1002/hep.27382. 22. Roh YH, Kang B-K, Jun DW, Lee C, Kim M. Role of FIB-4 for reassessment of hepatic fibrosis burden in referral center. Sci Rep. 2021;11:13616. doi: 10.1038/s41598-021-93038- 6. 23. Moolla A, Motohashi K, Marjot T, Shard A, Ainsworth M, Gray A, et al. A multidisciplinary approach to the management of NAFLD is associated with improvement in markers of liver and cardio-metabolic health. Frontline Gastroenterol. 2019;10:337–46. doi: 10.1136/flgastro-2018-101155. Georgian Scientists/ . 7 N 4, 2025 513 24. McPherson S, Hardy T, Dufour J-F, Petta S, Romero-Gomez M, Allison M, et al. Age as a confounding factor for the accurate non-invasive diagnosis of advanced NAFLD fibrosis. Am J Gastroenterol. 2017;112:740–51. doi: 10.1038/ajg.2016.453. 25. Hagström H, Talbäck M, Andreasson A, Walldius G, Hammar N. Repeated FIB-4 measurements can help identify individuals at risk of severe liver disease. J Hepatol. 2020;73:1023–9. doi: 10.1016/j.jhep.2020.06.007. 26. European Association for the Study of the Liver (EASL) ; European Association for the Study of Diabetes (EASD) ; European Association for the Study of Obesity (EASO) EASL–EASD–EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease. J Hepatol. 2016;64:1388–402. doi: 10.1016/j.jhep.2015.11.004. 27. Graupera I, Thiele M, Serra-Burriel M, Caballeria L, Roulot D, Wong GL-H, et al. Low accuracy of FIB-4 and NAFLD fibrosis scores for screening for liver fibrosis in the population. Clin Gastroenterol Hepatol. 2022;20:2567–76.e6. doi: 10.1016/j.cgh.2021.12.034. 28. Maroto-García J, Moreno Álvarez A, Sanz de Pedro MP, Buño-Soto A, González Á. Serum biomarkers for liver fibrosis assessment. Adv Lab Med. 2023 Nov 14;5(2):115-130. doi: 10.1515/almed-2023-0081. PMID: 38939201; PMCID: PMC11206202. 29. Zhao Y, Thurairajah PH, Kumar R, Tan J, Teo EK, Hsiang JC. Novel non-invasive score to predict cirrhosis in the era of hepatitis C elimination: a population study of ex- substance users in Singapore. Hepatobiliary Pancreat Dis Int. 2019;18:143–8. doi: 10.1016/j.hbpd.2018.12.002. 30. Vali Y, Lee J, Boursier J, Spijker R, Verheij J, Brosnan MJ, et al.; On Behalf Of The Litmus Systematic Review Team. FibroTest for Evaluating Fibrosis in Non Alcoholic Fatty Liver Disease Patients: A Systematic Review and Meta-Analysis. J Clin Med 2021;10:2415. 31. Munteanu M, Tiniakos D, Anstee Q, Charlotte F, Marchesini G, Bugianesi E, et al. Diagnostic performance of FibroTest, SteatoTest and ActiTest in patients with NAFLD using the SAF score as histological reference. Aliment Pharmacol Ther. 2016;44:877–89. doi: 10.1111/apt.13770. 32. Vali Y, Lee J, Boursier J, Spijker R, Verheij J, Brosnan MJ, et al. FibroTest for evaluating fibrosis in non-alcoholic fatty liver disease patients: a systematic review and meta-analysis. J Clin Med. 2021;10:2415. doi: 10.3390/jcm10112415. 33. Ampuero J, Pais R, Aller R, Gallego-Durán R, Crespo J, García-Monzón C, et al. Development and validation of Hepamet fibrosis scoring system-A simple, noninvasive test to identify patients with nonalcoholic fatty liver disease with advanced fibrosis. Clin Gastroenterol Hepatol. 2020;18:216–25.e5. doi: 10.1016/j.cgh.2019.05.051. Georgian Scientists/ . 7 N 4, 2025 514 34. Rigor J, Diegues A, Presa J, Barata P, Martins-Mendes D. Noninvasive fibrosis tools in NAFLD: validation of APRI, BARD, FIB-4, NAFLD fibrosis score, and Hepamet fibrosis score in a Portuguese population. Postgrad Med. 2022;134:435–40. doi: 10.1080/00325481.2022.2058285. 35. Higuera-de-la-Tijera F, Córdova-Gallardo J, Buganza-Torio E, Barranco-Fragoso B, Torre A, Parraguirre-Martínez S, et al. Hepamet fibrosis score in nonalcoholic fatty liver disease patients in Mexico: lower than expected positive predictive value. Dig Dis Sci. 2021;66:4501–7. doi: 10.1007/s10620-020-06821-2. 36. Zambrano-Huailla R, Guedes L, Stefano JT, de Souza aa, Marciano S, Yvamoto E, et al. Diagnostic performance of three non-invasive fibrosis scores (Hepamet, FIB-4, NAFLD fibrosis score) in NAFLD patients from a mixed Latin American population. Ann Hepatol 2020;19:622–6. 37. Harrison SA, Oliver D, Arnold HL, Gogia S, Neuschwander-Tetri BA. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut. 2008;57:1441–7. doi: 10.1136/gut.2007.146019. 38. Rosenberg W.M.C., Voelker M., Thiel R., Becka M., Burt A., Schuppan D., et al. Serum markers detect the presence of liver fibrosis: a cohort study. Gastroenterology. 2004;127:1704–1713. doi: 10.1053/j.gastro.2004.08.052. 39. Vali Y., Lee J., Boursier J., Spijker R., Löffler J., Verheij J., et al. Enhanced liver fibrosis test for the non-invasive diagnosis of fibrosis in patients with NAFLD: a systematic review and meta-analysis. J Hepatol. 2020;73:252–262. doi: 10.1016/j.jhep.2020.03.036. 40. Glen J., Floros L., Day C., Pryke R. Non-alcoholic fatty liver disease (NAFLD): summary of NICE guidance. BMJ. 2016;354:i4428. doi: 10.1136/bmj.i4428. 41. Berzigotti A., Tsochatzis E., Boursier J., Castera L., Cazzagon N., Friedrich-Rust M., et al. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol. 2021;75:659–689. doi: 10.1016/j.jhep.2021.05.025. 42. Mueller S, Sandrin L. Liver stiffness: a novel parameter for the diagnosis of liver disease. Hepat Med. 2010;2:49-67 https://doi.org/10.2147/HMER.S7394 43. Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 2019;156:1717–30. 44. Mariappan YK, Glaser KJ, Ehman RL. Magnetic resonance elastography: a review. Clin Anat 2010;23:497–511.