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Table of Contents
EDITORIAL
Year : 2022  |  Volume : 10  |  Issue : 1  |  Page : 1-2

Newer Investigations in the diagnosis of cirrhosis of liver


Institute of Gastrointestinal Sciences, Apollo BGS Hospital, Mysuru, Karnataka, India

Date of Submission18-Dec-2021
Date of Acceptance18-Dec-2021
Date of Web Publication06-Jan-2022

Correspondence Address:
Dr Rajkumar Prannath Wadhwa
Head and Chief Consultant Institute of Gastrointestinal Sciences, Apollo BGS Hospital, Mysuru, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ajim.ajim_130_21

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How to cite this article:
Wadhwa RP, Ravindranath A. Newer Investigations in the diagnosis of cirrhosis of liver. APIK J Int Med 2022;10:1-2

How to cite this URL:
Wadhwa RP, Ravindranath A. Newer Investigations in the diagnosis of cirrhosis of liver. APIK J Int Med [serial online] 2022 [cited 2022 Jul 1];10:1-2. Available from: https://www.ajim.in/text.asp?2022/10/1/1/335077



Assessing stages of liver fibrosis (FIB) are imperative to prognosticate and also to anticipate complications. Liver biopsy has the inherent disadvantage of being invasive and hence cannot be repeated periodically to assess progression; also, the FIB stage in the biopsy specimen may not be representative of the entire liver. Several noninvasive biomarkers have been developed and investigated to predict FIB stage. Direct markers of FIB rely on the basic underlying mechanisms of FIB, whereas the indirect markers of FIB are based on the effects of the fibrogenic process. The aspartate transaminase (AST):alanine transaminase (ALT) ratio and the AST: platelet index are easy to calculate but are inaccurate for advanced FIB and for nonalcoholic fatty liver disease (NAFLD).[1] Age, AST, ALT, and platelet count are used to calculate FIB-4 index with moderate accuracy. The NAFLD FIB score is a more comprehensive tool comprising age, body mass index (BMI), impaired fasting glucose or diabetes mellitus, AST: ALT ratio, platelet count, and serum albumin levels and it has better validity.[2] The BARD score calculated from BMI, diabetes mellitus, and AST: ALT ratio also has moderate accuracy in NAFLD.[3] The easy applicability in clinical practice makes these scores attractive options, although they lack good accuracy.

Kupffer cells activate the stellate cells of the liver in response to injury and inflammation. Stellate cells produce matrix proteins and cytokines such as hyaluronic acid, Type III collagen, tissue inhibitor of metalloproteinases-1 (TIMP-1), and the Wisteria floribunda agglutinin-positive Mac-2 binding protein. The specific indicators of FIB measure markers of fibrogenesis in the blood with good accuracy. Procollagen III amino-terminal peptide (PIIINP) is secreted when new Type III collagen is synthesized or degraded. To improve the accuracy, a neoepitope-specific competitive ELISA for PIIINP, that is, pro-C3 has been found to be correlating with the level of FIB as it is liver specific.[4] Hyaluronic acid is a nonproteoglycan polysaccharide and is a component of the extracellular matrix. Hyaluronic acid has an area under the receiver operating characteristic (AUROC) of 0.87 for F2 FIB and 0.92 for cirrhosis.[5] TIMP-1 regulates matrix metalloproteinases and reflects alterations in tissue matrix remodeling during hepatic fibrogenesis and fibrinolysis.[6] Laminin is a noncollagenous glycoprotein in basement membranes whose levels also get altered with FIB. Although these markers have excellent accuracy, widespread availability is a practical concern.

The combination of various direct and indirect markers was then assessed to improve the positive predictive value, but applicability across all etiologies of cirrhosis is questionable. The enhanced liver FIB panel is an algorithm consisting of three specific FIB markers: PIIINP, hyaluronic acid, and TIMP-1, which has a reliable prediction of FIB in adult and pediatric cases of NAFLD. FibroTest has five components (serum levels of gamma-glutamyl transferase, total bilirubin, α2 m, apolipoprotein AI and haptoglobin) and FibroMeter vibration-controlled transient elastography algorithm (FibroMeter NAFLD: Bodyweight, prothrombin index, ALT, AST, ferritin, fasting glucose, and liver stiffness measurement) both of which have good reliability and accuracy in predicting FIB in NAFLD.[7]

Image-based indicators of FIB include FibroScan, Two-dimensional (2D) shear-wave elastography, and magnetic resonance elastography (MRE) which have acceptable negative predictive values but lack good positive predictive value.

Transient elastography accurately diagnoses cirrhosis (FIB Stage 4 [F4]) and is useful for distinguishing advanced FIB (i.e., FIB Stage 2 [F2] or greater) from minimal or no FIB (FIB Stage 1 [F1] or FIB Stage 0 [F0]).[8] Two-dimensional shear-wave elastography accurately diagnoses early liver FIB in patients with chronic liver disease. AUROC for diagnosis of FIB Stage ≥F2 is typically >0.75. Greater accuracy is observed in diagnosing cirrhosis, with the area under the curve >0.80 for FIB Stage ≥F4.[9] To overcome observer dependence and to get a more global assessment of the liver, MRE is a better modality. MRE can be used where this facility is available. The advantage is MRE interrogates the entire liver. A meta-analysis that included 12 studies of MRE found the following test characteristics:

  • Detecting any FIB (FIB Stage ≥F1): Optimal cutoff 3.45 kPa, sensitivity 73%, and specificity 79%
  • Detecting significant FIB (≥F2): Optimal cutoff 3.66 kPa, sensitivity 79%, and specificity 81%
  • Detecting advanced FIB (≥F3): Optimal cutoff 4.11 kPa, sensitivity 85%, and specificity 85%
  • Detecting cirrhosis (F4): Optimal cutoff 4.71, sensitivity 91%, specificity 81%.[10]


Despite having many combinations of innovative blood investigations and imaging modalities, there are many confounders precluding appropriate interpretation of results. Artificial intelligence (AI) is the newest addition which can interpret imaging data with the help of convolutional neural network. A recent meta-analysis included 19 studies reporting the performances of AI-assisted ultrasonography, elastography, computed tomography, magnetic resonance imaging, and clinical parameters for the diagnosis of liver FIB. For the diagnosis of liver FIB, the pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and DOR were 0.78 (0.71–0.85), 0.89 (0.81–0.94), 0.72 (0.58–0.83), 0.92 (0.88–0.94), and 31.58 (11.84–84.25), respectively. However, further validation is required for clinical application.[11],[12]

Using multiple serologic panels or combining serologic panels with radiographic imaging may improve the ability to correctly assess the degree of a patient's FIB.8. In addition, it may be possible to improve the diagnostic performance of these panels if they are used in stepwise combinations.[13] In the given article, authors have compared Fib4 and APRI against Fibroscan to determine the predictive values of either of them in different etiologies of chronic liver diseases. They have concluded that APRI and Fib4 can be used as the alternative to Fibroscan in resource limited settings. However, we ought to understand that Fibroscan is not the gold standard and hence, there are limitations in generalized applicability of the results of the study. Hence, it is important to note that while authors have used these tests in clinical settings, it is still not possible to accurately predict liver FIB using a single method and combination of serological and radiological methods and perhaps, AI will give us the answer in future.



 
  References Top

1.
Shaheen AA, Myers RP. Diagnostic accuracy of the aspartate aminotransferase-to-platelet ratio index for the prediction of hepatitis C-related fibrosis: A systematic review. Hepatology 2007;46:912-21.  Back to cited text no. 1
    
2.
Wong VW, Wong GL, Chim AM, Tse AM, Tsang SW, Hui AY, et al. Validation of the NAFLD fibrosis score in a Chinese population with low prevalence of advanced fibrosis. Am J Gastroenterol 2008;103:1682-8.  Back to cited text no. 2
    
3.
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.  Back to cited text no. 3
    
4.
Leeming DJ, Grove J, Kaye P, Hoad CL, Francis ST, Nielsen MJ, et al. Estimation of serum “true collagen type III formation” (Pro-C3) levels as a marker of non-alcoholic steatohepatitis in a prospective cohort. J Hepatol 2017;66 Suppl 1:S154.  Back to cited text no. 4
    
5.
Suzuki A, Angulo P, Lymp J, Li D, Satomura S, Lindor K. Hyaluronic acid, an accurate serum marker for severe hepatic fibrosis in patients with non-alcoholic fatty liver disease. Liver Int 2005;25:779-86.  Back to cited text no. 5
    
6.
Hemmann S, Graf J, Roderfeld M, Roeb E. Expression of MMPs and TIMPs in liver fibrosis – A systematic review with special emphasis on anti-fibrotic strategies. J Hepatol 2007;46:955-75.  Back to cited text no. 6
    
7.
Boursier J, Vergniol J, Guillet A, Hiriart JB, Lannes A, Le Bail B, et al. Diagnostic accuracy and prognostic significance of blood fibrosis tests and liver stiffness measurement by FibroScan in non-alcoholic fatty liver disease. J Hepatol 2016;65:570-8.  Back to cited text no. 7
    
8.
Castéra L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, et al. Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 2005;128:343-50.  Back to cited text no. 8
    
9.
Gao Y, Zheng J, Liang P, Tong M, Wang J, Wu C, et al. Liver fibrosis with two-dimensional US shear-wave elastography in participants with chronic hepatitis B: A prospective multicenter study. Radiology 2018;289:407-15.  Back to cited text no. 9
    
10.
Singh S, Venkatesh SK, Wang Z, Miller FH, Motosugi U, Low RN, et al. Diagnostic performance of magnetic resonance elastography in staging liver fibrosis: A systematic review and meta-analysis of individual participant data. Clin Gastroenterol Hepatol 2015;13:440-51.e6.  Back to cited text no. 10
    
11.
Choi KJ, Jang JK, Lee SS, Sung YS, Shim WH, Kim HS, et al. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology 2018;289:688-97.  Back to cited text no. 11
    
12.
Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: A systematic review and meta-analysis. BMC Gastroenterol 2021;21:10.  Back to cited text no. 12
    
13.
Sebastiani G, Vario A, Guido M, Noventa F, Plebani M, Pistis R, et al. Stepwise combination algorithms of non-invasive markers to diagnose significant fibrosis in chronic hepatitis C. J Hepatol 2006;44:686-93.  Back to cited text no. 13
    




 

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