Original ContributionHepatic Elastometry and Glissonian Line in the Assessment of Liver Fibrosis
Introduction
Liver affections constitute a heterogeneous group of diseases with many different etiologies of which the most common are alcoholic and non-alcoholic fatty liver disease and viral hepatitis (Carbone and Neuberger 2014; Wilkins et al. 2015; Murphy et al. 2017; Liu et al. 2019). Chronic liver disease (CLD) causes constant inflammation that leads to the development of fibrosis and cirrhosis, which may cause liver failure and hepatocellular carcinoma development (Schuppan and Afdhal 2008). Accurate prediction of disease progress is crucial for proper patient management, and the Metavir classification was previously defined to objectively track disease progression (especially fibrosis staging) (Goodman 2007). Fibrosis progression is staged according to Metavir as follows: F0 = no fibrosis, F1 = mild fibrosis, F2 = significant fibrosis, F3 = severe fibrosis and F4 = cirrhosis.
To date, the gold standard technique is liver biopsy although it is an invasive procedure with possible complications such as hemorrhage and pain, but mortality is rare. Moreover, it is affected by sampling bias because of the small size of the hepatic tissue studied (1/50,000th of liver weight) (Van Thiel et al. 1993; Martínez et al. 2011).
Some studies have focused on the use of specific serum biomarkers, but these failed to assess early stages of the disease accurately; therefore, their use in clinical practice was limited (Lurie et al. 2015).
Computed tomography (CT) and magnetic resonance imaging (MRI) are effective for the diagnosis and grading of liver fibrosis, but they have limitations in that they are expensive (Sistrom and McKay 2005) and slightly widespread among peripheral hospitals (Ginde et al. 2008; Economou and Panteli 2019), which makes their use to screen and evaluate the large number of patients with liver diseases impractical in clinical practice. Moreover, the ionizing radiation employed by CT contraindicates its use for long-term follow-up and for women of childbearing age or pregnant women (Schmidt 2012). MRI evaluation, on the other hand, can be limited by metal prostheses, patient claustrophobia and its long duration (Yousaf et al. 2018). Despite these limits, MRI has high sensitivity, specificity and accuracy in detecting and staging hepatic fibrosis (Han et al. 2017; Park et al. 2017; Li et al. 2019).
B-Mode echography is the most widely used method for CLD assessment, although this technique allows only qualitative evaluation of liver echostructure and Glisson capsule regularity. Furthermore, some issues are related to the competence and experience of physicians, which makes the effectiveness of ultrasonography dependent on physicians (Ferral et al. 1992; Kudo et al. 2008).
Ultrasound elastography is an effective non-invasive method for liver staging that provides a quantitative measurement of liver fibrosis by detecting changes in hepatic stiffness caused by pathologic conditions (i.e., inflammation or fibrosis) (Murad Gutiérrez and Romero Enciso 2018). Different elastometric techniques and devices have been developed for this purpose, but the most commonly used and validated techniques are vibration-controlled transient elastography (VCTE), shear wave elastography (SWE), sound touch elastography (STE) and acoustic radiation force impulse (ARFI) elastography.
Vibration-controlled transient elastography has been a pioneering technology in the elastometric diagnosis of cirrhosis (87% and 91% sensitivity and specificity, respectively) and fibrosis (70% and 84% sensitivity and specificity respectively) (Tsochatzis et al. 2011; Bota et al. 2013; Brener 2015; Ragazzo et al. 2017). VCTE has also been proposed as a reference method for studying elastometric techniques other than liver biopsy (Sporea et al. 2014).
Some studies have established that ARFI elastography has a predictive value similar to that of VCTE with high specificity and sensitivity (Tsochatzis et al. 2011; Bota et al. 2013). Gatos et al. (2020) emphasized that VCTE, STE and SWE achieve similar performance using liver biopsy as a reference, and the receiver operating characteristic curve (ROC) yielded areas under the curve (AUROC) of approximately 0.95 in differentiating between different Metavir stages.
An innovative approach is the use of computer neural networks that allow training deep learning algorithms through some medical features and can be used in clinical research, computer-aided diagnosis (CAD) or classification of the severity of some medical phenomena (Handelman et al. 2018). A texture analysis of SWE images with a CAD algorithm has been proposed and reached a sensitivity of 83.3% and specificity of 89.1% with an AUROC of 0.85 in distinguishing healthy patients from those with CLD (Gatos et al. 2016). Similarly, a machine-learning algorithm has been successfully employed in the quantification of stiffness values derived from SWE images and surpassed in accuracy previous studies and expert radiologists in the differentiation of healthy patients from CLD patients (sensitivity = 93.5%, specificity = 81.2% and AUROC = 0.87) (Gatos et al. 2017). The machine learning implementation of SWE analysis has been tested in other studies with satisfying results (Gatos et al. 2019; Durot et al. 2020).
In a previous study (Borro et al. 2018) our group highlighted that Genoa Line Quantification (GLQ) was a valid, low-cost and quick method to stage liver fibrosis. This technique evaluates capsular irregularity at the level of the anterior margin, which is induced by fibrosis progression and is currently the object of investigation from both imaging and histologic points of view (Pickhardt et al. 2016; Xu et al. 2016). Ultrasound evaluation of the liver surface was previously identified as an effective method for staging fibrosis, as it distinguishes cirrhosis from moderate and mild to absent fibrosis (reliable indicator of 0.9524). However, ultrasonography was performed by expert radiologists and, therefore, was an operator-dependent examination (Nishiura et al. 2005). The aim of the present study was to develop and evaluate the effectiveness of a new fibrosis staging method (namely, GLQ) based on computer evaluation of ultrasound images and enhancement with the CAD technique.
Section snippets
Patients and study design
The present pilot study evaluated the effectiveness of evaluation of Glisson's line in patients with CLD of different etiologies. Overall, 215 patients referred to the Gastroenterological Clinic of IRCCS Ospedale San Martino Policlinico from January to June 2019 were included. We collected the most recent liver transaminase results available (i.e., aspartate aminotransferase and alanine aminotransferase). All patients underwent hepatic ultrasound evaluation with different techniques: B-mode
Demographic and clinical characteristics of all patients
The present pilot study included 215 patients (92 women) affected by different CDLs: 53 (24.7%) had NAFLD, 44 hepatitis C virus (HCV), 25 exotoxic hepatitis, 19 autoimmune hepatitis, 11 hepatitis B virus (HBV), 6 primary biliary cholangitis and 37 liver transplant recipients. The outstanding 20 patients had other rare causes of liver dysfunction.
Table 1 summarizes the demographic, ultrasonographic and biochemical characteristics of all 149 patients whose Glisson's line images were used to train
Discussion
The aim of this study was to evaluate the effectiveness of Glisson's line imaging, analyzed with GLQ software, in staging hepatic fibrosis both as an alternative exam and as a complementary method to other fibrosis staging techniques.
In the present study, we determined the efficacy of GLQ in correctly staging hepatic fibrosis after training on Glisson's line images using VCTE and SWE stages as true reference values. GLQ was based on neural network detection and was trained on images from
References (51)
- et al.
An algorithm for the grading of activity in chronic hepatitis C
Hepatology
(1996) - et al.
Autoimmune liver disease, autoimmunity and liver transplantation
J Hepatol
(2014) - et al.
Ultrasonographic morphological diagnosis of chronic liver disease: 2-Dimensional shear wave elastography as an add-on test
Ultrasonography
(2020) - et al.
Transient elastography (FibroScan)
Gastroenterol Clin Biol
(2008) - et al.
A new multimodel machine learning framework to improve hepatic fibrosis grading using ultrasound elastography systems from different vendors
Ultrasound Med Biol
(2020) - et al.
A machine-learning algorithm toward color analysis for chronic liver disease classification, employing ultrasound shear wave elastography
Ultrasound Med Biol
(2017) - et al.
Comparison of sound touch elastography, shear wave elastography and vibration-controlled transient elastography in chronic liver disease assessment using liver biopsy as the “reference standard”
Ultrasound Med Biol
(2020) - et al.
Assessment of liver fibrosis with 2-D shear wave elastography in comparison to transient elastography and acoustic radiation force impulse imaging in patients with chronic liver disease
Ultrasound Med Biol
(2015) Grading and staging systems for inflammation and fibrosis in chronic liver diseases
J Hepatol
(2007)- et al.
Shear wave elastography: An accurate technique to stage liver fibrosis in chronic liver diseases
Diagn Interv Imaging
(2016)
Liver elastography: What it is, how it is done, and how it is interpreted
Radiologia
Points to be considered when using transient elastography for diagnosis of portal hypertension according to the Baveno's VI consensus
J Hepatol
Accuracy of transient elastography-FibroScan®, acoustic radiation force impulse (ARFI) imaging, the enhanced liver fibrosis (ELF) test, APRI, and the FIB-4 index compared with liver biopsy in patients with chronic hepatitis C
Clinics
Liver cirrhosis
Lancet
Costs, charges, and revenues for hospital diagnostic imaging procedures: Differences by modality and hospital characteristics
J Am Coll Radiol
Which are the cut-off values of 2D-shear wave elastography (2D-SWE) liver stiffness measurements predicting different stages of liver fibrosis, considering transient elastography (TE) as the reference method?
Eur J Radiol
Elastography for the diagnosis of severity of fibrosis in chronic liver disease: A meta-analysis of diagnostic accuracy
J Hepatol
Validity criteria for the diagnosis of fatty liver by M probe-based controlled attenuation parameter
J Hepatol
Advances in MRI Methodology
Int Rev Neurobiol
Quantification of ultrasound imaging in the staging of hepatic fibrosis
Panminerva Med
Meta-analysis: ARFI elastography versus transient elastography for the evaluation of liver fibrosis
Liver Int
Transient elastography for assessment of liver fibrosis and steatosis: An evidence-based analysis
Ont Health Technol Assess Ser
Clinical applications, limitations and future role of transient elastography in the management of liver disease
World J Gastrointest Pharmacol Ther
Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver
Radiology
Assessment report: Monitoring and documenting systemic and health effects of health reforms in Greece
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