NOVEL IMAGING AND SERUM BIOMARKERS FOR THE DIAGNOSIS OF LIVER FIBROSIS AND PORTAL HYPERTENSION

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES MEDICAL ACADEMY

Sigita Gelman

NOVEL IMAGING AND SERUM

BIOMARKERS FOR THE DIAGNOSIS

OF LIVER FIBROSIS AND

PORTAL HYPERTENSION

Doctoral Dissertation Medical and Health Sciences,

Medicine (M 001)

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The dissertation was prepared in the Medical Academy of Lithuanian University of Health Sciences during the period of 2014–2020.

Scientific Supervisor

Prof. Habil. Dr. Limas Kupčinskas (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001).

Consultant

Prof. Habil. Dr. Arūnas Lukoševičius (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering – T 001).

Dissertation is defended at the Medical Research Council of the Medical Academy of Lithuanian University of Health Sciences:

Chairperson

Assoc. Prof. Dr. Diana Žaliaduonytė (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001).

Members:

Prof. Dr. Rimantas Kėvalas (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001);

Prof. Dr. Ligita Jančorienė (Vilnius University, Medical and Health Sciences, Medicine – M 001);

Dr. Andrius Petrėnas (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering – T 001);

Dr. Povilas Ignatavičius (University Hospital Zurich, Medical and Health Sciences, Medicine – M 001).

Dissertation will be defended at the open session of the Medical Research Council of Lithuanian University of Health Sciences on the 18th_{of March,}

2020 at 2 p.m. in the Great Auditorium of the Hospital of Lithuanian University of Health Sciences Kauno klinikos.

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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS MEDICINOS AKADEMIJA

Sigita Gelman

NAUJŲ VAIZDINIŲ IR KRAUJO

BIOŽYMENŲ PAIEŠKA

KEPENŲ FIBROZĖS IR PORTINĖS

HIPERTENZIJOS DIAGNOSTIKAI

Daktaro disertacija Medicinos ir sveikatos mokslai,

medicina (M 001)

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Disertacija rengta Lietuvos sveikatos mokslų universitete Medicinos akade-mijoje 2014–2020 metais.

Mokslinis vadovas

prof. habil. dr. Limas Kupčinskas (Lietuvos sveikatos mokslų universi-tetas, medicinos ir sveikatos mokslai, medicina – M 001).

Konsultantas

prof. habil. dr. Arūnas Lukoševičius (Kauno technologijos universitetas, technologijos mokslai, elektros ir elektronikos inžinerija – T 001). Disertacija ginama Lietuvos sveikatos mokslų universiteto Medicinos akademijoje medicinos mokslo krypties taryboje:

Pirmininkas

doc. dr. Diana Žaliaduonytė (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001).

Nariai:

prof. dr. Rimantas Kėvalas (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001);

prof. dr. Ligita Jančorienė (Vilniaus universitetas, medicinos ir sveikatos mokslai, medicina – M 001);

dr. Andrius Petrėnas (Kauno technologijos universitetas, technologijos mokslai, elektros ir elektronikos inžinerija – T 001);

dr. Povilas Ignatavičius (Ciuricho universiteto ligoninė, medicinos ir sveikatos mokslai, medicina – M 001).

Disertacija ginama viešame Lietuvos sveikatos mokslų universiteto medici-nos mokslo krypties tarybos posėdyje 2020 m. kovo 18 d. 14 val. Lietuvos sveikatos mokslų universiteto ligoninės Kauno klinikų Didžiojoje audito-rijoje.

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ABBREVIATIONS

2D-SWE – real time shear wave elastography AAR – AST and ALT ratio

ALT – alanine aminotransferase APRI – AST to platelet ration index ARFI – acoustic radiation force imaging AST – aspartate aminotransferase AUC – area under the curve CLD – chronic liver disease CNS – central nervous system

CSPH – clinically significant portal hypertension ECs – endothelial cells

ELISA – enzyme-linked immunosorbent assay EV – esophageal varices

FAS-r – FAS receptor

FHVP – free hepatic venous pressure FIB-4 – fibrosis 4 score

GUCI – Goteborg University Cirrhosis Index HREV – high risk esophageal varices

HSC – hepatic stellate cell HSP-70 – heat shock protein 70

HVPG – hepatic venous pressure gradient IHVR – intrahepatic vascular resistance IL-1b – interleukin 1 beta

IL-1Ra – interleukin 1 receptor antagonist IQR – interquartile range

KC – Kupffer cells

LSECs – liver sinusoidal endothelial cells MEDL – Model of end stage liver disease NO – nitric oxide

NPV – negative predictive value PDGF – pigment derived growth factor PH – portal hypertension

PlGF – placental growth factor PPV – positive predictive value pSWE – point shear wave elastography RF – radiofrequency

ROC – receiver operating curve ROI – region of interest

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8 sCD163 – soluble CD163

SE – strain elastography

SEC – sinusoidal endothelial cell SPH – severe portal hypertension TE – transient elastography TNFb – tumor necrosis factor beta VCAM-1 – vascular cell adhesion molecule VEGF – vascular endothelial growth factor VWF – von Willebrand factor

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INTRODUCTION

Chronic liver disease (CLD) causes significant morbidity and mortality worldwide, with estimated 844 million people having CLD and the rate of mortality reaching 2 million deaths per year [1]. CLD are caused by various etiological factors, most common of which are hepatitis B and C virus, alcohol consumption and metabolic syndrome. Despite the different etiolo-gical factors and variable course, the main patholoetiolo-gical features of CLD are chronic inflammation, degeneration and necrosis of hepatocytes and accumulation of fibrotic tissue [2]. The progression of fibrosis further causes the disruption of hepatic architecture and the development of liver cirrhosis. This chronic progressive condition ultimately leads to the hepato-cellular dysfunction and portal hypertension, with subsequent complications (ascites, gastroesophageal varices, hepatic encephalopathy etc.), as well as the formation of hepatocellular carcinoma [3], significantly impairing the condition and prognosis of the patient [4].

Accurate assessment of liver fibrosis stage that a patient has developed throughout the course of the disease is essential for several reasons. It not only guides the selection of appropriate treatment strategy, but also helps to identify the patients with more advanced stages of liver disease who need close surveillance and timely evaluation of cirrhosis-related complications [2, 5, 6]. Also as fibrosis is believed to be a dynamic process, safe and accurate eva-luation of fibrosis status in different moments of time is equally important [7].

Liver biopsy is the golden standard for the evaluation of hepatic fibrosis and cirrhosis. This is an invasive procedure, poorly accepted by patients and with certain limitations [8], such as sampling error, inter- and intra-observer variability, risk of complications and death [3]. A number of non-invasive methods for the dynamic liver tissue evaluation have been proposed, in-cluding direct and indirect serum markers and panels [2, 9, 10] and imaging techniques, such as ultrasound elastography [11, 12]. Ultrasound elastogra-phy is one of the most widely studied imaging method for the diagnosis of liver fibrosis and cirrhosis due to its low cost, applicability and availability, allowing the clinician to acquire prompt results at the patient’s bedside [12, 13]. Several techniques have been proposed, including transient elasto-graphy (TE), shear wave elastoelasto-graphy (2D shear wave elastoelasto-graphy (2D SWE)), acoustic radiation force impulse imaging (ARFI) (point shear wave elastography (pSWE)) and strain elastography (SE) [10, 11, 13].

Portal hypertension (PH) is a consequence of liver cirrhosis and can cause serious life-threatening complications. The degree of portal hyperten-sion is one of the most important prognostic factors for complications and

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decompensation of liver cirrhosis and is defined by the hepatic venous pressure gradient (HVPG) [5, 14]. The presence of clinically significant portal hypertension (CSPH; HVPG≥10 mmHg) increases the risk for the formation of gastroesophageal varices [15], clinical decompensation [16] and hepatocellular carcinoma [17], whereas severe portal hypertension (SPH; HVPG≥12 mmHg) increases the risk for variceal bleeding and death [5, 18]. In order to optimize the care of patients with liver cirrhosis, it is essential to detect PH in its early stage and prevent the development of CSPH or treat already present CSPH to avoid decompensation [19, 20].

The golden standard for the diagnosis of CSPH and SPH is an invasive measurement of HVPG, however this procedure is currently reserved for specialized centers only, as it is costly, invasive and requires expertise [5, 21, 22]. Ultrasound elastography techniques, especially TE, have shown promi-sing results in diagnopromi-sing PH [21, 23]. As an alternative various serum biomarkers are being evaluated in the setting of PH, which represent not only structural changes of the liver tissue but also functional disorders of liver circulation, especially important in advanced stages of liver cirrhosis and PH.

The aim of the study

To evaluate the relevance of novel ultrasound-based imagining and serum molecular biomarkers for the non-invasive assessment of liver fibrosis and portal hypertension.

The objectives of the study

1. To evaluate the relevance of the radiofrequency ultrasound-based tissue strain imaging method for the non-invasive diagnosis of liver fibrosis.

2. To evaluate the relevance of the radiofrequency ultrasound-based tissue strain imaging method for the non-invasive diagnosis of portal hypertension.

3. To evaluate plasma levels of placental growth factor, Nogo-A protein, soluble CD163 molecule and Von Willebrand factor in patients with liver cirrhosis and portal hypertension.

4. To evaluate the diagnostic value of placental growth factor, Nogo-A protein, soluble CD163 molecule and Von Willebrand factor for the diagnosis of portal hypertension.

5. To evaluate the diagnostic value of placental growth factor, Nogo-A protein, soluble CD163 molecule and Von Willebrand factor for the detection of esophageal varices.

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Novelty of the study

With growing burden of chronic liver diseases worldwide [1], the opti-mization of care provided to the patients with chronic hepatitis and liver cirrhosis has become a priority. In CDL, due to continuous liver tissue damage, liver parenchyma undergoes wide spectrum of changes leading to liver fibrosis and finally to liver cirrhosis, which in turn causes the loss of liver function and development of portal hypertension and hepatocellular carcinoma [24–26]. In order to provide adequate treatment, to evaluate the risk and rate of CLD progression, decompensation or development of complications and to choose patient monitoring strategies, accurate assessment of liver fibrosis and the degree of PH is crucial [6, 27]. Until now the golden standard for the assessment of liver fibrosis and PH have been invasive techniques, thus the search for non-invasive accurate diagnostic alternative has been actively ongoing. Among the available non-invasive approaches ultrasound elastography represents the most widely used tool, however all available methods, including transient elastography and shear-wave elastography, are most sensitive in the setting of advanced fibrosis and are not able to discriminate intermediate stages of fibrosis [28]. In the setting of PH, transient elastography and 2D shear wave technique have shown high accuracy for the detection of CSPH [23]. Nevertheless the use of these methods is limited by the need of special equipment and insufficient accuracy in obese patients, ascites and acute inflammation [13]. It has been suggested that strain elastography is less affected by inflammation [29], obesity or ascites [30]. In search for a novel, more accurate non-invasive approach, we applied a radiofrequency ultrasound-based tissue strain imaging method and evaluated the diagnostic value of the endogenously induced strain on the liver tissue for the assessment of liver fibrosis and PH. The endogenous strain was estimated by a specifically developed radiofrequency (RF) signal analysis algorithm, allowing quantitative assessment of liver fibrosis and PH. This is a novel approach, which, to our best knowledge, has not yet been applied by other research groups.

Serum biomarkers, mainly fibrosis markers, have also been used to pre-dict CSPH and SPH [31]. In the development of PH, especially in advanced stages of liver cirrhosis, functional disorders of liver circulation, including angiogenesis, endothelial dysfunction and macrophage activation, are important pathogenetic factors [5]. As current treatment options for prevention and treatment of PH are limited to attenuating splanchnic vasodilatation, new insights on pathogenic mechanisms and molecules involved in the formation of PH, would aid in search for newer and more

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effective treatment strategies targeting different pathogenic components of PH [32]. In the second part of our study, we have chosen to test biomarkers of endothelial dysfunction (von Willebrand factor) and macrophage activation (soluble CD163), which showed strong predictive value for PH in previous studies [33–36] in order to replicate the results in our patient population. We have also decided to test biomarkers of angiogenesis (placental growth factor and Nogo-A), which have been poorly examined in the context of PH, in order to gain deeper understanding of molecules involved in the pathogenesis of PH. To our knowledge Nogo-A protein has never been examined in the setting of portal hypertension and only one study in humans has evaluated the levels of placental growth factor in patients with PH [37].

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1. REVIEW OF LITERATURE

1.1. Liver fibrosis and portal hypertension

The global burden of chronic liver disease is growing worldwide with the predicted exponential rise in incidence in the upcoming decades [38]. The cause of CLD varies regionally, but most common etiological factors include hepatitis C and B virus infection, excessive alcohol consumption and fatty liver disease. Irrespective of the etiology, repetitive and long lasting damage to liver tissue drives progressive fibrogenesis [24]. Liver fibrogenesis is characterized by the progressive accumulation of extracellular matrix, as a wound healing response driven by activated hepatic stellate cells and infiltrating immune cells, due to the hepatocyte damage and apoptosis [25, 39]. The degree of liver fibrosis is crucial for accurate diagnosis, assessment of prognosis and for the choice of therapeutic interventions in CLD. Furthermore, recent studies have shown that the stage of fibrosis closely correlates with liver related and overall mortality [40, 41]. Despite of multiple drawbacks, such as sampling error, inter- and intra-observer variability, procedural complications [8], histological determination of fibrosis stage via liver biopsy currently remains the golden standard. Out of several histological scoring systems, METAVIR system is widely used as a reference system for the non-invasive fibrosis tests in clinical trials [42] (Table 1.1.1).

Table 1.1.1. The METAVIR staging system [43]

F0 no fibrosis

F1 stellate enlargement of portal tracts without septum formation F2 enlargement of portal tracts with rare septum formation F3 numerous septa without cirrhosis

F4 cirrhosis

The progression of liver fibrosis ultimately leads to liver cirrhosis, characterized by structural changes of liver tissue, mainly formation of rege-nerative nodules of parenchyma surrounded by fibrotic septa [26], as well as functional abnormalities in liver vasculature, such as endothelial dysfunction [25]. Such structural and functional abnormalities further lead to the development of portal hypertension, which is responsible for all the clinically significant complications of liver cirrhosis, such as ascites, development of gastroesophageal varices and variceal bleeding, hepatic

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encephalopathy, hepatorenal and hepatopulmonary/portopulmonary syndro-mes [44]. Pathophysiology of PH is a complex process, involving a number of different mechanisms and molecules [45] (see Fig. 1.1.1). The primary factor in the development of PH is increased hepatic vascular resistance (IHVR). Historically IHVR has been attributed to the disruption of the architecture of liver parenchyma due to fibrosis and the formation of regenerative nodules [46]. In the past 20 years deeper understanding of liver circulation has demonstrated that the dynamic component accounts for approximately 30% of the increased IHVR [44, 47]. This dynamic compo-nent represents functional disturbances in the liver circulation caused by the increased production of vasoconstrictors and decreased production of vasodilatators [48] due to endothelial dysfunction [49] and activation of stellate cells [50]. Recent studies suggest that angiogenesis could also contribute to an increase in IHVR, causing splanchnic hyperemia, porto-systemic collateralization and pathologic angiogenesis inside the liver, worsening an already existing PH [32, 51, 52].

In clinical practice PH is characterized by hepatic venous pressure gra-dient (HVPG), which indicates the degree of PH. Nowadays HVPG is used to predict liver-related outcomes, as well as for risk stratification, identifica-tion of surgery candidates for hepatocellular carcinoma, monitoring the treatment efficacy and progression of PH [53]. The normal HVPG is 1– 5 mmHg and values above 6 mmHg are considered to indicate PH, however values not reaching 10 mmHg are not associated with complications or reduced survival [54]. As the values cross the 10 mmHg threshold, clinically significant portal hypertension is diagnosed, which is associated with the increased risk of gastroesophageal varices [15], clinical decompensation [16] and hepatocellular carcinoma [17]. HVPG above 12 mmHg is consi-dered to be severe portal hypertension, which increases the risk of variceal bleeding, ascites and death [5, 18] (Table 1.1.2). The golden standard for the assessment of HVPG is direct HVPG measurement (Fig. 1.1.2). This is an invasive procedure and is usually reserved for specialized centers only.

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F ig. 1.1.1. P at hophy si ol ogy of por tal hy pe rt ens ion In cr eas ed in trah ep at ic v as cu la r r es ist an ce d ue t o ar ch itect ur al d ist ur ban ces a nd to in cr eas ed h ep at ic v as cu lar to ne l ead s t o t he d ev el op m en t o f po rta l h yp er te ns io n. S pla nc hn ic a rte rio la r v as od ila tio n to ge th er w ith f un ctio na l c ha ng es in s pla nc hn ic c irc ula tio n a nd a ng io ge ne sis f ur the r ag gr av at es p or tal h yp er ten sio n (G ar ci a-Pagan et al .) [4 4] . A bb re via tio ns : N O – n itr ic o xid e; S EC – s in us oid al en do th elia l c ell s; H SC – he pa tic ste lla te c el ls; E T1 – e nd ot he lin 1 . 15

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Table 1.1.2. Correlation between hepatic venous pressure gradient and

clinical outcomes (Procopet et al.) [55]

HVPG Clinical outcome

<5 mmHg

5–10 mmHg >6 mmHg

Normal

Mild portal hypertension

Progression of chronic viral hepatitis

High risk of recurrence after liver transplantation

>10 mmHg

>10 mmHg Clinically significant portal hypertension Esophageal varices development Ascites

Decompensation

Hepatocellular carcinoma occurrence Decompensation after hepatic resection

>12 mmHg

>16 mmHg >20 mmH >22 mmHg

Severe portal hypertension

Variceal bleeding High mortality

Failure to control bleeding

High mortality in severe alcoholic hepatitis Abbreviations: HVPG – hepatic venous pressure gradient.

Fig. 1.1.2. Method for the measurement of hepatic venous pressure gradient

A – A catheter is introduced into the hepatic vein; B – Wedged hepatic venous pressure (WHVP) is measured by occluding the hepatic vein; C – Free hepatic venous pressure (FHVP) is measured by maintaining the tip of the catheter “free” in the hepatic vein; D – HVPG is the difference between WHVP and FHVP (Suk et al. [53], Abraldes et al. [56]).

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1.2. Non-invasive assessment of liver fibrosis and portal hypertension Limitations of liver biopsy and HVPG measurement have driven the active search for the non-invasive alternatives. In the recent years, increa-sing numbers of studies have focused on the development of non-invasive methods for the diagnosis and staging of fibrosis as well as for the diagnosis of CSPH and SPH. There is also a growing need for an accurate screening test to detect or predict variceal complications, making it easier to select patients, which may benefit from primary prophylaxis, thus reducing complications and healthcare burden from frequent endoscopy [57]. These methods can be divided into two groups: imaging modalities and serum markers.

1.2.1. Imaging modalities for non-invasive diagnosis of liver fibrosis and portal hypertension

Various imaging modalities have been used for the diagnosis of liver fibrosis and PH, including conventional, Doppler and contrast-enhanced ultrasound [3, 22], magnetic resonance elastography [58, 59] and computer tomography [60, 61]. Ultrasound elastography is the leading method in this field due to its low cost, applicability and availability, allowing the clinician to acquire prompt results at the patient’s bedside [11, 23]. Several ultra-sound elastography techniques have been developed: strain imaging (using internal/ external compression stimuli), point shear wave elastography (using acoustic radiation force impulse (pSWE)), real-time shear wave imaging (2D-SWE) and transient elastography (TE, Fibroscan, Echosens). Out of existing techniques TE was the first technique to be applied for the diagnosis of liver fibrosis, and now it is the most widely used modality [19,62]. It has shown high accuracy in liver cirrhosis, CSPH and SPH (area under the curve AUC 0.97 [63], 0.90 [64] and 0.92 [62] respectively). A recent study by Zykus et al. [65] of Lithuanian population showed similar results with AUC for CSPH being 0.94 and for SPH 0.91. pSWE and 2D-SWE are considered to be alternative elastography modalities, comparable to TE, in the diagnosis of liver fibrosis, CSPH and SPH [66]. The diagnostic performance of ultrasound elastography in the diagnosis of fibrosis and PH are presented in Table 1.2.1.1.

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Table 1.2.1.1. Diagnostic performance of ultrasound elastography in the

diagnosis of liver fibrosis and portal hypertension

Utrasound elastography technique Fibrosis F≥2 Cirrhosis F=4 CSPH SPH EV TE AUC Sensitivity Specificity 0.89 [63] 80% 84% 0.97 [63] 94% 93% 0.90 [64] 87% 85% 0.91[65] 82% 80% 0.84 [72] 87% 53% pSWE AUC Sensitivity Specificity 0.82 [67] 74% 81% 0.84 [67] 84% 76% 0.85 [69] 71% 87% 0.88 [71] 74% 83% 0.73 [69] 2D-SWE AUC Sensitivity Specificity 0.89 [68] 83% 82% 0.92 [68] 85% 83% 0.81 [70] 85% 80% 0.86 [70] 83% 80% – Abbreviations: AUC – area under the curve; TE – transient elastography; pSWE – point shear wave elastography; 2D-SWE – real-time shear wave elastography; EV – esophageal varices.

Ultrasound elastography techniques mentioned above have good diag-nostic performance and reproducibility, but their applicability is limited in certain patient groups. TE performance is not optimal in patients with obe-sity, ascites, intercostal space stenosis, whereas readings for all elastography techniques are not accurate in case of active inflammation, cholestasis, fatty liver and biliary obstruction [73, 74]. Also TE, pSWE and 2D-SWE are reliable in ruling out advanced fibrosis, but their performance in interme-diate fibrosis stages is not so accurate [75].

Strain elastography (SE) is another ultrasound elastography technique widely used for the examination of musculoskeletal system [76], breast and thyroid pathologies [77], however it is the least evaluated technique in the setting of liver diseases. Diagnostic performance of strain elastography is considered to be less affected by the inflammation fluctuations [29] and can be successfully performed in case of obesity and ascites [30], therefore it might be an attractive alternative in specific subgroups of patients.

1.2.2. Strain elastography in liver fibrosis and portal hypertension Strain elastography was the first ultrasound elastography modality introduced in 1970 that is now available in most commercial ultrasound systems and is being used in different clinical settings [78]. SE is a technique that measures axial displacement of tissue caused by manual

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compression or physiological shifts inside the patient’s body (cardiovascular pulsatily or breathing) [79, 80]. This technique allows detecting tissue strain in real time, and the two images, the strain image and conventional gray scale ultrasound image, are displayed side-by-side or fused in one. This allows to identify the target area and to draw a region of interest (ROI) for further analysis [81]. Before and after the application of the deformation stimulus, axial displacements between sequentially acquired ultrasound images are calculated using either radiofrequency echo correlation tracking or Doppler processing [82]. The resulting deformation image is displayed as a color coded elastogram: areas with lower strain are displayed in blue and areas with higher strain in red [62, 79, 83, 84]. We applied a novel strain elastography technique in our study, which is based on the same principles of SE and assesses the strain of liver tissue caused by endogenous motion of the beating heart as an excitation source. The main difference from the traditional SE technique is that B-mode images are recorded and stored for further offline analysis, when ROI is chosen and a specific radiofrequency signal analysis algorithm is used to calculate the parameters for quantification of fibrosis and detection of PH.

The drawback of SE is that the resulting elastogram is not quantifiable [80]. In order to obtain more accurate and reproducible results various quantitative methods have been developed to objectively assess the elasticity of tissue [79, 82, 83]. Some research groups, similarly as ours, have developed and applied specific algorithms for quantitative evaluation of elasticity using a number of statistical parameters, derived from the distribution of recorded strains within a ROI [85–90].

Despite these limitations SE has several advantages over other ultrasound elastography modalities. Firstly, it allows to take into account the inhomogeneity of the distribution of the strain in larger areas of the liver [73, 81]. Also, what is more important, SE readings are not affected by inflammation, jaundice, liver congestion, fatty degeneration, obesity, ascites or restricted intercostal spaces as is the case with other elastography moda-lities [29, 79, 84, 91].

Traditional SE has been previously used for the evaluation of liver fibrosis and detection of PH. Friedrich-Rust et al. [89] were the first group of researchers, who published the results on SE in liver diseases. The authors reported AUC of 0.75 for the diagnosis of significant fibrosis (F≥F2), 0.73 for the diagnosis of severe fibrosis (F≥F3), and 0.69 for the diagnosis of cirrhosis (F=F4). Initially, freehand compression was used for tissue deformation, but, since 2010, pioneered by Tatsumi et al. [92], the displacement of tissue has been induced by the heartbeat. A number of studies have been published evaluating the performance of SE in

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invasive diagnosis of liver fibrosis. The AUC for the diagnosis of significant fibrosis (F≥F2) ranged from 0.64 to 0.95 and for the diagnosis of cirrhosis (F=F4) from 0.63 to 0.95 [93–100]. A recent meta-analysis by Kobayashi et

al. [101] reported the summary receiver operating characteristic curves

(SROCs) for F≥2, F≥3, and F≥4 as 0.69, 0.86, and 0.72, respectively and concluded that SE was nearly identical to TE and p-SWE for the diagnosis of significant fibrosis (F≥2), but less accurate for the diagnosis of cirrhosis (F≥4). Studies concerning SE performance in PH are scarce. Ochi et al. [99] reported SE to be a useful tool for the diagnosis of liver fibrosis and PH in patients with non-alcoholic liver disease. Hirooka et al. [102] evaluated SE performance in CSPH and SPH using elastic ratio, reporting AUC for the diagnosis of CSPH of 0.83 and for the diagnosis of SPH – 0.78. Spleen elasticity performed better in diagnosing CSPH (AUC 0.97) and SPH (AUC 0.94).

Only a few studies have used radiofrequency ultrasound-based tissue strain imaging method to assess the strain in liver tissue. Ling et al. [103] used similar to ours technique of radiofrequency signal analysis to grade liver steatosis in animal models. Liao et al. [104] also used ultrasound radiofrequency data analysis for the classification of non-alcoholic fatty liver disease. Our study group has previously reported the ability of radiofrequency ultrasound-based tissue strain imaging method to charac-terize tissue elasticity in vitro [105]. To our best knowledge no studies have evaluated RF based strain elastography in liver fibrosis and portal hyper-tension.

1.2.3. Serum biomarkers for non-invasive diagnosis of portal hypertension

Serum biomarkers are an attractive alternative for the non-invasive diagnosis of liver fibrosis and PH. They are easy to use, offer representation of the whole liver and have a small sampling error, assays can be performed in a routine laboratory setting, making them cost-effective [106]. Also there is a limited observer-related variability and the possibility of repeated measurements [106]. Various circulating markers and their panels have been extensively evaluated.

Serum biomarkers for liver fibrosis can be divided into two large groups: direct and indirect markers. The group of direct markers represents products, derived from extracellular matrix production and degradation. Indirect markers do not directly represent liver injury, but are markers of inflammation and impaired liver function [107–109]. Examples of biomarkers representing both groups are summarized in Table 1.2.3.1 [110].

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Due to the poor accuracy of individual markers, algorithms or panels of markers have been developed and widely validated, with varied diagnostic accuracy (Table 1.2.3.2).

Table 1.2.3.1. Summary of serum biomarkers for the diagnosis of liver

fibrosis

Direct serum

markers/panels markers/panels Indirect serum Patented serum panels

Hyaluronate Laminin YKL-40

Procollagen type I carboxy-terminal peptide (PICP)

Procollagen type III amino-terminal peptide (PIIINP)

Metalloproteinases (MMP)-1 and MMP-2

Tissue inhibitors of the metallopro-teinases (TIMPs)

MP3

Transforming growth factor-β1 (TGF-β1) Microfibril-associated glycoprotein 4 (MFAP-4) AST/ALT ratio PGA APRI Forns index FIB-4 Lok index

Fibrosis probability index (FPI)

Goteborg University Cirrhosis Index (GUCI)

Virahep-C model SHASTA index BAAT

NAFLD fibrosis score BARD Fibrotest Fibroindex Hepascore Fibrospect Enhanced Liver Fibrosis score (ELF) Fibrometers

Abbreviations: AST – aspartate transaminase; ALT – alanine transaminase; PGA – prothrombin time, gamma-glutamyl transpeptidase, apolipoprotein A1; YKL – 40-chitinase-3-like-1; FIB-4 – fibrosis-4 index; APRI – aspartate transaminase to platelet ratio index; NAFLD – nonalcoholic fatty liver disease.

Table 1.2.3.2. Performance of biomarker panels for non-invasive diagnosis

of significant fibrosis and cirrhosis (Caviglia et al.) [75]

Test Parameters F≥2

(AUC) (AUC) F=4

APRI AST (/ULn)/platelet (109_{/L) × 100} _0.69–0.88_0.61–0.94

FibroTest® Patented score combining age, gender,

α2-macroglobulin, γ-GT, apolipoprotein A1, haptoglobin, bilirubin

0.74–0.89 0.82–0.92

Forns index 7.811–3.131 × ln(platelet count) + 0.781 ×

ln(GGT) + 3.467 × ln(age) – 0.014 × (cholesterol) 0.75–0.91 0.87 Hepascore® Patented score combining age, gender, bilirubin,

γ-GT, HA, α2-macroglobulin 0.76–0.81 0.88–0.90 ELF® Patented score combining age, Ha, n-terminal

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Table 1.2.3.2. Continued

Test Parameters F≥2

(AUC) (AUC) F=4

FIB-4 FIB-4 = age (yr) × AST [U/L]/(platelets [109_{/L] ×}

(ALT [U/L])1/2 0.78 0.76–0.84

NFS (–1.675 + 0.037 × age [yr]) + (0.094 × BMI [kg/m2_]

+ (1.13 × IFG/diabetes [yes=1, no=0]) + 0.99 × AST/ALT ratio –0.013 × platelet count (×109_{/L) –}

0.66 × albumin [g/dL])

0.67 0.82

Abbreviations: γ-GT – gamma-glutamyltransferase; ALT – alanine aminotransferase: AST – aspartate aminotransferase; AUC – area under the curve; BMI – Body Mass Index; HA – hyaluronic acid; iFG – impaired fasting glucose; TimP-1 – tissue inhibitor of metallo-proteinase-1; NFS – non-alcoholic fatty liver disease fibrosis score

Since the main pathophysiological pathway of portal hypertension is increased intrahepatic resistance due to tissue fibrosis, the same fibrosis serum biomarkers have been evaluated for the non-invasive diagnosis of portal hypertension, however their performance is not optimal. Some authors attempted to correlate PH or the risk of variceal bleeding with the serum levels of direct biomarkers such as laminin, type III procollagen, type IV collagen and hyaluronic acid but their accuracy did not exceed AUC 0.7 [31, 111, 112]. Kropf et al. [113] reported higher AUC of 0.82 for the combination test of hyaluronic acid and laminin, whereas Leeming et al. [114] reported good performance of extracellular matrix proteins (AUC 0.92). Indirect serum markers and panels have also been evaluated for the diagnosis of PH and gastroesophageal varices with varying results. APRI, AAR, FIB-4, Lok, GUCI and Forns scores were tested as non-invasive markers of PH and EV in numerous studies [21, 34, 115–123], however none of the biomarkers proved to be superior to others. A meta-analysis by

Deng et al. [124] showed that serum biomarkers did not provide sufficient

accuracy or predictive values for the diagnosis of EV. Summary of the diagnostic performance of serological scores for the diagnosis of CSPH and EV are presented in Table 1.2.3.3.

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Table 1.2.3.3. Summary of diagnostic performance of serum biomarker panels

Score CSPH (AUC) EV (AUC)

APRI 0.62–0.76 [115, 121] 0.57–0.69 [115, 116, 119] Lok 0.84 [115] 0.7–0.86 [115, 116, 119] FIB-4 0.79 [115] 0.63–0.80 [115, 116, 120] GUCI 0.73 [115] 0.75 [115] Risk Score 0.80 [115] 0.84 [115] Forns 0.65 [121] 0.66–0.75 [116, 119] Fibroindex 0.79 [122] 0.65 [116] AAR 0.50 [121] 0.64–0.72 [116, 124] VITRO 0.86 [34] 0.84 [123] ELF 0.68 [34] Fibrotest 0.79 [21] 0.77 [21]

Abbreviations: AUC – arean under the curve; APRI – aspartate transaminase to platelet ratio index; GUCI – Goteborg University Cirrhosis Index; FIB-4 – fibrosis-4 index; AAR – aspartate aminotransferase-to-alanine aminotransferase ratio; VITRO – Von Willebrand Factor Antigen/Thrombocyte Ratio; ELF – enhanced liver fibrosis; CSPH – clinically signi-ficant portal hypertension; EV – esophageal varices.

Attempts have been made to increase the diagnostic performance of non-invasive markers by combining several serum and imaging markers into scores or algorithms. Stefanescu et al. [125] evaluated liver stiffness – spleen size – to – platelet ratio score (LSPS) and reported an AUC of 0.8 for high risk EV (HREV), but a low rate of spared endoscopy. In a large multinational cohort of patients with compensated cirrhosis Abraldes et al. [126] evaluated the risk prediction models based on non-invasive tests and concluded that LSPS showed the highest discriminative value for predicting HREV, with an AUC of 0.79. Berzigotti et al. [127] developed the esophageal varices risk score (EVRS) and demonstrated good accuracy for the diagnosis of CSPH and prediction of EV (AUC 0.90 and 0.93 respectively). A recent systematic review by Colli et al. [128] including 71 studies reported that platelet count/spleen diameter ratio (PSR) could not identify HREV with sufficient accuracy. TE combined with platelet count showed highest accuracy in ruling out EV as reported in a recent meta-analysis by Marot et al. [129]. An important advance in clinical practice was that the combination of TE<20 kPa with platelets >150 g/L was included in the recent Baveno VI [19] consensus meeting to detect EV. However the specificity of this combination score is low and further validation is still needed, as well as more specific and accurate tests [130].

As discussed above the pathophysiology of PH is a complex process, which involves not only structural component but other important pathophy-siological mechanisms representing changes in hepatic and extrahepatic

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circulation. Therefore it is suggested that biochemical changes associated with this dynamic/functional component of PH pathogenesis may provide useful information for the diagnosis and monitoring of PH [111].

1.3. Serum biomarkers of dynamic/functional component of PH Generally the dynamic component of PH pathogenesis is mediated by the increased production of vasoconstrictors and reduced production of vasodilatators [5]. Several overlapping pathophysiological mechanisms are responsible for this dynamic component of PH: endothelial dysfunction, vascular injury, immune activation, increased vasoconstriction and patho-logic angiogenesis [5, 47, 111, 131]. A number of molecules included in the above mentioned pathophysiological processes have been evaluated as potential biomarkers for PH (Table 1.3.1). However none of these non-invasive tests have shown optimal performance for the diagnosis of PH.

Table 1.3.1. Summary of biomarkers representing dynamic/functional

component of portal hypertension

Pathophysiological

mechanism Biomarker

Endothelial

dysfunction • Von Willebrand Antigen (La Mura et al. [132], Ferlitsch et al. [33]) • Von Willebrand factor Antigen/platelet ratio – VITRO score (Hametner et al. [34])

• Asymmetric dimethylarginine (ADMA) (Vizzutti et al. [133]) • Dimethylargininedimethylaminohydrolase1 (DDAH-1) (Mookerje

et al. [134])

• Apelin (Lim et al. [135])

Vascular injury • Circulating endothelial cells (Abdelmoneim et al. [136]) Immune activation • Soluble CD163 (Gørnbaek et al. [35], Waidmann et al. [137],

Yang et al. [138])

• IL-1b, IL-1Ra, Fas-R, VCAM-1, TNFb, and HSP-70 (Buck et al. [139])

Increased

vasoconstriction •• Urotensin – II (Pawar et al. [141], Giulio et al. [142]) Endothelin – 1 (Hasegawa et al. [140]) Pathologic

angiogenesis • Vascular endothelial growth factor (VEGF) (Fernandez et al. [143], Huang et al. [144]) • Soluble vascular adhesion molecule (sVCAM-1) (Diaz-Sanchez et

al. [145])

• Placental growth factor (PlGF) (Van Steenkiste et al. [146])

Abbreviations: IL-1b: interleukin 1 beta; IL-1Ra: interleukin 1 receptor antagonist; Fas-R: Fas receptor; VCAM-1: vascular cell adhesion protein 1; TNFb: tumor necrosis factor beta; HSP-70: heat shock protein 70.

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1.3.1. Pathological angiogenesis in portal hypertension

Angiogenesis is a process causing new vessel formation from the pre-existing blood vessels, by sprouting or intussusception. Angiogenesis occurs in various organs and is associated with tissue damage, wound healing and remodeling. In liver cirrhosis angiogenesis is stimulated by tissue hypoxia and has been referred to as pathological angiogenesis, where vascular remo-deling within the hepatic sinusoids is driven by impaired function of hepatic stellate cells (HSC) and sinusoidal endothelial cells (SEC) [147].

Recent studies suggest that pathological angiogenesis is not only promoting liver fibrogenesis, but also contributes to an increase in IHVR, causing splanchnic hyperemia, portosystemic collateralization and pathological angiogenesis inside the liver, worsening an already existing PH [32, 52, 148].

The process of angiogenesis is dynamic and the balance between proan-giogenic mediators and endogenous inhibitors of angiogenesis is very important. Pathological angiogenesis can result from the up regulation of proangiogenic mediators as well as from the simultaneous down regulation of angiogenesis inhibitors, thus both stimuli are important and contribute to this pathophysiological process [149].

1.3.1.1. Placental growth factor as proangiogenic factor

Vascular endothelial growth factor (VEGF) is a well known major proangiogenic mediator, which promotes vascular growth and remodeling [149, 150]. VEGF was shown to be overexpressed in splanchnic organs in animals with portal hypertension and is believed to be associated with the pathogenesis of PH [151]. Number of animal studies [152–154] and two small human studies [155, 156] have demonstrated the reduction in portal pressure when blocking VEGF and VEGF/PDGF signaling. However, as angiogenesis is essential for tissue healing and regeneration, such treatment options are limited due to the side effects on other tissues [32]. In search for a safer treatment option a new target for research – placental growth factor (PlGF), which is a member of the VEGF family – was introduced.

Originally PlGF was discovered in human placenta in 1991 and is believed to be a multitasking cytokine affecting various cellular activities. In liver disease the most important activity of this cytokine is the stimulation of vessel growth and maturation. PlGF enhances the proliferation, migra-tion, and survival of endothelial cells, stimulates the proliferation of mesen-chymal fibroblasts and regulates the contractile response of mural cells during collateral vessel growth. However, differently from VEGF, PlGF is

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believed to enhance angiogenesis only in pathological conditions, not affecting the growth of healthy tissues [157–160].

PlGF has been examined in the context of various diseases and has been associated with tumorogenesis, atherosclerosis and ischemic heart disease, hematologic malignancies, limb and ocular ischemia, rheumatoid arthritis, pulmonary hypertension [159, 161–163]. Data on the role of PlGF in liver disease is scarce. The expression and function of the protein has been addressed in several animal studies, showing that PlGF plays an important role in liver inflammation, fibrogenesis and pathologic angiogenesis as well as PH [37, 146, 164, 165], however the association of PlGF with liver cirrhosis, PH and it’s complications in humans has been poorly examined.

1.3.1.2. Nogo-A protein as angiogenesis inhibitor

Angiogenesis is a two way process including activation and inhibition of angiogenesis stimuli, therefore several endogenous angiogenesis inhibitors have been identified, especially in tumorogenesis [166]. Some of them, such as Pigment epithelium derived factor and Vasohibin-1, have been found to take part in the pathogenesis of PH [167]. The identification of new angioge-nesis inhibitors might improve the understanding of these complex mecha-nisms.

Recent research discovered Nogo-A and Nogo-B isoforms of reticulon 4 protein family as possible angiogenesis mediators. Nogo-A protein is a well known potent neurite growth inhibitor but data on its expression and functions outside the central nervous system (CNS) is limited. Studies have reported the expression of Nogo-A in cardiomyocytes [168], enteric nervous system [169] and its roles in ocular diseases [170] and hepatocellular carcinoma pathogenesis [171]. Nogo-A has also been reported to be a negative regulator of retinal and CNS [172, 173] angiogenesis, but the effects on angiogenesis in other tissues have not been studied. Nogo-B protein, a splice variant of Nogo-A, on the other hand is expressed in various tissues and has been associated with liver fibrogenesis and liver cirrhosis [174, 175], angiogenesis [176, 177] and endogenous tissue repair [178]. Ramo et al. [179] have recently reported that Nogo-B and Nogo-A proteins are expressed simultaneously in fibroblasts and endothelial cells. Furthermore Dana et al. [180] have shown that both isoforms have the same large N-terminal region, exposed to the surface of the cells. Therefore Nogo-A protein, similarly to Nogo-B, might be associated with liver cirrhosis and the pathogenesis of PH, however no studies have evaluated Nogo-A protein in this context.

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1.3.2. Endothelial dysfunction in portal hypertension

Healthy endothelium is responsible for the expression of vasodilator and vasocontrictor agents in response to changes in blood volume and pressure, preventing or attenuating the increased intravascular pressure. In pathological conditions endothelium-dependent vasodilatation becomes impaired, causing a condition called “endothelial dysfunction” [52]. Endothelial dysfunction has been observed in hepatic vascular bed of cirrhotic livers. The condition is associated with reduced nitric oxide (NO) bioavailability and increased levels of vasoconstrictor cyclooxygenase-1 (COX-1) derived prostanoids, due to impaired function of the endothelial cells (ECs). 98% of hepatic ECs are liver sinusoidal ECs (LSECs), thus, the endothelial dysfunction observed in the intrahepatic circulation is apparently due to the dysfunction of LSECs [49, 181, 182].

Both hepatic and non-hepatic endothelial cells causing endothelial dysfunction are important in the pathogenesis of portal hypertension. Hepatic ECs contribute to an increased intrahepatic resistance mainly by decreasing nitric oxide (NO) production as mentioned above, which in turn initiates portal hypertension. On the other hand portal hypertension causes arterial vasodilatation and collateral vessel formation in the extrahepatic vascular beds of the splanchnic and systemic circulation, leading to further exacerbation of PH [49, 183, 184].

1.3.2.1. Von Willebrand factor in portal hypertension

Von Willebrand factor (VWF) is a large, highly adhesive, multimeric glycoprotein which is found in plasma and produced in endothelial cells and megakaryocytes [185]. Newly synthesized VWF is either released directly into plasma or stored in the Weibel–Palade bodies as ultra-large VWF mole-cules [185]. Experimental models have revealed that Weibel–Palade bodies in activated endothelial cells are the primary source for releasing ultra-large VWF, therefore it has been suggested that VWF reflects endothelial damage [186]. Also the shear forces caused by the increased intrahepatic pressure in liver cirrhosis and PH drive increased inflammatory response and endothelial damage and further stimulate the release of VWF multimers [187].

Apart from its role in hemostasis and clot formation, VWF has been associated with negative regulation of angiogenesis, stimulation of smooth muscle cell proliferation, platelet and tumor cell apoptosis, inflammatory processes and physiological bone remodeling [188]. In liver disease VWF has been shown to play an active role in regulating vascular proliferation, liver injury and repair [189], being not only an indicator of endothelial dysfunction and angiogenesis but also contributing to the pathogenesis of

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liver cirrhosis and PH. Several authors have reported increased VWF levels in patients with liver cirrhosis [190–194] and the ability of VWF levels to predict the development of hepatic decompensation and mortality [33, 132, 195, 196]. Moreover previous studies have evaluated VWF as a non-invasive marker of PH, showing that VWF positively correlated with HVPG and could predict CSPH and SPH [197, 198]. In a recent meta-analysis Ding

et al. [189] reported the summary AUC of 0.88 when differentiating patients

with and without PH. Mandorfer et al. [199] found that VWF levels predicted the development of complications and mortality independently of HVPG. All this data suggests that VWF levels could serve as a surrogate marker of portal hypertension, however more studies would aid in better understanding the role of VWF in selected patient groups.

1.3.3. Inflammation and portal hypertension

Liver cirrhosis with consequently developing portal hypertension has been associated with systemic inflammation that is reflected by the increase in pro-inflammatory cytokines, markers of macrophage activation and syste-mic oxidative stress [200–202]. PH has also been observed in the absence of liver fibrosis in fulminant acute liver failure and acute viral hepatitis due to liver injury and inflammation [203, 204]. This suggests that inflammation is an important factor for the development of portal hypertension. Moreover SPH has been reported in patients with chronic alcoholic liver disease without cirrhosis, suggesting that hepatocyte injury and inflammation play a role in the development of PH [205].

There are two main mechanisms of systemic inflammation in cirrhosis: 1) the translocation of viable bacteria and/or their products from intestinal lumen into intestinal mucosa [206] and 2) inflammation derived from acute hepatic inflammatory processes, caused by the spread of immune cells acti-vated within the liver and released by dying hepatocytes [200]. One way to assess the inflammatory status in portal hypertension is to evaluate the levels of these immune cells and their products. Buck et al. [139] reported that IL-1b, IL-1Ra, Fas-R, VCAM-1, TNFb, and HSP-70 significantly corre-lated with HVPG in compensated cirrhosis. On the other hand Kupffer cells (KC), macrophages residing in the liver, have also been associated with the innate immune system and production of various cytokines, which may be associated with PH [207].

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1.3.3.1. Soluble CD163 molecule in portal hypertension

Kupffer cells are located in liver sinusoids constituting about 10–15% of the total liver cell population and the majority of the body’s fixed macro-phages. These cells are activated as part of the innate immune response to acute hepatitis, acute liver failure, and alcoholic liver disease. Activated KC are involved in the pathogenesis of liver cirrhosis via production of cytokines and growth factors and seem to play an important role in the dynamic inflammatory component of PH [208–210].

The function of activated KC can be assessed by measuring soluble CD163 (sCD163). CD163 is a scavenger receptor that is expressed on monocytes as well as macrophages and is upregulated by macrophage proliferation and activation [211]. Soluble CD163 has been evaluated as a biomarker of various diseases, including Gaucher disease, chronic inflame-matory conditions, cancer, diabetes and atherosclerosis [212]. As liver KC comprise a major part of innate immune system, sCD163 has been evaluated in liver diseases as well. Studies have reported increased sCD163 levels in patients with viral hepatitis and fulminant liver failure [213]. SCD163 has been associated with non-alcoholic fatty liver disease [214], inflammation and fibrosis in chronic hepatitis [215, 216], hepatocellular carcinoma [217] and allograft dysfunction after liver transplantation [218]. The role of KC activation and sCD163 has also been evaluated in liver cirrhosis and PH. Several researchers reported higher sCD163 levels in patients with liver cirrhosis and significant linear correlation with HVPG [35, 219]. SCD163 alone and in combination with other non-invasive tests predicted PH, esophageal varices, complications and overall survival [137, 138, 210].

The determination of soluble CD163 might be used as a biomarker for non-invasive assessment of PH as well as for patient monitoring and predic-tion of EV.

The need for non-invasive diagnostic tests for the prediction of PH and EV is growing. As the pathogenesis of PH is very complex, all data on the mechanisms involved provide deeper understanding and future perspectives in diagnosis and treatment of liver cirrhosis and PH. This research is dedica-ted to the exploration of new non-invasive imaging methods and deeper knowledge of molecules involved in the pathogenesis of PH.

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2. METHODS

2.1. Ethics

The clinical study was approved by Kaunas Regional Biomedical Research Ethics Committee (2015-08-24, No. BE-2-28, Kaunas, Lithuania). Every parti-cipant has given a written informed consent to participate in the study.

2.2. Design of the study

The study has three parts: 1) evaluation of radiofrequency ultrasound-based tissue strain imaging method in liver fibrosis; 2) evaluation of radio-frequency ultrasound-based tissue strain imaging method in portal hyper-tension; 3) evaluation of serum biomarkers in portal hypertension. The first and second parts of the study were conducted in collaboration with the scientists from the Biomedical Engineering Institute, Kaunas University of Technology.

2.2.1. Patient selection

Patients included in the study were referred for the scheduled liver biopsy and/or HVPG measurement in the Department of Gastroenterology, Lithuanian University of Health Sciences in the period of September 2015 to December 2017. Healthy subjects were selected for the control group.

The first part of the study included patients with hepatitis C virus infection who underwent a scheduled liver biopsy procedure. The second and third parts of the study included patients with hepatitis C virus and/or alcohol-induced liver cirrhosis who underwent a scheduled HVPG measurement. Viral hepatitis C was diagnosed by serology and histology. Liver cirrhosis was diagnosed according to clinical, laboratory and radio-graphic data and/or histology. Alcohol-induced liver cirrhosis was diagno-sed in patients without other obvious causes for liver disease and alcohol intake greater than 30 g/d in males and greater than 20 g/d in females. Control group consisted of healthy volunteers, matched for age and sex.

All the patients and healthy controls, who met the inclusion criteria and agreed to participate in the study, were evaluated for the presence of the exclusion criteria.

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1. Abnormal laboratory tests suggesting liver or kidney disease, diabetes, active infection;

2. Findings in abdominal ultrasound suggesting liver or kidney disease or any abnormal findings in liver, spleen, kidneys;

3. History of cardiovascular, kidney disease, diabetes, neurodegene-rative diseases, any location cancer;

4. Abnormal reading of transient elastography of the liver. Exclusion criteria for the study population:

1. Cardiovascular disease; 2. Kidney disease;

3. Diabetes;

4. Neurodegenerative diseases; 5. Active infection;

6. Pre- or posthepatic causes of portal hypertension; 7. Hepatocellular carcinoma or cancer of other location 8. Current use of beta-blockers or other vasoactive drugs.

If the exclusion criteria were absent, demographic data, clinical data (cause of liver disease, presence of esophageal varices) and transient elasto-graphy (Fibroscan, Echosens, France) values were recorded on the day of the procedure prior to liver biopsy and HVPG measurement. Blood samples for clinical biochemisty, biomarker levels and abdominal ultrasound for the evaluation of RF parameters were also performed on the day of the procedure prior to liver biopsy and HVPG measurement. Collected data is summarized in Table 2.2.1.1. and the detailed flow chart of the study is presented in Fig. 2.2.1.1.

Table 2.2.1.1. Data collected in the study

Demographic data Gender Age

Clinical data Cause of chronic liver disease Presence of esophageal varices

Laboratory data Total blood count; liver enzymes; bilirubin, albumin, prothrombin time, creatinine

Evaluation of liver

function Child-Turcotte-Pugh score Model of End Stage liver disease (MELD) score

Transient elastography Median, IQR, SR

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Table 2.2.1.1. Continued

RF ultrasound scanning Acquisition of RF parameters Plasma biomarker levels PlGF, VWF, sCD163, Nogo-A HVPG measurement HVPG value

Abbreviations: IQR – interquartile range; SR – success rate; RF – radiofrequency; PlGF – placental growth factor; VWF – von Willebrand factor; sCD163 – soluble CD163; HVPG – hepatic venous pressure gradient.

Fig. 2.2.1.1. Flow chart of the study

HVPG – hepatic venous pressure gradient; ELISA – sandwich enzyme linked immuno-sorbent assay; RF – radiofrequency; PH – portal hypertension.

2.2.2. Measurement of biomarker levels

Peripheral venous blood samples were obtained on the day of the proce-dure prior to HVPG measurement. Blood samples from the hepatic vein were obtained during the HVPG measurement. Blood samples were collected in EDTA tubes. Samples were centrifuged at room temperature in 3500 g for 10 minutes, plasma was separated and stored in –80 °C for further analysis. Sandwich enzyme-linked immunosorbent assay (ELISA) kits were used to determine Nogo-A (Elabscience, USA), PlGF (Abbexa LTD, UK), VWF (Elabscience, USA) and sCD163 (R&D systems, USA)

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plasma levels, according to manufacturers’ protocols using SunriseTM _(Tecan

Trading AG, Switzerland) microplate reader with 450 nm wavelength filter and MagellanTM_{(Tecan Trading AG, Switzerland) software.}

2.2.3. Ultrasound scanning

The procedure was performed on the day of liver biopsy or HVPG measurement prior to the procedures. Ultrasound scanner Ultrasonix Sonix

Touch (Analogic Ultrasound, Canada), which allows to collect raw RF

signals of all scanning lines, equipped with a phased array sector probe (SA 4–2, 64 acoustic elements) was used for the data collection in this study. The main parameters of ultrasonic scanning and RF signal digitization were as follows: sampling frequency fs – 40 MHz, ADC resolution 16 bits,

number of scanning lines – 58 (angle of phased array imaging sector – 810_,

scanning depth – 14 cm, frequency of ultrasound waves – 2 MHz, frame rate – 41.48 Hz, transmit focal single depth – 8 cm). The B scans (316 frames) containing raw RF signals were acquired and stored for off-line processing. Patients were asked to hold their breath after deep inspiration and ultrasound probe was kept in a fixed position for 10 s, while the sequences of B scans and raw B scan forming RF signals were obtained. The 2nd _{and 3}rd _{anatomical segments of the liver were scanned. Scanning}

plane in these segments was aligned with the region of homogeneous liver parenchyma, trying to minimize vascular and other structure patterns in the B scan image. Heart structures were visualized on the right side of the scanning sector.

2.2.4. RF signal processing and quantitative analysis

Stored RF signals were analyzed using a specifically developed algo-rithm. The detailed description of the creation of the algorithm and signal processing is presented in the publications by Sakalauskas et al. [220, 221]. Investigated parameters are presented in Table 2.2.4.1.

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Table 2.2.4.1. Investigated derived parameters of endogenous motion

No. Parameter Description

1. dantero maximal amplitude of endogenous displacements towards

the probe, μm

2. dretro maximal amplitude of the displacements backward, μm

3. dRMS average level of motion (standard deviation), μm

4. µROI[0–10Hz, 2×2 cm] average strain [estimated for the 0–10 Hz sub-band of

endogenous motion in the 2×2 cm ROI], μm/cm 5. σROI[0–10 Hz, 2×2 cm] standard deviation of strain [0–10 Hz, 2×2 cm], μm/cm

6. µROI[0–10 Hz, 1×1 cm] average strain [0–10 Hz, 1×1 cm], μm/cm

7. σROI[0–10 Hz, 1×1 cm] standard deviation of strain [0–10 Hz, 1×1 cm], μm/cm

Abbreviations: ROI – region of interest; μROI – average strain; σROI – standard deviation of

strain.

2.2.5. Liver biopsy

Liver biopsy was performed using a spring-loaded core biopsy instru-ment with 22 mm shooting length and a 14G biopsy needle. Liver biopsy specimen was placed in formalin and processed routinely by the pathologists. Histological fibrosis grade was evaluated using METAVIR score by the expert pathologist.

2.2.6. HVPG measurement

The degree of portal hypertension was determined by the invasive HVPG measurement. The procedure was performed by the same experienced radiologist according to the standard described by Groszmann [221]. At least three repeated measurements were performed to determine free and wedged hepatic vein pressure for the calculation of HVPG. HVPG values of 1–5 mmHg were considered to represent normal portal pressure, portal hypertension was diagnosed at HVPG≥6 mmHg. HVPG≥10 mmHg was considered to be CSPH and ≥12 mmHg – SPH.

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2.3. Statistical analysis

Statistical analysis was performed using SPSS 25.0 and Medcalc softwares. Calculations of study power and minimal sample for the study of biomarkers were based on publications by other authors and on the results of a pilot study (N=50). According to these calculations the sample of 100 patients was sufficient to observe significant differences. Additional power calculations were performed after all patients were included in the study, which showed power for all investigated parameters between 0.8 and 1. Kolmogorov–Smirnov test was used to check data normality. Descriptive statistics are provided as mean and standard deviation (SD) or as median and range for non-parametric data. Differences between the groups were assessed with the Student’s t test or Mann-Whitney’s test as appropriate. Correlations were performed by means of Spearman’s correlation or Pearson’s correlation and expressed by Spearman’s or Pearson’s coefficient as appropriate. Receiver operating characteristic (ROC) curves were created to assess the predictive values of biomarkers for CSPH, SPH and EV and predictive values of RF parameters for different stages of liver fibrosis and degree of PH. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated. The value with the best sensitivity and specificity in AUC analysis was chosen for further analysis for biomarker levels and cut-off value according to Youden’s index for the RF parameters. Statistical significance was established at p<0.05

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3. RESULTS

3.1. Radiofrequency ultrasound-based tissue strain imaging method in liver fibrosis

The first part of the study included 60 patients (60 RF data recordings – 316 frames/record; duration of the record, 7.62 s – analyzed): 23 subjects as healthy controls (F0 METAVIR fibrosis score); 21 subjects with hepatitis C (F1–F3 METAVIR fibrosis score) and 16 subjects with liver cirrhosis (F4 METAVIR fibrosis score). Mean age (±standard deviation) of the subjects was 41.48 ± 14.49, 41.7% were male. Summary of demographic data is pre-sented in Table 3.1.1.

Table 3.1.1. Summary of demographic data

Variable Characteristics (N=60) Sex (female/male; %) 58.3/41.7 Age (years; SD) 41.48 (14.49) Aethiology (% of patients) • Hepatitis C • Alcohol-induced cirrhosis • HCV cirrhosis 35 56.2 43.8 Fibrosis score by METAVIR

• F0 (% of patients) • F1 (% of patients) • F2 (% of patients) • F3 (% of patients) • F4 (% of patients) 38.3 25 8.3 1.7 26.7 Abbreviations: SD – standard deviation; HCV – hepatitis C virus.

Two parameters – average strain over ROI (μROI) and standard deviation

of strain (σROI) were analyzed in 2 frequency subbands (integration

bandwidths 0–10 Hz and 10–20 Hz) and 2 sizes of ROI (1×1 cm and 2×2 cm). Ordinary B-mode images of reflections and parametric strain maps of μROI parameter (integration bandwidth, 0–10 Hz) are displayed in

Fig. 3.1.1. The largest strain amplitude values and variance were observed in healthy liver; meanwhile, the lowest values were observed in the cirrhotic liver. The values in the strain map of a cirrhotic liver did not exceed 5 μm/cm; meanwhile, the strain map of a healthy subject frequently had values exceeding 10 μm/cm. Such differences could not be observed in ordinary B-mode images, which looked quite similar in the regions of liver parenchyma.

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Fig. 3.1.1. The examples of ordinary B-mode images and obtained strain

maps in different stages of liver disease

Healthy control (A and D, respectively), hepatitis C patient (B and E, respectively), and a patient with liver cirrhosis (C and F, respectively).

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Further we evaluated the relation of RF parameters with the assigned METAVIR liver fibrosis scores. The results are presented in boxplots as median and interquartile ranges in Fig. 3.1.2 (ROI size 1×1 cm) and Fig. 3.1.3 (ROI size 2×2 cm). The largest spread of the parameters was observed for the healthy controls and decreased with higher METAVIR scores. When ROI was 1×1 cm both parameters showed statistically significant diffe-rences within all groups in lower frequencies, however in higher frequencies the difference between F0 and F1–F3 groups was not significant. The Kruskal-Wallis test confirmed that the differences among all 3 investigated groups were statistically significant (p<0.001).

Fig. 3.1.2. The parameters obtained performing the analysis of

strain maps in ROI (size, 1×1 cm) in relation to liver fibrosis levels (F0, healthy controls; F1–F3, hepatitis C; F4, liver cirrhosis)

A, μROI (integration bandwidth, 0–10 Hz); B, σROI (integration bandwidth, 0–10 Hz); C, μROI

(integration bandwidth, 10–20 Hz); D, σROI (integration bandwidth, 10–20 Hz). p, results of

Kruskal-Wallis test; p*, results of Mann-Whitney test. μROI – average strain; σROI – standard

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Fig. 3.1.3. The parameters obtained performing the analysis of

strain maps in ROI (size, 2×2 cm) in relation to liver fibrosis levels (F0, healthy controls; F1–F3, hepatitis C; F4, liver cirrhosis)

A, μROI (integration bandwidth, 0–10 Hz); B, σROI (integration bandwidth, 0–10 Hz); C, μROI

(integration bandwidth, 10–20 Hz); D, σROI (integration bandwidth, 10–20 Hz). p, results of

Kruskal-Wallis test; p*, results of Mann-Whitney test. μROI – average strain; σROI – standard

deviation of strain; ROI – region of interest.

When ROI was 2×2 cm, μROI parameter performed better in

distingui-shing F0 group at low frequencies. Both parameters showed statistically signi-ficant differences for F0 versus F4 (p<0.001) and F1–F3 versus F4 (p<0.01) groups in both frequency subbands.

Linear correlation between the analyzed parameters and liver fibrosis stage was observed. Only σROI parameter in ROI 2×2 cm and low frequency

subband showed strong negative correlation (r=-0.619, p<0.0001), other parameters showed moderate correlation (Table 3.1.2).

NOVEL IMAGING AND SERUM BIOMARKERS FOR THE DIAGNOSIS OF LIVER FIBROSIS AND PORTAL HYPERTENSION

Sigita Gelman