Correlating MRI-Derived Biomarkers with Histological Staging of Liver Fibrosis: A Cross-Sectional Analysis(https://doi.org/10.63386/618614)

By /

Feiyang Xiong1， Wenliang Lv2*.

1, Graduate school, Hunan University of Traditional Chinese Medicine, Changsha, Hunan Province, 410208, China.

2, Department of Infectious Diseases, Guang ‘anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.

Corresponding Author: Wenliang Lv(lvwenliang@sohu.com)

The First Author: Feiyang Xiong Email: xfy199609@163.com

Abstract

Background: When the liver experiences chronic inflammation or repeated damage, the condition known as liver fibrosis develops as a result of an excessive buildup of scar tissue. Fibrosis can develop from most forms of chronic liver disease in the end. Scar tissue cells are functionally incapable of self-repairing or regenerating like healthy liver cells. Fibrosis can lower total liver function and limit the organ’s capacity to regenerate.

Scope of Review: This review covers the fundamental concept, biomarkers obtained from magnetic resonance imaging (MRI) and their relationship to histological staging, diagnostic performance, as well as the benefits and drawbacks of using MRI for the noninvasive staging of liver fibrosis. Extracellular volume fraction, liver stiffness, and other indicators that are connected with histological fibrosis stages can be measured because of recent advancements in MRI technology.

Major conclusions: Although the liver biopsy is the most effective method for fibrosis diagnosis and staging, there are a number of drawbacks, including sample variability and invasiveness. To evaluate liver fibrosis, efforts have recently shifted toward noninvasive methods like MRI. MRI is used to evaluate liver lesions that are not clear, because of its strong image contrast without ionizing radiation. As a result, MRI has become more important in the liver fibrosis stage. The use of MRI-derived biomarkers, such as liver stiffness and iron content, which exhibit encouraging correlations with histology findings, that has been highlighted in a number of investigations conducted recently. The study also reviewed how deep learning (DL) algorithms applied to MRI data have significantly improved the accuracy of fibrosis staging.

Keywords: Magnetic Resonance Imaging (MRI), Liver Fibrosis, Noninvasively Stage, Chronic Liver Disease.

 

  1. Introduction

The leading solid organ in the human bodyis considered the liver, making up around 1/50thof total body weight and weighing between 1,200 and 1,500g of an adult. Comprehending the complex structure of liver is important for comprehending hepatic fibrosis (HF) and its outcome. The hepatic artery (HA)and the portal vein are the two main liver’s primary sources of blood flow [1]. It was traditionally assumed that fibrosis and compensated and decompensated cirrhosis were the irreversible processes that happened in a sequential order. However, many patients have had fibrosis regression as a result of efficient and long-term viral suppression due to the quick advancements in the anti-viral treatment for the chronic hepatitis B (CHB) and Chronic hepatitis C (CHC) [2].The histologic characteristic can consistently predict long-term outcome is fibrosis, and repeated evaluations of fibrosis are crucial for predicting the patient outcomes and gauging response to the treatment. Early stages of liver damage and fibrosis are curable and reversible, even if liver transplantation (LT) is considered the onlytreatment for the decompensated at the final stage of the liver fibrosis (LF) [3].

In addition, hepatic encephalopathy and other clinical problems such as hydropic decompensation and bleeding episodes are frequently brought on by the persistent portal hypertension by LF [4].Globally, the most frequent cause of the chronic liver disease (CLD) is nonalcoholic fatty liver disease (NAFLD). Benign nonalcoholic steatohepatitis (NASH) and NAFLD, which is linked to hepatocellular cancer and contains severe fibrosis, are the two types of NAFLD [5]. The most significant factor influencing the course of the clinical condition and the death of NASH patients is LF. While the exact mechanism responsible for the beginning and advancement of fibrosis in NASH remains unclear, new research indicates that bile acids can play a part in the inflammatory and fibrotic processes of the liver [6].

CLD is the root cause of LF and it can be brought by autoimmune disorders, viral infections, metabolic disorders, alcoholic liver disease (ALD), drug toxicity,and NAFLD [7].Hepatic stellate cells (HSC) are activated following liver damage and inflammation, which is a crucial step in the fibrogenesis process. This activation converts HSC from quiescent to proliferative, fibrogenic, and contractile myofibroblasts[8]. Treatment focusing on the underlying etiology and the fibrosis concurrently can hasten the fibrosis’s remission and encourage liver regeneration. To optimize the chance of regression and a better prognosis, early identification of LF is crucial for the early start of treatment [9].

The occurrence of CLD morbidity and mortality come along with notably increased over the past few decades, mainly with the growth in NAFLD. It is true that a sizable percentage of individuals will eventually experience LF, which will lead to cirrhosis. About 1.8% of deaths are related to cirrhosis, an end-stage illness that can have fatal consequences such as liver failure, portal hypertension, and hepatocellular carcinoma [10]. Developing fibrosis and regenerating nodules that interfere with the standard architecture of the liver are characteristics of cirrhosis [11]. LF is the outcome of hepatocyte damage brought by a variety of etiologies, including infection, alcohol, NAFLD, genetic metabolic disease, immunological disease, and drug usage. These factors also activate the HSC and produce cytokine release and collagen accumulation. The most advanced and irreversible stage of LF is called cirrhosis, and it can lead to hepatocellular cancer and portal hypertension [12].

The unnecessaryaccretion of the extracellular matrix (ECM)proteins caused by the activation of HSCs is a characteristic of LF, referred to as HF. It is possible to witness this fibrotic scarring process in all chronic liver disease sources [13]. The preferred gold standard procedure for predictingNASH and assessing LF in NAFLD patients is liver biopsy (LB) [14]. Ionizing radiation is not required for MRI to provide a high resolution evaluation of liver disease. Advanced imaging sequences can additionally provide molecular, structural and functional information, possibly acting as imaging biomarkers. However, longer scanning times, a greater risk of motion artifacts, and higher expenses are characteristics of MRI [15]. The objective is to correlate the biomarkers of the MRI with the histological staging of the LF.

 

  1. Pathophysiology of LF

Chronic liver injuries resulting from hepatotoxic and cholestatic injuries are the two main causes of LF. Alcoholic Steatohepatitis (ASH), Hepatitis C and B (HBC and HCB) viral infections, and NASH and other external causes can cause cellular damage that results in hepatotoxic injury [16]. All LF is caused by the same molecular pathways, including hepatocyte loss, endothelial or epithelial barrier breakdown, activation of hepatocyte-stromal cells, and persistent inflammation with cytokine release. Cellular and extracellular signals are necessary for the complicated process of hepatic fibrogenesis[17].

2.1. Causes of LF

LF is a medical disorder that arises from persistent liver injury caused by multiple sources, such as drug-induced hepatotoxicity, viral hepatitis, ALD, NAFLD, NASH, and AIH. Alcohol consumption and hepatitis B and C are the most common causes. Depending on the fundamental cause, LF can exhibit a variety of morphological features. Hepatocytes (HSCs) are stimulated and undergo myofibroblast differentiation following liver damage. HSC activation is triggered by intracellular signaling cascades that are started by activated monocytes, Kuffer cells (KCs), platelets, and injured hepatocytes secreting Transforming growth factor-β (TGF-β) and platelet derived growth factor (PDGF). There are pathways unique to each disease, and the distinct pathophysiological mechanisms among the various etiologies of LF emphasize the necessity for a varied therapeutic strategy [18].

2.1.1 Viral Hepatitis

Viral hepatitis is the term for virally-induced inflammation of the liver. Currently, there are 5 viruses known to specifically infect the liver (hepatitis A to E), usually by distinct pathways [19]. Although usually produce small infections in immunocompetent hosts, non-hepatotropic viruses can also induce hepatitis by targeting different organs in the body. In immunocompromised hosts, especially transplant recipients, the importance of non-hepatotropic viruses is most noticeable. A persistent global public health issue, viral hepatitis affects millions of individuals and results in thousands of deaths from cirrhosis, liver cancer, and acute and chronic infections [20].

2.1.2. ALD

The pathophysiology of ALD is multifactorial and includes mechanisms such as HSC-stimulated fibrogenesis, organelle stress and hepatocyte mortality, and fat accumulation[21].

2.1.3. NAFLD

A variety of liver disorders, including NASH, simple fatty liver (SFL), and associated fibrosis or cirrhosis, are collectively referred to as NAFLD. These disorders are intimately linked to type 2 diabetes, insulin resistance, and hereditary vulnerability [22].

2.1.4. Autoimmune Liver Disease

Immunological tolerance to hepatocytes and biliary epithelial cells is lost in immune-mediated liver illnesses such as Primary Sclerosing Cholangitis (PSC), Autoimmune Hepatitis (AIH),and Primary Biliary Cholangitis (PBC). The factors that cause Autoimmune Liver Disease (AILD) to initiate are currently unknown and the pathogenic mechanisms behind AILD are generally acknowledged to be dependent on effector Cluster of differentiation 4 (CD4) and CD8 T cell immune responses to self-autoantigen (s) [23].

  1. Magnetic Resonance Imaging in Liver Assessment

3.1. Overview of MRI Techniques

The macrostructural and parenchymal alterations that characterize LF and cirrhosis can be better understood by using an intravenous difference agent in conjunction with conventional MRI[24]. Although MRI is highly sensitive in identifying nodular liver parenchymal alterations in cirrhosis, it is challenging to quantify the subjective visual evaluation of liver texture on imaging. Moreover, early fibrosis stages might not be noticeable for visual evaluation. Nonetheless, there remains an unfulfilled need to create methods for noninvasively diagnosing LF and tracking its response to treatment [25].

3.2. Traditional Methods of MRI for Evaluating the LF

MRI-based methods are useful not only for measuring steatosis, it also useful for measuring HF. More specifically, LF measurement and NASH assessment benefit from the use of magnetic resonance elastography (MRE). An acoustic driver fastened to the patient creates a shear wave during MRE. After that, the wave moves through the tissues, which physically shifting the tissue can be captured by an MRI.Differential fibrosis levels lead to different degrees of liver stiffness, which show up as variations in shear wave velocity [26].  A new non-invasive technique called MRE maps the elasticity of tissue based on phase contrast; it works similarly to the liver’s tissue stiffness is physically assessed by palpation. It can quantify the quantitative propagation of acoustic strain waves and visualize them directly. In clinical practice, MRE’s diagnostic performance is promising for identifying LF as well as assessing patient survival based on clinical endpoints like hepatocellular carcinoma and hepatic decompensation [27].

  1. MRI-Derived Biomarkers of LF

MRI biomarkers of LF are quantitative measures that show the anatomical and functional alterations in the liver brought by fibrosis. Liver stiffness, diffusion-weighted imaging (DWI), and extracellular volume fraction (ECV) are important indicators that are correlated with the histological phases of fibrosis. These non-invasive markers improve LF staging and assessment, providing an option to more conventional biopsy techniques.

4.1. Liver Stiffness Measurement (LSM)

HF is well-represented by the Liver Stiffness Measurement (LSM). LSM is a helpful therapeutically for the staging of LF but also for the Noninvasive identification of clinically considerable portal hypertension. More importantly, individuals with CLD, the LSM are also connected with thehigher risk of consequent liverrelated events. LSM provides the major benefits of wide clinical availability and noninvasiveness when compared to LB or hepatic venous pressure gradient measurement. This allows for the long-term evaluation of the severity of liver disease in a single patient [28]. The use of LSM as a stand-in marker for HF has grown. MRE and transient elastography (TE) can be used to quantify LS [29]. Inflammation, passive venous congestion, portal hypertension, and biliary blockage are among the other pathophysiological mechanisms that impact liver stiffness. The diagnosis of LF stage can be complicated by several processes, which frequently coexist with the disease. Careful interpretation of high stiffness with elastography is necessary, and clinical characteristics, liver imaging features when available, and laboratory testing for liver function can be helpful in identifying these coexisting confounders[30].

4.2. Extracellular Volume Fraction (ECV)

ECV, which can be computed from the contrast enhanced MRI scan data, has also been shown to be helpful in determining the extent of LF. As a result, MRE and ECV are founded on entirely distinct ideas. ECV is the total of intravascular and extravascular extracellular spaces [31].

4.3. Diffusion-weighted imaging (DWI)

DWI can offer quantitative data for fibrosis staging and used to detect LF. DWI was demonstrated to be a good sequence for LF staging in a number of positive investigations. Conventional DWI, uses a mono-exponential model to analyze the diffusion properties while assuming a Gaussian diffusion distribution, which has drawbacks when assessing liver tissue [32].

4.4.Liver Surface Nodularity (LSN)

To quantify LF and cirrhosis, LSN scores are commonly utilized as a clinical or imaging assessment technique. They are not a direct indicator of the histological alterations themselves, but they definitely correlate with the histological stages of LF. The investigator employed previously published standardized methods to calculate the LSN score on the abdominal Computed Tomography (CT) images by utilizing the LSN software [33].There are no technical issues during the measurement of LSN by using all MR images and measurements typically took less than four minutes [34]. Table 1 represents the methods, uses of the [31], [32],[33], [34]and their Key Findings.

Table 1: Overview of the Other Methods

Reference

Methods/Techniques Used

Study Population

Uses

Key Findings

[31]

MRI measurements, statistical analysis

Patients with chronic liver disease

Biomarker development for clinical assessment

Spleen extracellular volume fraction (mean 0.21) significantly correlates with high-risk varices (p < 0.05).

[32]

Various diffusion-weighted MRI techniques, ROC analysis

Patients with LF

Improve non-invasive LF staging

AUC values for staging LF: 0.84 for DTI and 0.80 for CDI (p < 0.01).

[33]

CT imaging, liver nodularity scoring, statistical validation

Hepatitis C patients

Clinical assessment of liver disease

LSN score of ≥ 2.23 yielded AUROC of 0.80 (95% CI 0.66–0.95) with 62% specificity and 72% sensitivity.

[34]

MRI, semi-automatic image analysis

Patients with LF

Non-invasive assessment tool development

The measurement of semi-automatic nodularity showed a strong connection with the stages of LF.

.

 

  1. Correlation between MRI Biomarkers and Histological Staging of Liver Fibrosis

5.1. Liver Biopsy (LB)

For the diagnostic and histological evaluation of NAFLD, LB is presently considered the gold standard. Unfortunately, because of its intrusive character, a biopsy cannot be used early in a possible patient’s diagnosis process and not appropriate for screening. It is primarily used to differentiate NASH from NAFL and assess the degree of LF in individuals who have a high risk of developing severe liver disease during long-term check-in. A biopsy is necessary in later phases of drug improvement to further evaluate the effectiveness of treatment for NASH [35]. Evaluation of LF phases can be accomplished with non-invasive imaging approaches, particularly with regard to MRI-derived indicators like MRE, which has demonstrated effectiveness. A LB is the gold standard, although it has a number of drawbacks. There is some danger and discomfort associated with the operation. 

5.1.1. Histological Staging of LF and their clinical implication

The Ishak score, Metavir score, and Batts-Ludwig staging system [36] are three widely used techniques for evaluating histological fibrosis. Every scoring system is predicated on the increasing development of septal fibrosis, nodule formation, and periportal fibrosis. Table 2 illustrates the histological staging of LF and its clinical implications.Figure 1 [37] illustrates the associated histopathologic slides and T1-Weighted Gradient Echo (T1w GRE) MR images. Figure 2 [38] consists of four individuals with NAFLD at varying the degrees of fibrosis. The left column of the figure shows the LSN scores. An increased LSN score denotes increased surface nodularity. 

Table 2: Histological Staging and clinical implication of LF

Staging System

Stages

Description

Clinical Implications

Batts-Ludwig score

0

No fibrosis

Normal liver

1

Mild fibrosis

Early stage, usually asymptomatic

2

Either or Between stage 1 or Stage 2

Risk of progression to severe fibrosis

3

Severe fibrosis

Increased risk of liver dysfunction and cirrhosis

4

cirrhosis

End-stage liver disease, significant complications possible

Metavir score

F0

No fibrosis

Normal liver

F1

Mild fibrosis

Early fibrosis

F2

Moderate fibrosis

Progressing fibrosis

F3

Severe fibrosis

Significant fibrosis, risk of cirrhosis

F4

Cirrhosis

End-stage liver disease

Ishak score

0

No Fibrosis

Normal Liver

1

Fibrous Portal Expansion

Mild Fibrosis

2

Few Septa

Moderate Fibrosis

3

Numerous Septa

Severe Fibrosis

4

Bridging Fibrosis

Major risk of progression to cirrhosis

5

Incomplete Cirrhosis

Advanced liver disease

6

Complete Cirrhosis

End-stage liver disease

Figure 1: Metavir Score of LF

 

Figure 2: LSN score of T1w GRE

5.2. Overview of the Outcomes

The ROC curves for predicting LF ≥ F2 show that the AUC forthe (T1W & T2W)fat-saturated (FS) model [39] separates ≥ F2 with an AUC of 0.915 which demonstrated excellent diagnostic performance and for Native T1 values shows high accuracy for diagnosing LF (≥ F2), with Shear wave velocity (SWV) demonstrating even higher accuracy for liver cirrhosis [40]. Furthermore, for both liver cirrhosis and significant fibrosis, native T1 values show high accuracy, while SWV shows greater accuracy for the latter.When differentiating F > 2, the (T1W&T2W) FS model also shows an improved AUC of 0.915.Figure 3 illustrates how the existing method of [39] and [40] are overcomes.

Figure 3: Outcomes of the Existing method using 100-specificity

The LSN score’s ROC curve is used to diagnose extensive fibrosis (F2-F4). In Figure 4, utilizing an LSN score threshold of 2.23 yielded an AUROC of 0.80 with 62% specificity and 72% sensitivity [38].

Figure 4: Outcomes of the Existing method using 1-specificity

  1. Outcomes of MRI of LS using Deep Learning (DL) and Machine Learning

Deep learning (DL) techniques help the physician to make judgments, which makes them beneficial. The diagnostic value increases when the clinical perspective is combined with several paraclinical facts, such as imaging and laboratory results. Given that timely detection and precise staging of LF are essential for avoiding or postponing the clinical decompensation and the need for the LT, DL techniques ought to be able to detect HF at an early stage. Based on clinical reasoning, it seems reasonable that the DL model should concentrate on the spleen and the liver in cases of severe HF as both organs experience morphological changes as cirrhosis progresses, along with consequences such as cites, collateral circulation, and esophageal varices. Because of this, when these models describe LF, they should also include the problems brought on by advanced liver disease [41].

In comparison to elastography alone, outcomes demonstrated that utilizing ML to determine the optimal mix of quantitative ultrasound (QUS) and the elastography parameters can greatly enhance the classification of fibrosis, steatosis and inflammation in patients with the CLD. Because various histological mechanisms cohabit can have confusing effects on LSM, a holistic strategy is essential to reducing or eliminating the need for LB. The correlations between fibrosis stage and inflammatory grade that investigation discovered are noteworthy because, it was necessary to use a multi-parametric method to adjust for the method that fibrosis and inflammation increased stiffness [42].

  1. Conclusion

With the ROC curve (AUC) of 0.915 for distinguishing between severe fibrosis (≥ F2), the (T1W & T2W) FS model stands out for its excellent diagnostic performance. Furthermore, an AUC of 0.80 was obtained by applying a threshold of 2.23 to the LSN score to diagnose severe fibrosis (F2-F4) with 72% sensitivity and 62% specificity. Shear wave velocity (SWV) revealed an even higher AUC of 0.86 for cirrhosis, whereas native T1 values showed an AUC of 0.84 for both liver cirrhosis (F4) and severe fibrosis (≥ F2). Notably, there were no statistically significant variations found among the different MRI parameters for the staging of LF. The potential of MRI and associated biomarkers to improve LF staging accuracy, decrease the need for invasive biopsy procedures, and improve patient outcomes is highlighted by these studies. The study’s limitations include the possibility of selection bias and the unpredictability of histological evaluations, which could have an impact on the connection with biomarkers produced from MRIs. In the future, the scope will include undertaking multi-center, longitudinal research, using machine learning and advanced imaging techniques, and investigating novel biomarkers for enhanced diagnostic precision in LF staging.

References

  • [1] Acharya, P., Chouhan, K., Weiskirchen, S. and Weiskirchen, R., 2021. Cellular mechanisms of liver fibrosis. Frontiers in Pharmacology, 12, p.671640.https:// doi.org/10.3389/fphar.2021.671640
  • [2] Sun, Y.M., Chen, S.Y. and You, H., 2020. Regression of liver fibrosis: evidence and challenges. Chinese Medical Journal, 133(14), pp.1696-1702.https://doi.org/11 0.1097/CM9.0000000000000835
  • [3] Pirasteh, A., Periyasamy, S., Meudt, J.J., Liu, Y., Lee, L.M., Schachtschneider, K.M., Schook, L.B., Gaba, R.C., Mao, L., Said, A. and McMillan, A.B., 2022. Staging liver fibrosis by fibroblast activation protein inhibitor PET in a human-sized swine model. Journal of Nuclear Medicine, 63(12), pp.1956-1961.https://doi.org/10.296 7/jnumed.121.263736
  • [4] Roehlen, N., Crouchet, E. and Baumert, T.F., 2020. Liver fibrosis: mechanistic concepts and therapeutic perspectives. Cells, 9(4), p.875.http://dx.doi.org/ 10.3390/cells9040875
  • [5] Imajo, K., Honda, Y., Kobayashi, T., Nagai, K., Ozaki, A., Iwaki, M., Kessoku, T., Ogawa, Y., Takahashi, H., Saigusa, Y. and Yoneda, M., 2022. Direct comparison of US and MR elastography for staging liver fibrosis in patients with nonalcoholic fatty liver disease. Clinical Gastroenterology and Hepatology, 20(4), pp.908-917. https://doi.org/10.1016/j.cgh.2020.12.016
  • [6] Harrison, S.A., Rossi, S.J., Paredes, A.H., Trotter, J.F., Bashir, M.R., Guy, C.D., Banerjee, R., Jaros, M.J., Owers, S., Baxter, B.A. and Ling, L., 2020. NGM282 improves liver fibrosis and histology in 12 weeks in patients with nonalcoholic steatohepatitis. Hepatology, 71(4), pp.1198-1212.https://doi.org/10.1002/hep.305 90
  • [7] Tan, Z., Sun, H., Xue, T., Gan, C., Liu, H., Xie, Y., Yao, Y. and Ye, T., 2021. Liver fibrosis: therapeutic targets and advances in drug therapy. Frontiers in cell and developmental biology, 9, p.730176.https://doi.org/10.3389/fcell.2021.730176
  • [8] Soon, G. and Wee, A., 2021. Updates in the quantitative assessment of liver fibrosis for nonalcoholic fatty liver disease: histological perspective. Clinical and Molecular Hepatology, 27(1), p.44.https://doi.org/10.3350%2Fcmh.2020.0181
  • [9] Xiao, B.H., Huang, H., Wang, L.F., Qiu, S.W., Guo, S.W. and Wáng, Y.X.J., 2020. Diffusion MRI derived per area vessel density as a surrogate biomarker for detecting viral hepatitis B-induced liver fibrosis: a proof-of-concept study. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 25(5), pp.474-483.https:// doi.org/10.1177/2472630320915838
  • [10] Dana, J., Venkatasamy, A., Saviano, A., Lupberger, J., Hoshida, Y., Vilgrain, V., Nahon, P., Reinhold, C., Gallix, B. and Baumert, T.F., 2022. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatology international, 16(3), pp.509-522.https://doi.org/10.1007% 2Fs12072-022-10303-0
  • [11] Nitsch, J., Sack, J., Halle, M.W., Moltz, J.H., Wall, A., Rutherford, A.E., Kikinis, R. and Meine, H., 2021. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. International Journal of Computer Assisted Radiology and Surgery, 16, pp.457-466.https://doi.org/10.1007/s11548-020-02295-9
  • [12] Park, H.C., Joo, Y., Lee, O.J., Lee, K., Song, T.K., Choi, C., Choi, M.H. and Yoon, C., 2024. Automated classification of liver fibrosis stages using ultrasound imaging. BMC Medical Imaging, 24(1), p.36. https://doi.org/10.1186/s12880-024-01209-4
  • [13] Vernuccio, F., Cannella, R., Bartolotta, T.V., Galia, M., Tang, A. and Brancatelli, G., 2021. Advances in liver US, CT, and MRI: moving toward the future. European radiology experimental, 5(1), p.52.https://doi.org/10.1186/s4174 7-021-00250-0
  • [14] Imajo, K., Honda, Y., Yoneda, M., Saito, S. and Nakajima, A., 2020. Magnetic resonance imaging for the assessment of pathological hepatic findings in nonalcoholic fatty liver disease. Journal of Medical Ultrasonics, 47, pp.535-548.https://doi.org/10.1007/s10396-020-01059-x
  • [15] Zheng, S., He, K., Zhang, L., Li, M., Zhang, H. and Gao, P., 2023. Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging. European Journal of Radiology, 165, p.110912.https://doi.org/10.1016/j.ejrad.2023.110912
  • [16] Berumen, J., Baglieri, J., Kisseleva, T. and Mekeel, K., 2021. Liver fibrosis: Pathophysiology and clinical implications. WIREs mechanisms of disease, 13(1), p.e1499.https://doi.org/10.1002%2Fwsbm.1499
  • [17] Dhar D, Baglieri J, Kisseleva T, & Brenner DA (2020). Mechanisms of liver fibrosis and its role in liver cancer. Exp Biol Med (Maywood), 245(2), 96–108. https://doi.org/ 10.1177/1535370219898141
  • [18] Khanam, A., Saleeb, P.G. and Kottilil, S., 2021. Pathophysiology and treatment options for hepatic fibrosis: can it be completely cured?. Cells, 10(5), p.1097.https://doi.org/10.3390/cells10051097
  • [19] Pisano, M.B., Giadans, C.G., Flichman, D.M., Ré, V.E., Preciado, M.V. and Valva, P., 2021. Viral hepatitis update: Progress and perspectives. World journal of gastroenterology, 27(26), p.4018.https://doi.org/10.3748%2Fwjg.v27.i26.4018
  • [20] Cheung, A. and Kwo, P., 2020. Viral hepatitis other than A, B, and C: evaluation and management. Clinics in liver disease, 24(3), pp.405-419.https://doi.org/ 10.1016%2Fj.cld.2020.04.008
  • [21] Yang, Y.M., Cho, Y.E. and Hwang, S., 2022. Crosstalk between oxidative stress and inflammatory liver injury in the pathogenesis of alcoholic liver disease. International journal of molecular sciences, 23(2), p.774.https://doi.org/ 10.3390/ijms23020774
  • [22] Du, W. and Wang, L., 2022. The crosstalk between liver sinusoidal endothelial cells and hepatic microenvironment in NASH related liver fibrosis. Frontiers in Immunology, 13, p.936196. https://doi.org/10.3389/fimmu.2022.936196
  • [23] Richardson, N., Wootton, G.E., Bozward, A.G. and Oo, Y.H., 2022, July. Challenges and opportunities in achieving effective regulatory T cell therapy in autoimmune liver disease. In Seminars in immunopathology(Vol. 44, No. 4, pp. 461-474). Berlin/Heidelberg: Springer Berlin Heidelberg.https://doi.org/10.1007/ s00281-022-00940-w
  • [24] Tagliaferro, M., Marino, M., Basile, V., Pocino, K., Rapaccini, G.L., Ciasca, G., Basile, U. and Carnazzo, V., 2024. New Biomarkers in Liver Fibrosis: A Pass through the Quicksand?. Journal of Personalized Medicine, 14(8), p.798.https:// doi.org/10.3390/jpm14080798
  • [25] Hectors, S.J., Kennedy, P., Huang, K.H., Stocker, D., Carbonell, G., Greenspan, H., Friedman, S. and Taouli, B., 2021. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid–enhanced MRI. European radiology, 31, pp.3805-3814. https://doi.org/10.1007/s00330-020-07475-4
  • [26] Trujillo, M.J., Chen, J., Rubin, J.M. and Gao, J., 2021. Non-invasive imaging biomarkers to assess nonalcoholic fatty liver disease: A review. Clinical Imaging, 78, pp.22-34.https://doi.org/10.1016/j.clinimag.2021.02.039
  • [27] Wu, L., Shen, Y. and Li, F., 2020. Non-invasive diagnosis of liver fibrosis: A review of current imaging modalities. Gastroenterología y Hepatología (English Edition), 43(4), pp.211-221.
  • [28] Semmler, G., Yang, Z., Fritz, L., Köck, F., Hofer, B.S., Balcar, L., Hartl, L., Jachs, M., Stopfer, K., Schedlbauer, A. and Neumayer, D., 2023. Dynamics in liver stiffness measurements predict outcomes in advanced chronic liver disease. Gastroenterology, 165(4), pp.1041-1052.https://doi.org/10.1053/j.gastro. 2023.06.030
  • [29] Ajmera, V., Kim, B.K., Yang, K., Majzoub, A.M., Nayfeh, T., Tamaki, N., Izumi, N., Nakajima, A., Idilman, R., Gumussoy, M. and Oz, D.K., 2022. Liver stiffness on magnetic resonance elastography and the MEFIB index and liver-related outcomes in nonalcoholic fatty liver disease: a systematic review and meta-analysis of individual participants. Gastroenterology, 163(4), pp.1079-1089.https://doi.org/ 10.1097%2FMCG.0000000000001433
  • [30] Ozturk, A., Olson, M.C., Samir, A.E. and Venkatesh, S.K., 2022. Liver fibrosis assessment: MR and US elastography. Abdominal Radiology, pp.1-14.https://doi.org/10.1007%2Fs00261-021-03269-4
  • [31] Tani, T., Sato, K., Sakamoto, K., Ito, E., Nishiyama, M., Urakawa, H. and Yoshimitsu, K., 2021. Importance of extracellular volume fraction of the spleen as a predictive biomarker for high-risk esophago-gastric varices in patients with chronic liver diseases: a preliminary report. European Journal of Radiology, 143, p.109924.
  • [32] Jiang, Y.L., Li, J., Zhang, P.F., Fan, F.X., Zou, J., Yang, P., Wang, P.F., Wang, S.Y. and Zhang, J., 2024. Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models. World Journal of Gastroenterology, 30(9), p.1164. https://doi.org/10.3748%2Fwjg.v30.i9.1164
  • [33] Elkassem, A.A., Allen, B.C., Lirette, S.T., Cox, K.L., Remer, E.M., Pickhardt, P.J., Lubner, M.G., Sirlin, C.B., Dondlinger, T., Schmainda, M. and Jacobus, R.B., 2022. Multiinstitutional evaluation of the liver surface nodularity score on CT for staging liver fibrosis and predicting liver-related events in patients with hepatitis C. American Journal of Roentgenology, 218(5), pp.833-845.
  • [34] Kim, S.W., Kim, Y.R., Choi, K.H., Cho, E.Y., Song, J.S., Kim, J.E., Kim, T.H., Lee, Y.H. and Yoon, K.H., 2020. Staging of liver fibrosis by means of semiautomatic measurement of liver surface nodularity in MRI. American Journal of Roentgenology, 215(3), pp.624-630.http://dx.doi.org/2214/AJR.19.22041
  • [35] Heyens, L.J., Busschots, D., Koek, G.H., Robaeys, G. and Francque, S., 2021. Liver fibrosis in non-alcoholic fatty liver disease: from liver biopsy to non-invasive biomarkers in diagnosis and treatment. Frontiers in medicine, 8, p.615978.https:// doi.org/10.3389/fmed.2021.615978
  • [36] Chowdhury, A.B. and Mehta, K.J., 2023. Liver biopsy for assessment of chronic liver diseases: a synopsis. Clinical and experimental medicine, 23(2), pp.273-285.http://dx.doi.org/10.1007/s10238-022-00799-z
  • [37] Im, W.H., Song, J.S. and Jang, W., 2022. Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques. Abdominal Radiology, pp.1-17.https://doi.org/10.1007/s00261-021-03181-x
  • [38] Catania, R., Furlan, A., Smith, A.D., Behari, J., Tublin, M.E. and Borhani, A.A., 2021. Diagnostic value of MRI-derived liver surface nodularity score for the non-invasive quantification of hepatic fibrosis in non-alcoholic fatty liver disease. European Radiology, 31, pp.256-263.https://doi.org/10.1007/s00330-020-07114-y
  • [39] Wei, H., Shao, Z., Fu, F., Yu, X., Wu, Y., Bai, Y., Wei, W., Meng, N., Liu, K., Han, H. and Wang, M., 2023. Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity. The British Journal of Radiology, 96(1141), p.20220512.https://doi.org/10.1007/s00 330-019-06335-0
  • [40] Li, J., Liu, H., Zhang, C., Yang, S., Wang, Y., Chen, W., Li, X. and Wang, D., 2020. Native T1 mapping compared to ultrasound elastography for staging and monitoring liver fibrosis: an animal study of repeatability, reproducibility, and accuracy. European Radiology, 30, pp.337-345.https://doi.org/10.1259%2Fbjr. 20220512
  • [41] Popa, S.L., Ismaiel, A., Abenavoli, L., Padureanu, A.M., Dita, M.O., Bolchis, R., Munteanu, M.A., Brata, V.D., Pop, C., Bosneag, A. and Dumitrascu, D.I., 2023. Diagnosis of liver fibrosis using artificial intelligence: a systematic review. Medicina, 59(5), p.992.https://doi.org/10.3390/medicina59050992
  • [42] Destrempes, François, Marc Gesnik, Boris Chayer, Marie-Hélène Roy-Cardinal, Damien Olivié, Jeanne-Marie Giard, Giada Sebastiani, Bich N. Nguyen, Guy Cloutier, and An Tang. “Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease.” PLoS One17, no. 1 (2022): e0262291. https://doi.org/10.1371/journal .pone.0262291

 

Original-Version-Master-File-Zkg-International-Feiyang (1)Download

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top