Effects of hepatic fibrosis on the quantification of hepatic steatosis using the controlled attenuation parameter in patients with chronic hepatitis B

Abstract

Purpose

This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB).

Methods

CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses.

Results

Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not.

Conclusion

CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Key points

In chronic hepatitis B (CHB) patients, the correlation coefficient between controlled attenuation parameter (CAP) and the histologically determined hepatic fat fraction was lower in patients with fibrosis stages F2-4 (r=0.443) than in those with F0-1 (r=0.527). CAP was less accurate in diagnosing significant hepatic steatosis in CHB patients with significant or more advanced hepatic fibrosis than in those with no or mild fibrosis. The results of CAP quantification of hepatic steatosis by transient elastography in patients with severe fibrosis should be interpreted cautiously.

Graphic Abstract

Introduction

Chronic hepatitis B (CHB) is a significant viral liver disease that is responsible for over 600,000 deaths annually from related conditions. It often involves varying degrees of hepatic fibrosis, and a substantial number of patients eventually develop liver cirrhosis [1-3]. Additionally, 14%-30% of individuals with CHB also suffer from fatty liver, which heightens the risk of fibrosis progression, liver cancer, and mortality [4-7]. Over recent decades, the prevalence of steatotic liver disease has steadily increased [8], making it important to accurately quantify the degree of hepatic steatosis in the presence of fibrosis in patients with CHB.

Although liver biopsy is considered the standard method for evaluating liver fibrosis and steatosis [9], it is invasive and subject to sampling error, as well as inter- and intra-observer variability [10]. Noninvasive methods such as ultrasonography (US) and magnetic resonance imaging (MRI) are preferred, especially for follow-up examinations [11,12]. Transient elastography (TE) has become widely used for noninvasively quantifying liver fibrosis, and the results from TE are both accurate and reproducible [13,14]. TE not only measures liver stiffness but also quantifies hepatic steatosis by assessing the controlled attenuation parameter (CAP) [15]. CAP is an ultrasound-based method that measures the extent of attenuation as ultrasound beams pass through fat droplets within the liver tissue [16]. Various components within the liver tissue, such as fats, glycogen, and fibrotic scars, can directly affect the attenuation of these ultrasound beams [17]. In patients with hepatic fibrosis, the accumulation of scar tissue in the liver may potentially influence the accuracy of CAP measurements. There are conflicting findings regarding how the degree of hepatic steatosis and liver fibrosis influence each other's measurements on TE [18-22]. Studies involving patients with CHB have shown that CAP values were higher in patients with severe fibrosis than in those without [18,19]. However, other studies, involving patients with various types of chronic liver disease, have indicated that CAP values or attenuation coefficient values were not impacted by the degree of hepatic fibrosis [20-22]. These studies faced limitations in assessing the effects of hepatic fibrosis on steatosis measurements using TE in patients with CHB, as some included patients with a heterogeneous etiology rather than exclusively CHB, did not use liver biopsy as the reference standard, or involved a relatively small number of patients with severe fibrosis or liver cirrhosis.

The present study retrospectively assessed how the degree of liver fibrosis influences the CAP quantification of hepatic steatosis in patients with CHB, using histopathology as the reference standard.

Materials and Methods

Compliance with Ethical Standards

The protocol of this retrospective study was approved by the institutional review board of Asan Medical Center (approval No. 2023-0103), which waived the requirement for written informed consent due to the retrospective design of the study.

Study Design and Patients

The electronic medical records of Asan Medical Center, a tertiary institution in Seoul, Korea, were searched to identify all adult patients (aged 18-80 years) who underwent liver resection between January 2019 and December 2022. Inclusion criteria were as follows: (1) age between 18 and 80 years, (2) positive serum hepatitis B virus surface antigen for more than 6 months, (3) availability of FibroScan data within 1 month prior to surgery, and (4) availability of pathology slides to assess hepatic steatosis and fibrosis. Exclusion criteria included: (1) chronic liver disease due to causes other than CHB, (2) serum alanine aminotransferase (ALT) activity exceeding five times the upper normal limit (>200 U/L), (3) previous liver transplantation, or (4) unreliable FibroScan results.

Clinical information collected from these patients included body mass index (BMI), mean daily alcohol intake, past medical history, and laboratory data. The laboratory data included results from liver function tests, such as serum concentrations of ALT, aspartate aminotransferase, total bilirubin, and albumin, as well as coagulation parameters like prothrombin time/international normalized ratio (PT-INR). Additionally, details regarding tumors, including their pathology, size, and location, were also obtained.

Transient Elastography

At the authors’ hospital, most patients undergo routine TE prior to major hepatectomy, based on clinicians' assessments to predict postoperative outcomes. Before conducting the TE, radiology technicians utilize a gray-scale US system (Aplio i800, Canon Medical Systems, Otawara, Japan) equipped with a 1-8 MHz convex probe (PVI-475BX). This equipment is located in the same room as the FibroScan device and is used to measure the skin-to-capsule distance (SCD) and to check for the presence of ascites and tumors in the area where liver stiffness measurement (LSM) is planned. TE examinations are generally contraindicated, and the use of 2-dimensional shear wave elastography is recommended for LSM in cases where the SCD exceeds 25 mm, or when ascites and tumors are present, as these conditions can interfere with the proper TE examination [23].

TE measurements of liver stiffness and CAP were obtained using a FibroScan device (Echosens, Paris, France). The scans were conducted by one of two radiology technicians, each with over 1,000 TE procedures annually for at least two years, adhering to the guidelines set by the World Federation for Ultrasound in Medicine and Biology [24,25]. Due to the unavailability of the XL probe at the authors’ affiliated institution during the study period, all measurements were taken using the M probe. LSM and CAP values were reported in kPa and dB/m, respectively. For each patient, the representative LSM and CAP values were calculated as the median of at least 10 successful measurements. Values were deemed unreliable if the success rate was below 60%, or if the interquartile range to median ratio (interquartile range [IQR]/m) exceeded 30%.

Histologic Evaluation

Liver specimens were collected from each patient during liver resection. These specimens were preserved in formalin, embedded in paraffin, and sectioned into 4-mm-thick slices for hematoxylin-eosin staining. The stained specimens were then evaluated by one of two expert pathologists (H.J.K. and I.H.S.), who were blinded to clinical and radiologic information. The histologic hepatic fat fraction (HFF), which serves as a measure of steatosis, was visually assessed based on the percentage of liver parenchyma occupied by fat droplets in the stained specimens. The degree of steatosis was classified using a grading system developed by the Nonalcoholic Steatohepatitis Clinical Research Network, with HFFs of <5%, 5%-33%, 34%-66%, and >66% corresponding to grades S0, S1, S2, and S3, respectively [26]. Steatosis was considered significant if the HFF was 10% or higher, a threshold that is used in donor selection criteria for liver transplantation [27-29] and has been employed previously to assess the diagnostic performance of US-based fat quantification [21]. The fibrosis stage for each patient was determined according to the METAVIR scoring system, which categorizes fibrosis as F0 (no fibrosis), F1 (portal fibrosis without septa), F2 (portal fibrosis with few septa), F3 (portal fibrosis with numerous septa without cirrhosis), or F4 (cirrhosis) [30].

Statistical Analysis

Quantitative variables were expressed as mean±standard deviation or median and IQR, as appropriate. Qualitative variables were presented as numbers and percentages. The correlation between CAP and HFF was evaluated using Pearson’s correlation coefficient. Differences between correlation coefficients were analyzed using bootstrapping with 1,000 iterations. The Mann-Whitney U test was used to compare CAP values based on HFF results. The areas under the receiver operating curve (AUCs) were calculated to assess the diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) across different stages of histologic hepatic fibrosis using DeLong’s test. Similarly, the diagnostic perfomance of CAP to detect HFF ≥5% was evaluated according to histologic hepatic fibrosis stages. Additionally, AUCs for CAP were determined to identify significant hepatic steatosis (HFF ≥10%) based on LSM values, which serve as surrogate markers for hepatic fibrosis. For LSM values, cutoffs of 7.53 kPa, 9 kPa, and 12 kPa were selected, corresponding to advanced fibrosis [31,32]. Using these LSM values, dual cutoff values for CAP were established to diagnose significant hepatic steatosis (HFF ≥10%) with 90% sensitivity to rule out and 90% specificity to rule in hepatic steatosis. Factors significantly influencing CAP were identified through univariable and multivariable linear regression analyses using backward elimination. Statistical analyses were conducted using SPSS version 25 (IBM Corp., Armonk, NY), R version 4.3.1 (The R Foundation for Statistical Computing, Vienna, Austria), and MedCalc version 15.2 software (MedCalc Software, Ostend, Belgium). P-values less than 0.05 were considered statistically significant.

Results

Patient Characteristics

A search of electronic medical records identified 437 patients with CHB who underwent both FibroScan and liver resection between January 2019 and December 2022. Of these, 38 were excluded due to one having concurrent chronic hepatitis C and 37 presenting unreliable FibroScan results. The study ultimately included 399 patients, comprising 306 men (76.7%) and 93 women (23.3%), with a median age of 59 years (IQR, 53 to 64 years) (Fig. 1). The median SCD value among these patients was 17.3 mm (IQR, 15.1 to 19.1 mm), with a range from 10.6 to 28.9 mm. Only four cases (1.0%, 4/399) had SCD values exceeding 25 mm. The BMI of 18 patients exceeded 30 kg/m². Fifteen patients (3.8%) had mildly elevated bilirubin levels, ranging from 1.3 to 1.7 mg/dL. However, none of these patients showed significant bile duct dilatation in preoperative imaging studies. Among the 399 patients, 65 (16.3%) exhibited significant steatosis (HFF ≥10%), with HFF values across all patients ranging from 0 to 60%. The evaluation of hepatic fibrosis stages revealed F0 or F1 in 39 patients (9.8%), F2 in 79 (19.8%), F3 in 117 (29.3%), and F4 in 164 (41.1%). The demographic and clinical characteristics of the study population, along with their laboratory data, are described in Table 1.

In total, 91.5% (356/399) of the resected tumors were hepatocellular carcinomas, while 6.0% (24/399) were cholangiocarcinomas. The median tumor size was 2.6 cm. Although 66.1% (264/399) of the tumors were located in the right lobe of the liver, TE measurements were meticulously conducted to avoid the tumor area, using gray-scale US prior to the TE measurements. Details regarding the tumors are provided in Supplementary Table 1.

CAP and Histopathologic HFF

For each of the 399 patients, at least 10 valid measurements of both CAP and LSM were obtained. The median CAP was 231 dB/m, ranging from 126 to 367 dB/m. CAP values were significantly higher in patients with an HFF ≥10% (274 dB/m; IQR, 240 to 303 dB/m) compared to those with an HFF <10% (222 dB/m; IQR, 199 to 250 dB/m) (P<0.001). There was a significant positive correlation between CAP and histopathologic HFF (r=0.432, P<0.001). Interestingly, the correlation coefficient was lower in patients with F2-4 (r=0.443, P<0.001) than in those with F0-1 (r=0.527, P<0.001). However, this difference was not statistically significant (0.084; 95% confidence interval [CI], -0.178 to 0.295; P=0.501).

The Diagnostic Performance of CAP for Detecting Hepatic Steatosis

The AUC of CAP for detecting significant steatosis in all patients was 0.786 (95% CI, 0.726 to 0.845) (Fig. 2A). However, it tended to decrease with higher fibrosis stages. This trend is illustrated by the comparisons between fibrosis stages F0-1 (0.924; 95% CI, 0.835 to 1.000) and F2-4 (0.772; 95% CI, 0.708 to 0.836), F0-2 (0.828; 95% CI, 0.704 to 0.952) and F3-4 (0.772; 95% CI, 0.708 to 0.836), and F0-3 (0.799; 95% CI, 0.728 to 0.871) and F4 (0.755; 95% CI, 0.650 to 0.860) (Fig. 2B-D). The AUC of CAP for detecting significant steatosis was significantly lower in patients with F2-4 than in those with F0-1 fibrosis stages (P=0.006). However, there were no significant differences in the AUCs of CAP between fibrosis stages F0-2 and F3-4 (P=0.368) or between F0-3 and F4 (P=0.471). The AUCs of CAP were also assessed using an HFF cutoff of ≥5%. Although a trend toward lower AUCs was observed with increasing hepatic fibrosis, it did not reach statistical significance (Supplementary Fig. 1).

The diagnostic performance of CAP was examined using LSM values as surrogate markers for hepatic fibrosis (Supplementary Table 2). The groups with higher LSM values generally exhibited lower AUC compared to those with lower LSM values, despite the lack of statistical significance. CAP cutoff values for ruling out and ruling in, based on LSM values, are detailed in Supplementary Table 2.

Factors Associated with CAP

Univariable linear regression analysis revealed significant associations between CAP and several variables, including BMI, ALT, albumin concentrations, PT-INR, and HFF. However, LSM and hepatic fibrosis did not show a significant relationship with CAP (Table 2). Subsequent multivariable linear regression analysis identified BMI (P<0.001) and HFF (P<0.001) as significant independent predictors of CAP.

Discussion

The present study revealed a positive correlation between CAP and HFF (r=0.445, P<0.001) in patients with CHB. However, the correlation coefficients were lower in patients with fibrosis stages F2-4 (r=0.443) compared to those with stages F0-1 (r=0.527). The ability of CAP to diagnose significant hepatic steatosis was notably lower in patients with significant or advanced hepatic fibrosis (F2-4) than in those with no or mild fibrosis (F0-1) (AUC, 0.924 vs. 0.772; P=0.006). Both BMI and HFF (both P<0.001) significantly influenced CAP, while hepatic fibrosis stages did not.

CAP has been shown to correlate strongly with the histopathologic determination of hepatic steatosis in patients with chronic liver diseases in previous research [15,33]. The present study further confirmed a significant correlation between CAP and the histopathologic assessment of hepatic steatosis in patients with CHB, as shown in both univariable and multivariable linear regression analyses.

Interestingly, the correlation between CAP and HFF was lower in CHB patients with significant or more advanced hepatic fibrosis (F2-4) than in those with no or mild fibrosis (F0-1). Similarly, a prospective study found that the correlation between US-based attenuation values and MRI-proton density fat fraction (PDFF) was weaker in patients with severe fibrosis compared to those without severe fibrosis [34]. This diminished correlation may be attributed to fibrosis-induced changes in the US attenuation of the liver [16,17]. Consistent with these observations, the current study also demonstrated that the diagnostic performance of CAP values declined as fibrosis stage increased in patients with CHB. Specifically, CAP values were less reliable for detecting significant hepatic steatosis in patients with significant or more advanced fibrosis (F2-4) than in those with no or mild fibrosis (F0-1). Few studies have explored the relationship between the diagnostic performance of CAP and fibrosis stage, limiting analysis of how hepatic fibrosis affects US quantification of steatosis [18-22]. Moreover, unlike previous studies that included patients with various etiologies of liver disease and only a small number with advanced fibrosis, all participants in the current study had CHB-associated chronic liver disease, and a relatively high number had significant or more advanced fibrosis (F2-4). Therefore, the findings of this study provide more relevant evidence concerning the impact of hepatic fibrosis on TE staging of hepatic steatosis in patients with CHB.

Although this study primarily focused on the effects of histologically determined hepatic fibrosis on the diagnostic performance of CAP, LSM values measured using TE may serve as a practical surrogate marker for hepatic fibrosis in real-world clinical settings. In the dataset for this study, similar to histologically determined hepatic fibrosis, groups with higher LSM values tended to have lower AUCs compared to groups with lower LSM values. To minimize the impact of hepatic fibrosis on the ultrasound quantification of hepatic steatosis, as confirmed in this study, further research is needed to propose different CAP cutoff values based on LSM values. This should involve a large cohort of patients and include a separate validation set.

This study, however, could not determine the mechanism underlying the impact of hepatic fibrosis on the diagnostic performance of CAP. The multivariable analysis showed that only HFF and BMI were significantly associated with CAP values, whereas the histologic fibrosis stage and LSM were not. The association between BMI and CAP values may be related to the higher likelihood of concurrent hepatic steatosis in obese patients [35]. Conversely, hepatic fibrosis might indirectly affect CAP readings, as fibrotic tissue could alter the attenuation of ultrasound beams [16,17]. However, further studies are necessary to provide a clearer explanation of this phenomenon.

The present study had several limitations. First, all examinations were conducted using the M probe, as the XL probe was unavailable at the authors’ institution during the study period. Despite the predetermined contraindication for TE examinations using the M probe, the SCD exceeded 25 mm in 1.0% (4/399) of the population, while the BMI exceeded 30 kg/m² in 4.5% (18/399) of patients. Notably, the BMI exceeded 30 kg/m² in 10.8% (4/37) of patients with unreliable TE measurements. Although these patients represented only a small fraction of the entire study population and likely did not significantly deviate from the indications for using the M probe, employing the XL probe could yield different results. Second, few patients in this study had notably high BMIs, limiting a more thorough evaluation of the influence of BMI on CAP. In patients with steatotic liver disease, the accuracy of CAP was found to be lower in those with a BMI >30 kg/m² than in those with a BMI ≤30 kg/m² [36]. However, in this study, only 18 of 399 patients (4.5%) had BMIs >30 kg/m², which restricted a more comprehensive assessment of the impact of BMI on CAP and indicated the need for additional studies that include higher percentages of patients with high BMI. Third, fatty components in the liver exhibit a propensity for rapid alterations [37]. Although this study included patients who had undergone TE within 1 month prior to surgery, hepatic steatosis may have changed during the interval. Fourth, 15 patients (3.8%) exhibited mildly elevated bilirubin levels ranging from 1.3 to 1.7 mg/dL without notable bile duct dilatation on preoperative imaging studies. Cholestasis related to bile duct obstruction can affect the results of TE measurements [38]. Although a proportion of the study population exhibited elevated bilirubin levels, the effects would be minimal, given the level of bilirubin and the absence of bile duct dilatation. Lastly, HFF, rather than the PDFF on MRI, was used as a reference for CAP. The interpretation of fat fraction in histopathology can be influenced by the subjective interpretation of pathologists and is limited by sampling variability and inter- and intra-observer variability [10]. Nonetheless, histopathologic examinations remain the gold standard method for evaluating hepatic steatosis and liver fibrosis, although MRI PDFF is increasingly employed to quantify liver fat [39].

In conclusion, CAP is less reliable for evaluating significant hepatic steatosis in CHB patients with significant or more advanced hepatic fibrosis than in those with no or mild hepatic fibrosis. Results obtained from using TE with CAP to quantify hepatic steatosis in patients with severe fibrosis should be interpreted with caution.

Notes

Author Contributions

Conceptualization: Kim SY. Data acquisition: Park HJ, Yoon S. Data analysis or interpretation: Park HJ, Kang HJ, Kim SY, Baek S, Song IH. Drafting of the manuscript: Park HJ. Critical revision of the manuscript: Kim SY, Baek S, Jang HJ, Jang JK. Approval of the final version of the manuscript: all authors.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Supplementary Material

Supplementary Table 1.

Tumor data (https://doi.org/10.14366/usg.24138).

usg-24138-Supplementary-Table.pdf

Supplementary Table 2.

Diagnostic performance of CAP according to LSM values and cutoff values (https://doi.org/10.14366/usg.24138).

usg-24138-Supplementary-Table.pdf

Supplementary Fig. 1.

Areas under the receiver operating curve of the controlled attenuation parameter for diagnosing hepatic fat fraction ≥5% in patients with fibrosis grades F0–1 and F2–4 (0.805 vs. 0.766, P=0.624) (A), patients with F0–2 and F3–4 (0.820 vs. 0.747, P=0.169) (B), and patients with F0–3 and F4 (0.773 vs. 0.766, P=0.888) (C) (https://doi.org/10.14366/usg.24138).

usg-24138-Supplementary-Fig-1.pdf

Appendix

[IMAGE OMITTED: SEE PDF]

[IMAGE OMITTED: SEE PDF]

[IMAGE OMITTED: SEE PDF]

Table 1.

Demographic and clinical characteristics of the included patients

Characteristic	Value
Age (year)	59 (53-64)
Sex
Male	306 (76.7)
Female	93 (23.3)
BMI (kg/m2)	24.1 (22.5-26.1)
Alcohol intake (g/day)	3.4 (0.0-24.5)
Hypertension	148 (37.1)
Diabetes mellitus	99 (24.8)
Dyslipidemia	23 (5.7)
AST (IU/L)	27.0 (22.0-34.0)
ALT (IU/L)	22.0 (16.0-33.0)
Total bilirubin (mg/dL)	0.5 (0.4-0.7)
Albumin (g/dL)	3.8 (3.6-4.0)
PT-INR	1.03 (0.99-1.06)
Histopathologic HFF	3.0 (1.0-5.0)
Histopathologic hepatic steatosis grade
S0 (<5%)	267 (66.9)
S1 (5%-33%)	124 (31.1)
S2 (34%-66%)	8 (2.0)
S3 (>66%)	0
Histopathologic hepatic fibrosis stage
F0	13 (3.3)
F1	26 (6.5)
F2	79 (19.8)
F3	117 (29.3)
F4	164 (41.1)

Values are presented as median (IQR) or number of patients (%).

BMI, body mass index; AST, aspartate transaminase; ALT, alanine aminotransferase; PT-INR, prothrombin time–international normalized ratio; HFF, hepatic fat fraction; IQR, interquartile range.

Table 2.

Univariable and multivariable analyses of factors affecting CAP

Factor	Univariable analysis ------------			Multivariable analysis ------------
	Coefficient	95% CI	P-value	Coefficient	95% CI	P-value
Age	-0.070	-0.552 to 0.413	0.777
Sex	7.742	-2.700 to 18.184	0.146
BMI	6.640	5.324 to 7.956	<0.001	4.888	3.561 to 6.215	<0.001
ALT	0.392	0.151 to 0.633	0.002
Total bilirubin	13.325	-1.476 to 28.126	0.078
Albumin	14.460	1.806 to 27.114	0.025
PT-INR	-72.825	-140.091 to -5.559	0.034
HFF	2.392	1.899 to 2.885	<0.001	1.801	1.311 to 2.290	<0.001
LSM	0.183	-0.419 to 0.786	0.550
Fibrosis stage	0.135	-3.971 to 4.240	0.949

CAP, controlled attenuation parameter; CI, confidence interval; BMI, body mass index; ALT, alanine aminotransferase; PT-INR, prothrombin time–international normalized ratio; HFF, hepatic fat fraction; LSM, liver stiffness measurement;.

References

1. GBD 2019 Hepatitis B Collaborators. Global, regional, and national burden of hepatitis B, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Gastroenterol Hepatol 2022;7:796–829.

2. Wong GL. Prediction of fibrosis progression in chronic viral hepatitis. Clin Mol Hepatol 2014;20:228–236.

3. Tang LS, Covert E, Wilson E, Kottilil S. Chronic hepatitis B infection: a review. JAMA 2018;319:1802–1813.

4. Kumar R, Boon-Bee Goh G. Chronic hepatitis B and fatty liver: issues in clinical management. Clin Res Hepatol Gastroenterol 2016;40:755–759.

5. Gordon A, McLean CA, Pedersen JS, Bailey MJ, Roberts SK. Hepatic steatosis in chronic hepatitis B and C: predictors, distribution and effect on fibrosis. J Hepatol 2005;43:38–44.

6. Shi YW, Yang RX, Fan JG. Chronic hepatitis B infection with concomitant hepatic steatosis: current evidence and opinion. World J Gastroenterol 2021;27:3971–3983.

7. Mak LY, Hui RW, Fung J, Liu F, Wong DK, Cheung KS, et al. Diverse effects of hepatic steatosis on fibrosis progression and functional cure in virologically quiescent chronic hepatitis B. J Hepatol 2020;73:800–806.

8. Vernon G, Baranova A, Younossi ZM. Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Aliment Pharmacol Ther 2011;34:274–285.

9. Bravo AA, Sheth SG, Chopra S. Liver biopsy. N Engl J Med 2001;344:495–500.

10. Ratziu V, Charlotte F, Heurtier A, Gombert S, Giral P, Bruckert E, et al. Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology 2005;128:1898–1906.

11. Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology 2023;77:1797–1835.

12. European Association for the Study of the Liver. EASL 2017 Clinical practice guidelines on the management of hepatitis B virus infection. J Hepatol 2017;67:370–398.

13. Friedrich-Rust M, Ong MF, Martens S, Sarrazin C, Bojunga J, Zeuzem S, et al. Performance of transient elastography for the staging of liver fibrosis: a meta-analysis. Gastroenterology 2008;134:960–974.

14. Terrault NA, Lok AS, McMahon BJ, Chang KM, Hwang JP, Jonas MM, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology 2018;67:1560–1599.

15. Shi KQ, Tang JZ, Zhu XL, Ying L, Li DW, Gao J, et al. Controlled attenuation parameter for the detection of steatosis severity in chronic liver disease: a meta-analysis of diagnostic accuracy. J Gastroenterol Hepatol 2014;29:1149–1158.

16. Park J, Lee JM, Lee G, Jeon SK, Joo I. Quantitative evaluation of hepatic steatosis using advanced imaging techniques: focusing on new quantitative ultrasound techniques. Korean J Radiol 2022;23:13–29.

17. Kumada T, Toyoda H, Ogawa S, Gotoh T, Suzuki Y, Imajo K, et al. Advanced fibrosis leads to overestimation of steatosis with quantitative ultrasound in individuals without hepatic steatosis. Ultrasonography 2024;43:121–131.

18. Seto WK, Hui RW, Mak LY, Fung J, Cheung KS, Liu KS, et al. Association between hepatic steatosis, measured by controlled attenuation parameter, and fibrosis burden in chronic hepatitis B. Clin Gastroenterol Hepatol 2018;16:575–583.

19. Cardoso AC, Beaugrand M, de Ledinghen V, Douvin C, Poupon R, Trinchet JC, et al. Diagnostic performance of controlled attenuation parameter for predicting steatosis grade in chronic hepatitis B. Ann Hepatol 2015;14:826–836.

20. Shen F, Zheng RD, Mi YQ, Wang XY, Pan Q, Chen GY, et al. Controlled attenuation parameter for non-invasive assessment of hepatic steatosis in Chinese patients. World J Gastroenterol 2014;20:4702–4711.

21. Bae JS, Lee DH, Lee JY, Kim H, Yu SJ, Lee JH, et al. Assessment of hepatic steatosis by using attenuation imaging: a quantitative, easy-to-perform ultrasound technique. Eur Radiol 2019;29:6499–6507.

22. Mi YQ, Shi QY, Xu L, Shi RF, Liu YG, Li P, et al. Controlled attenuation parameter for noninvasive assessment of hepatic steatosis using Fibroscan(R): validation in chronic hepatitis B. Dig Dis Sci 2015;60:243–251.

23. Berger A, Shili S, Zuberbuhler F, Hiriart JB, Lannes A, Chermak F, et al. Liver stiffness measurement with FibroScan: use the right probe in the right conditions! Clin Transl Gastroenterol 2019;10:e00023.

24. Ferraioli G, Filice C, Castera L, Choi BI, Sporea I, Wilson SR, et al. WFUMB guidelines and recommendations for clinical use of ultrasound elastography: Part 3: liver. Ultrasound Med Biol 2015;41:1161–1179.

25. Ferraioli G, Wong VW, Castera L, Berzigotti A, Sporea I, Dietrich CF, et al. Liver ultrasound elastography: an update to the World Federation for Ultrasound in Medicine and Biology guidelines and recommendations. Ultrasound Med Biol 2018;44:2419–2440.

26. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313–1321.

27. Sharma A, Ashworth A, Behnke M, Cotterell A, Posner M, Fisher RA. Donor selection for adult-to-adult living donor liver transplantation: well begun is half done. Transplantation 2013;95:501–506.

28. Pascher A, Sauer IM, Walter M, Lopez-Haeninnen E, Theruvath T, Spinelli A, et al. Donor evaluation, donor risks, donor outcome, and donor quality of life in adult-to-adult living donor liver transplantation. Liver Transpl 2002;8:829–837.

29. Neil DA, Minervini M, Smith ML, Hubscher SG, Brunt EM, Demetris AJ. Banff consensus recommendations for steatosis assessment in donor livers. Hepatology 2022;75:1014–1025.

30. Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 1996;24:289–293.

31. Xiao H, Shi M, Xie Y, Chi X. Comparison of diagnostic accuracy of magnetic resonance elastography and Fibroscan for detecting liver fibrosis in chronic hepatitis B patients: a systematic review and meta-analysis. PLoS One 2017;12:e0186660.

32. Ferraioli G, Roccarina D. Update on the role of elastography in liver disease. Therap Adv Gastroenterol 2022;15:17562848221140657.

33. Seo JW, Kim YR, Jang JK, Kim SY, Cho YY, Lee ES, et al. Transient elastography with controlled attenuation parameter versus two-dimensional shear wave elastography with attenuation imaging for the evaluation of hepatic steatosis and fibrosis in NAFLD. Ultrasonography 2023;42:421–431.

34. Jeon SK, Lee JM, Joo I, Yoon JH, Lee DH, Lee JY, et al. Prospective evaluation of hepatic steatosis using ultrasound attenuation imaging in patients with chronic liver disease with magnetic resonance imaging proton density fat fraction as the reference standard. Ultrasound Med Biol 2019;45:1407–1416.

35. Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology 2010;51:679–689.

36. Cao YT, Xiang LL, Qi F, Zhang YJ, Chen Y, Zhou XQ. Accuracy of controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) for assessing steatosis and fibrosis in non-alcoholic fatty liver disease: a systematic review and meta-analysis. EClinicalMedicine 2022;51:101547.

37. Jiang H, Chen HC, Lafata KJ, Bashir MR. Week 4 liver fat reduction on MRI as an early predictor of treatment response in participants with nonalcoholic steatohepatitis. Radiology 2021;300:361–368.

38. Millonig G, Reimann FM, Friedrich S, Fonouni H, Mehrabi A, Buchler MW, et al. Extrahepatic cholestasis increases liver stiffness (FibroScan) irrespective of fibrosis. Hepatology 2008;48:1718–1723.

39. Qadri S, Vartiainen E, Lahelma M, Porthan K, Tang A, Idilman IS, et al. Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: a meta-analysis. JHEP Rep 2024;6:100928.