Assessment of Hepatic Steatosis Using Ultrasound-Based Techniques: Focus on Fat Quantification

Rou, Woo Sun; Woo Sun Rou

doi:10.18525/cu.2024.9.1.1

Clin Ultrasound > Volume 9(1); 2024 > Article

Rou: Assessment of Hepatic Steatosis Using Ultrasound-Based Techniques: Focus on Fat Quantification

Review

Clinical Ultrasound 2024; 9(1): 1-17.

Published online: May 30, 2024

DOI: https://doi.org/10.18525/cu.2024.9.1.1

Assessment of Hepatic Steatosis Using Ultrasound-Based Techniques: Focus on Fat Quantification

Woo Sun Rou^1,²

¹Department of Internal Medicine, Chungnam National University School of Medicine, Daejeon, Korea

²Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chungnam National University Sejong Hospital, Sejong, Korea

Address for Correspondence: Woo Sun Rou, M.D., Ph.D. Department of Internal Medicine, Chungnam National University School of Medicine, 266 Munhwa-ro, Jung-gu, Daejeon 35015, Korea Tel: +82-44-995-4770, Fax: +82-44-995-3329 E-mail: rws00@cnuh.co.kr

Received April 21, 2024 Revised May 9, 2024 Accepted May 9, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide, affecting more than 30% of the global population, and is associated with increased liver-related morbidity and mortality, and extrahepatic complications. With the increasing prevalence of MASLD, there is an urgent need for accessible, non-invasive methods to detect hepatic steatosis. Currently, liver biopsy and magnetic resonance imaging proton density fat fraction are considered reference standards for diagnosing hepatic steatosis; however, their invasiveness and limited accessibility limit their widespread use. Ultrasonography (US) is a promising alternative owing to its cost-effectiveness and widespread accessibility. Recently, quantitative US techniques have been developed and commercialized by several vendors to measure steatosis by detecting changes in various acoustic properties associated with hepatic steatosis, making it readily accessible. Controlled attenuation parameter (CAP), which evaluates hepatic steatosis using the attenuation of the US beam, is the most widely studied algorithm as a non-imaging technique. Several other algorithms are also available on B-mode ultrasound systems, and their diagnostic performance is comparable to or better than that of the CAP. Therefore, we aimed to review current US-based methods for detecting and grading hepatic steatosis, discussing their diagnostic performance and utilization.

Keywords: Ultrasound; Steatosis; Fatty liver; Quantification; Attenuation

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD), proposed as a new nomenclature for non-alcoholic fatty liver disease (NAFLD), is the most common cause of chronic liver disease worldwide [1]. MASLD results from fat accumulation in the liver and is associated with features of metabolic syndromes, including obesity and type 2 diabetes mellitus. Its prevalence is rising worldwide simultaneously with an increase in the prevalence of obesity and comorbid metabolic disease in more than 30% of the general population [2,3]. Liver biopsy remains the gold standard for diagnosing metabolic dysfunction-associated steatohepatitis (MASH) and plays an essential role in staging hepatic steatosis and fibrosis. According to Brunt et al., the degree of steatosis is classified as follows: grade 0, < 5% hepatocytes; grade 1, 5–33% steatosis; grade 2, 34–66% steatosis; and grade 3, > 66% steatosis [4]. However, in practice, it is difficult to perform a liver biopsy in all patients suspected of having MASLD because of high medical costs, sampling errors, low intra- and inter-observer repeatability, invasiveness (e.g., pain, bleeding, or infection), and the difficulty of repeated examination during treatment [5-8]. Moreover, hepatic steatosis is found in other chronic liver diseases, such as chronic hepatitis C, and is possibly associated with hepatic fibrosis progression [9]. Therefore, accurate measurement of liver fat content is crucial for liver donor selection because steatosis may be associated with liver regeneration and perigraft mortality [10,11].

Accordingly, there is an unmet need and growing interest in non-invasive assessment of steatosis. Conventional imaging modalities for assessing the hepatic fat content include B-mode ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Particularly, MRI proton density fat fraction (PDFF) has been accepted as a reference standard for quantifying hepatic fat content as an alternative to biopsy [12,13]. However, these modalities have some limitations: lower sensitivity in detecting low degrees of fat content and substantial inter-observer variability in conventional B-mode US; exposure to ionizing radiation in CT; and high cost and limited accessibility in MRI. Recently, new quantitative US (QUS) methods have been developed to quantitatively assess hepatic fat content which rely on the analysis of radiofrequency echoes detected by a transducer, enabling the calculation of parameters to quantify the fat content in the liver [14].

Given the lack of studies examining these new US modalities, this article aimed to review various US-based techniques, with a focus on the recently developed QUS for assessing hepatic fat content, providing an overview of the concepts, advantages, limitations, and diagnostic performance of these techniques.

CONVENTIONAL B-MODE US

Conventional B-mode US is commonly used to assess hepatic steatosis because of its cost-effectiveness, safety, and widespread availability [15]. It utilizes qualitative sonographic patterns which include increased hepatic parenchymal echogenicity, blurring of the normal echogenic walls of the portal and hepatic veins, posterior beam attenuation, and poor visualization of the diaphragm, to evaluate the hepatic fat infiltration severity [16]. Additionally, B-mode US can be used to grade steatosis extent based on the degree of liver brightening and/or blurring of the vessels and diaphragm. It is reportedly 90% sensitive in detecting steatosis in at least 20% of hepatocytes but less sensitive in detecting lower degrees of hepatic fat content [17]. Furthermore, conventional B-mode US has inter-observer variability, which may result in overestimation owing to beam attenuation by the overlying non-liver fat and confounded by fibrosis and inflammation [16,18,19].

SEMI-QUANTITATIVE US

The most popular semi-quantitative methods for assessing hepatic steatosis include the hepatorenal index (HRI), Hamaguchi score, ultrasonographic fatty liver index (US-FLI), and US fatty liver score [20]. HRI indirectly measures steatosis as the brightness ratio between the liver and the renal cortex [21]. To measure HRI, two regions of interest (ROIs) are placed in segment VI of the liver and right renal cortex on the same image (Fig. 1). The measurement points should be at the same distance from the transducer to minimize the attenuation difference with depth [21,22]. Various cutoff values of HRI for hepatic steatosis have been proposed, ranging from 1.17–2.2 [23-25]. However, HRI is not suitable for patients with advanced liver fibrosis or severe renal disease because of confounding interpretations and insufficient cortical thickness. Additionally, limitations including technical dependency, intervendor variability, and a lack of large amounts of clinical validation data make it difficult to widely utilize HRI.

The Hamaguchi score is based on four US findings: hepatic parenchymal brightness and hepatorenal echo contrast (0–3), deep attenuation (0–2), and vessel blurring (0–1). According to the Hamaguchi scoring system, hepatic steatosis is graded on a scale of up to six scores [26]. A score ≥ 2 had an area under the receiver operating characteristic curve (AUC) of 0.98, with 91.7% sensitivity and 100% specificity for diagnosing MASLD [26]. US-FLI and US fatty liver scores are semi-quantitative ultrasonographic scoring systems similar to the Hamaguchi score, with slight differences in imaging parameters and the sum of the scores. Although these scoring systems have demonstrated good diagnostic performance in detecting hepatic steatosis in several studies [27-29], they are limited owing to their insufficient reproducibility and potential to underestimate the severity of hepatic steatosis due to recent technological solutions with better US penetration.

QUS PARAMETERS FOR ASSESSING HEPATIC STEATOSIS

Recently, several new US techniques have been developed to quantitatively assess hepatic steatosis. In contrast to conventional B-mode US imaging, which provides qualitative information on hepatic steatosis, QUS aims to quantify the physical phenomena related to the propagation of US into biological tissues. QUS extracts fundamental tissue properties based on the interactions of US waves with the tissue microstructure using raw radiofrequency signals detected by the transducer, allowing for the calculation of parameters to quantify hepatic fat [14,30]. T hese QUS methods include measurement of the attenuation coefficient (AC), backscatter coefficient (BSC), and sound speed [14,31].

Attenuation

Attenuation refer to the acoustic energy loss of a signal resulting from the combination of absorption, reflection, refraction, scattering, and diffusion when an US wave passes through a medium. The presence of fat in the tissue increases the attenuation, causing a signal delay [31,32]. AC is a quantitative measure of acoustic energy loss in tissues and provides a quantitative parameter analogous to the obscuration of liver structures [33]. AC is expressed in dB/cm/MHz. Compared to biologic tissues, which typically have an AC ranging from 0.01–4 dB/cm/MHz, AC in the liver is expected to range from 0.43 to 1.26 dB/cm/MHz [33-36]. The thresholds for grading steatosis are not yet well-defined; therefore, vendor-specified thresholds are recommended. Recently, with improvements in the bandwidth, sensitivity, stability, and reproducibility of commercial systems, more calibration data from reference phantoms (RPs) have been integrated into imaging systems. This facilitates easier clinical implementation without the need for external calibration.

Backscatter

Backscatter refers to the reflection or scattering of US compression waves by tissues or structures [30]. The BSC is a quantitative measurement of the US energy returned from the tissue, defined as the differential scattering cross-section per unit volume in the 180º direction [30,37,38]. Generally, higher echogenicity and BSC are observed with hepatic fat infiltration [38]. Several experimental studies have demonstrated a positive correlation between backscatter and steatosis [39-41]. BSC measurement requires obtaining a reference BSC in units of cm-1 Sr-1 a t a s pecific f requency o r a t other f requencies w ithin t he transducer bandwidth. This is followed by a comparison of the radiofrequency signal of the insonified organ with that of the reference for calibration [30,40]. Unlike the almost linear variation in frequency observed in local attenuation, the BSC displays a more complex, tissue-specific frequency behavior, as mentioned above. Therefore, additional effort is required to calibrate the BSC of the RP at a specific frequency [30]. Recently, more robust estimates have been reported in vivo using a more standardized method to compute BSC with clinical array transducers [42,43].

Speed of sound

Speed of sound is another QUS parameter previously shown to be a biomarker of hepatic steatosis which is expressed in m/s [44,45]. In a healthy human liver, it ranges from approximately 1,538–1,588 m/s. However, fat accumulation in the liver leads to a significant decrease in the speed of sound, with values as low as 1,423–1,567 m/s in fatty liver [41,46-48]; thus, speed of sound has a strong negative correlation with hepatic fat content. Most US scanners assume a constant speed of sound of 1,540 m/s [49]. Although this assumption is demonstrably invalid for an average human, its utilization in image formation still yields reasonable image quality [48].

QUS AND ITS DIAGNOSTIC PERFORMANCE

Controlled attenuated parameter (CAP)

CAP is the most widely clinically studied proprietary algorithm that has been available since 2010, by transient elastography (FibroScan^®; EchoSens, Paris, France) using A-mode US (Fig. 2). The CAP has been validated in several studies [50-52]. In a previous study, CAP was measured simultaneously with vibration-controlled transient elastography using a 3.5-MHz standard probe (≥ 2.5 cm skin distance, XL probe and < 2.5 cm skin distance, M probe) [53]. Patients were positioned in the supine or dorsal decubitus position with their right arm fully abducted. The measurements were performed by scanning the right lobe of the liver through the intercostal space. The final CAP results were expressed in dB/m, ranging from 100–400 dB/m. Only examinations with at least 10 valid individual measurements with an interquartile range/median (IQR/M) < 30% or an IQR of CAP < 40 dB/m were deemed valid [51,54-56]. In a recent meta-analysis of 61 studies involving 10,537 adult patients with MASLD/MASH, good performance for steatosis was reported as AUCs of 0.924, 0.794, and 0.778 for steatosis grades ≥ S1, ≥ S2, and = S3, respectively [50]. The cutoff values for steatosis grades ≥ S1, ≥ S2, and = S3 were 302, 331, and 337 dB/m, respectively [50]. However, the cutoff values used for grading steatosis vary with the etiology of the liver disease, body mass index, and population [50,52]. In two other meta-analyses of MASLD, the CAP was unable to accurately grade hepatic steatosis [52,57]. The CAP showed inferior diagnostic performance to that of MRI-based assessment in diagnosing steatosis grade ≥ S1 (AUC 0.77 vs. 0.99) and grading hepatic steatosis (AUC 0.88, 0.73, 0.70 vs. 0.96, 0.90, 0.79 for steatosis grades ≥ S1, ≥ S2, and = S3, respectively) [58,59]. The diagnostic ability of CAP was particularly inferior to that of MRI‑PDFF and decreased with increasing BMI compared to MRI‑PDFF [60].

B-mode guided QUS

B-mode guided US for measuring hepatic fat content allows visualization of the area to be sampled while avoiding artifacts. Moreover, it can be used for morphological evaluation of the liver, assessment of portal hemodynamics using Doppler US, and evaluation of liver stiffness using two-dimensional shear wave elastography. The most studied acoustic parameters for quantifying hepatic steatosis are AC, BSC, and speed of sound; a combination of these parameters has also been used. These QUS methods of measuring the AC, depending on the vendor, include attenuation imaging (ATI), attenuation measurement (ATT), US-guided attenuation parameter (UGAP), tissue attenuation imaging (TAI), liver fat quantification (LFQ), and so on. Additionally, they allow for the simultaneous measurement of AC and backscatter, and include composite quantitative techniques like US-derived fat fraction (UDFF) and US fat fraction (USFF). The results of the studies using B-mode guided QUS are classified according to quantitative techniques and summarized in Table 1.

Although the detailed process for estimating hepatic steatosis differs slightly among vendors, the general measurement process is as follows: patients are positioned in the supine or slightly left lateral decubitus position. Intercostal scanning is performed using a convex probe to visualize the right hepatic lobe. With a midinspiration breath-hold, a large ROI is positioned in the hepatic parenchyma at least 2 cm away from the liver capsule with the transducer perpendicular to the skin, avoiding large vessels and masses. The loss of acoustic magnitude over a specific depth range at a particular frequency is calculated as dB/cm/MHz. According to the manufacturer’s recommendations, 5–10 measurements are obtained, except for UDFF (one measurement), and the median or average values is considered the final result [31,61]. Measurement reliability varies depending on the method used to estimate AC. For AT, a reliability index (R2 value) of ≥ 0.6–0.9 is considered valid [36,62,63], whereas for LFQ and UGAP, a valid measurement is indicated by an IQR/M value < 30% [64].

ATI

ATI is the most studied method for measuring AC, which is implanted in the Aplio i-series machines (Canon Medical Systems, Otawara, Japan) (Fig. 3A). The ATI quantifies the degree of US beam attenuation using the adjusted echo intensity, eliminating the focus-dependent beam profile and the compensated gain profile from the original signal [65]. The correlation coefficients range from 0.47–0.72 [62,66-70] when compared to the degree of histological steatosis as a reference standard and from 0.51–0.89 [35,36,65,70-73] when compared to MRI-PDFF. The AUCs for steatosis grades ≥ S1, ≥ S2, and = S3 ranged from 0.75–0.98, 0.82–0.96, and 0.79–0.95, respectively, indicating good diagnostic performance for grading hepatic steatosis. The cutoff values were 0.62–0.67, 0.64–0.74, and 0.68– 0.91 for grading hepatic steatosis ≥ S1, ≥ S2, and = S3, respectively [62,67,69,71,73-80]. A recent meta-analysis of studies conducted with the AC algorithm in 1,509 patients, reported pooled sensitivity and specificity of 76% and 84%, respectively, for ≥ S1, as well as 87% and 79%, respectively, for ≥ S2 [81]. In 2022, Bae et al. [82] compared the diagnostic performance of conventional B-mode US, CT, MRI-PDFF, CAP, and ATI in detecting steatosis grade ≥ S1 using histology as a reference standard. The MRI-PDFF showed the highest performance, with an AUC of 0.946, while ATI was the second-best modality, with an AUC of 0.892 (compared to CAP [AUC of 0.829], CT [AUC of 0.807], conventional B-mode US [AUC of 0.761]). For steatosis grade ≥ S2, all imaging modalities demonstrated a good diagnostic performance with no significant differences (AUCs of 0.947, 0.914, 0.914, 0.900, and 0.887 for MRI-PDFF, ATI, grayscale US, CAP, and CT, respectively).

UGAP

The UGAP is calculated from the slope based on the measured liver signal and the RP signal. It utilizes an US phantom with known attenuation and BSCs to compensate for the characteristics of transmission and reception beamforming [83]. The UGAP is available in the Logic E10 series and P10 (GE Healthcare, New York, USA) (Fig. 3B). In studies using biopsy as a reference standard for grading steatosis, correlation coefficients ranged from 0.70–0.81 [64,84,85]. However, they ranged from 0.50–0.80 when using MRI-PDFF [83,86,87]. The cutoff values for grading steatosis as ≥ S1, ≥ S2, and = S3 were 0.53–0.70, 0.60–0.74, and 0.65–0.77, with corresponding AUCs of 0.89–0.95, 0.87–0.95, and 0.82–0.96 respectively [77,83-87]. In 2023, Kang et al. [64] compared the diagnostic performance of MRI-PDFF, conventional B-mode US, CAP, and UGAP in detecting > 5% steatosis using histology as a reference standard. The AUC of ATI (0.821) was found to be similar to MRI-PDFF (AUC, 0.829) but slightly higher than those of CAP (0.788) and B-mode US (0.766). Additionally, ATI ( AUC, 0 .796) s howed t he s econd h ighest d iagnostic performance for detecting steatosis grade ≥ S2. However, its diagnostic performance was lower than that of the MRI-PDFF (AUC 0.971).

ATT

The ATT is determined by transmitting two ultrasonic waves of different frequencies along the same beam line and calculating the slope of the obtained signal ratio [88]. ATT is available in Fujifilm US systems (Fujifilm, Tokyo, Japan). When using biopsy as the reference standard and PDFF, the correlation coefficient was 0.47 and 0.80 [88,89], respectively. The AUCs for steatosis grades ≥ S1, ≥ S2, and = S3 were 0.74–0.93, 0.87–0.96, and 0.90–0.96, with cutoff values of 0.62–0.68, 0.67–0.74, and 0.73–0.78, respectively [88-90].

TAI

TAI quantifies attenuation based on the slope of the US central frequency downshift along the depth [91,92]. TAI is available in the Samsung Medison US systems (Samsung Medison, Seoul, Korea) (Fig. 4B). When measuring AC using TAI, the correlation coefficients with MRI-PDFF and MRS as reference standards were 0.66–0.78 [91,93] and 0.71 [94], respectively. The AUCs for steatosis grades ≥ S1, ≥ S2 were 0.86–0.86 and 0.70–0.84, with cutoff values of 0.88–0.91 and 0.96–0.98, respectively [91,94].

LFQ

Philips developed the LFQ method for estimating AC, which is available for the Elite and Affiniti US systems (Philips, Amsterdam, Netherlands) (Fig. 3C). In studies using MRI-PDFF as a reference standard for grading steatosis, the correlation coefficient was 0.76–0.89 [95,96], with AUCs of 0.98 for steatosis grade ≥ S1, 0.96 for steatosis grade ≥ S2, and 0.95 for S3, with cutoff values of 0.63, 0.70, and 0.84, respectively [95].

Composite quantitative techniques (UDFF and USFF)

The individual quantitative parameters are affected by imprecisions in measurements, biological variability, and unmeasured confounders. Therefore, combining simultaneously measured quantitative parameters may enhance hepatic steatosis assessment [97]. QUS techniques utilizing attenuation and backscatter are now commercially available as UDFF and USFF.

UDFF is obtained by combining both the attenuation and BSC information to measure the hepatic fat content, displaying the result as a percentage. QUS measurements of the AC and BSC required an RP. Recently, the RP has been integrated into the US system using a fixed ROI. UDFF is available in the Acuson Sequoia US system (Siemens Healthineer, Erlangen, Germany) (Fig. 4A). UDFF showed a good correlation of 0.71 [98] with histologic steatosis as the reference standard and 0.79–0.87 [98-100] when compared to MRI-PDFF. In previous studies, the AUCs for steatosis grades ≥ S1, ≥ S2, and = S3 were 0.94, 0.88, and 0.83, with cutoff values of 8.1, 15.9, and 16.1, respectively [98]. Recently, UDFF has shown robust agreement with PDFF and good diagnostic performance. The AUCs for steatosis grades ≥ S1, ≥ S2, and = S3 were 0.90, 0.95, and 0.95, with cutoff values of 11.2, 13.6, and 17.2, respectively [100]. However, UDFF showed a bias towards slightly larger values than PDFF did, with intraand inter-operator variation increasing as hepatic steatosis increased [100]. Therefore, further optimization is necessary.

Similar to UDFF, USFF assesses hepatic fat content by measuring two QUS parameters: attenuation and backscatter. It measures AC using TAI and the scatter-distribution coefficient using TSI based on the shape parameter of the Nakagami distribution [91]. These two values are then combined to derive the USFF value. USFF is implemented in the Samsung Medison US systems (Samsung Medison) (Fig. 4B, C). When compared to MRI-PDFF as the reference standard, the correlation coefficient ranged from 0.799– 0.86 [101,102]. The AUCs for steatosis grades ≥ S1, ≥ S2, and = S3 were 0.92–0.97, 0.93–0.96, and 0.91–0.95, with cutoff values of 5.7–8.7, 14.1–14.9, and 16.0–16.7, respectively [101,102].

UDFF and USFF methods offer more practical and intuitive assessment of liver fat content. However, notably, this percentage does not directly correspond to the percentage of liver fat observed histologically.

Comparison among QUS

B-mode guided QUS has the advantage of a more precise measurement of hepatic fat content, along with simultaneous visualization of hepatic parenchyma, compared with CAP. Several studies have shown a better diagnostic performance of ATI and UGAP than that of CAP [77,85,103]; however, some results indicate that the two methods exhibit a similar diagnostic performance [68-70]. In a recent meta-analysis of 13 studies involving 1,422 patients, AC showed a tendency for higher sensitivity and AUC, compared with CAP, however, the difference was not significant. CAP has the advantage of simultaneously measuring liver steatosis and fibrosis, whereas QUS has a lower failure rate and performs better at identifying grade 3 steatosis [77,85,90].

The values measured by B-mode guided QUS from different vendors showed a strong correlation [77,104]. However, significant inter-platform variability were observed, making it difficult to interchangeably use the values measured on different US systems [105,106]. Accordingly, it is difficult to use different US systems for longitudinal follow-up of patients, as specific thresholds recommended by vendors are suggested.

Limitations of B-mode guided QUS and future directions

Despite the advantages of QUS, several barriers to its widespread use exist. The cutoff values for detecting and grading liver steatosis often differ among studies, even when based on algorithms from the same US system. These differences could result from variations in etiology and lack of a standardized measurement protocol. Previous studies using CAP have shown diverse cutoff values depending on the etiology of liver disease. Similarly, although the techniques for estimating hepatic steatosis may differ from CAP, all attenuation-based QUS methods cannot be exempt from this concern. Kubale et al. [100] showed that the cutoff values for detecting mild steatosis using UDFF showed variations compared to Dillman et al. [99] and Labyed et al. [98] The authors suggested that these variations, including AUC and cutoffs, may reflect differences in patient populations, US transducers, and other factors, and proposed that the optimal threshold also depends on specific clinical scenarios. Currently, most QUS studies evaluating hepatic steatosis have focused on patients with MASLD. Therefore, further investigation into QUS techniques for other populations, including those with chronic viral hepatitis, alcoholic liver disease, and the healthy population is necessary. The ROI size and depth also affects the AC values [78,107]. Moreover, several issues that have not yet been clearly defined include greater skin-to-capsule distance [100], number of measurements to be performed [108], respiration [92], patient position [109], fasting conditions [110], and hepatic fibrosis [31,34,62,65,66,80,111]. Recently, several studies have been conducted to standardize the measurement methods [110,112]. Ferraioli et al. [112] demonstrated high repeatability when measuring liver steatosis using ATI, ATT, and UDFF with a 3-cm ROI located 2 cm below the liver capsule.

CONCLUSION

With the prevalence of MASLD continually increasing, there is a growing demand for noninvasive, accurate, and reproducible techniques to estimate hepatic steatosis. QUS is simple, costeffective, and easy to measure. Additionally, it shows a good correlation with histology and MRI-PDFF values, with a diagnostic performance comparable to or better than that of CAP. In recent years, there have been significant advances in QUS technology, resulting in the development of commercially available algorithms for B-mode US systems. Furthermore, the capabilities of US scanners to estimate several quantitative parameters, such as compression and shear wave phenomena, have been steadily improving, leading to a range of techniques and metrics [14,113]. Several studies have attempted to enhance the accuracy of measuring hepatic steatosis by combining multiple parameters, such as the integrated BSC, the signal-to-noise ratio with the AC value, or by improving existing algorithms [89,114]. Moreover, a predictive model utilizing multiparametric US combining dispersion slope, normalized local variance, and other parameters has demonstrated high diagnostic performance for MASH [79,115-117]. However, there is a need to mitigate the variability in cutoff values, develop standardized protocols, and further investigate the effects of confounding factors. As commercialized QUS technology becomes more widely available, further research is required to enable its application not only for accurately measuring hepatic fat but also for diagnosing MASH and utilizing it as a surrogate marker for the effects of MASH-specific treatment.

Acknowledgments

The author is grateful to Philips Korea, Canon Medical Systems Korea, Siemens Healthineers Korea, Samsung Medison, and General Electric Healthcare Korea for supplying information and images for LFQ, ATI, UDFF, TAI plus TSI, and UGAP.

Figure 1.

The hepatorenal index (HRI) is measured in patients with fatty liver using the subcostal window. It is automatically calculated using the ultrasound (US) machine's embedded formula when regions of interest at the same depth in the kidney and liver parenchyma are placed. (A) Philips US system, (B) Samsung Medison US system. Alternatively, the HRI can be measured by dividing the mean values of the histograms measured in the liver and renal areas, respectively. (C) Cannon Medical US system; (B) and (C) images were provided by Samsung Medison and Cannon Medical Systems Korea, respectively.

Figure 2.

The controlled attenuation parameter (CAP, Echosens) is measured simultaneously on the same area with liver stiffness by vibration-controlled transient elastography. The interquartile range (IQR) reflects its measurement variability.

Figure 3.

Various quantitative ultrasound methods express the attenuation coefficient in dB/cm/MHz. (A) attenuation imaging (ATI), (B) ultrasound-guided attenuation parameter (UGAP), and (C) liver fat quantification (LFQ); (A) and (B) images were provided by Cannon Medical Systems Korea and General Electric Healthcare Korea, respectively.

Figure 4.

Two quantitative ultrasound methods assess hepatic fat content, using both attenuation and backscatter information, and report the result as a percentage. (A) The ultrasound-derived fat fraction (UDFF) directly displays the result as a percentage on the monitor. (B, C) After measuring TAI and TSI values using the same ROI, one can confirm the ultrasound fat fraction (USFF) value as a percentage through the ultrasound report; (A), (B), and (C) images were provided by Siemens Healthineers Korea and Samsung Medison, respectively.

Table 1.

Studies since 2020 analyzing the accuracy of QUS in the quantification of hepatic steatosis compared with LB and MRI-based techniques

Author	No	Participants	Steatosis grade	Technique	Reference standard	r	Steatosis grade	Cutoff	AUC	Sn	Sp
Sugimoto et al. [76]	111	NAFLD (≥ 20 years old): NAFL/NASH 19%/81%	S0/S1/S2/S3: 6%/49%/29%/16%	ATI	LB	n/a	≥ S1	0.67	0.88	75	100
							≥ S2	0.72	0.86	90	66
							≥ S3	0.86	0.79	61	85
Kuroda et al. [77]	105	NAFLD (18–80 years old): NAFL/NASH 21%/79%	S1/S2/S3: 49%/27%/25%	ATI	LB	n/a	≥ S1	0.640	0.88	82	45
							≥ S2	0.710	0.88	89	68
							≥ S3	0.750	0.91	89	81
Sugimoto et al. [78]	105	NAFLD: NAFL/NASH 19%/81%	S0/S1/S2/S3: 8%/48%/28%/17%	ATI	LB	n/a	≥ S1	n/a	0.75	n/a	n/a
							≥ S2	n/a	0.82	n/a	n/a
							≥ S3	n/a	0.85	n/a	n/a
Jang et al. [79]	132	NAFLD (20–85 years old): NAFL/NASH 34%/40%	S0/S1/S2/S3: 26%/42%/23%/10%	ATI	LB	n/a	≥ S1	0.62	0.94	85	97
							≥ S2	0.70	0.94	95	80
							≥ S3	0.78	0.94	100	83
Huang et al. [69]	60	NAFLD (≥ 18 years old)	S0/S1/S2/S3: 18%/28%/25%/28%	ATI	LB	0.72	≥ S1	0.671	0.97	86	100
							≥ S2	0.726	0.91	94	79
							≥ S3	0.757	0.77	94	61
Lee et al. [75]	102	Increased liver enzyme or NAFLD (20–85 years old): NAFL/NASH 25%/54%	S0/S1/S2/S3: 22%/35%/29%/14%	ATI	LB	n/a	≥ S1	0.64	0.93	75	95
							≥ S2	0.70	0.88	84	76
							≥ S3	0.73	0.83	86	69
Jesper et al. [67]	27	Diffuse liver disease (≥ 18 years old): NAFL/NASH 7%/30%	S0/S1/S2/S3: 48%/15%/15%/22%	ATI	LB	0.65	≥ S1	Not significant
							≥ S2	0.64	0.98	100	94
							≥ S3	0.68	0.98	100	86
Burgio et al. [66]	101	Mixed: NAFLD 40%	S0/S1/S2/S3: 42%/35%/12%/11%	ATI	LB	0.58	≥ S1	0.69	0.81	76	86
Burgio et al. [66]	101	Mixed: NAFLD 40%	S0/S1/S2/S3: 42%/35%/12%/11%	ATI	LB	0.58	≥ S2	0.72	0.89	96	74
Yuri et al. [80]	328	Chronic liver disease: HBV/HCV 22%/23%	S0/S1/S2/S3: 61%/21%/10%/9%	ATI	LB	n/a	≥ S1	0.63	0.82	n/a	n/a
							≥ S2	0.67	0.93	n/a	n/a
							≥ S3	0.70	0.92	n/a	n/a
Jang et al. [68]	57	Donor candidates for living LT	S0/S1/S2: 54%/42%/4%	ATI	LB	0.62	≥ S1	0.62	0.81	62	90
Bae et al. [82]	120	Patients who underwent liver resection (≥ 19 years old): HBV/HCV/NAFLD 62%/5%/7%	S0/S1/S2/S3: 45%/42%/12%/2%	ATI	Surgical resection specimen	n/a	≥ S1	0.66	0.91	100	73
Bae et al. [82]	120		S0/S1/S2/S3: 45%/42%/12%/2%	ATI	Surgical resection specimen	n/a	≥ S2	0.71	0.83	75	82
Burgio et al. [70]	208	NAFLD (≥ 18 years old)	S0/S1/S2/S3: 3%/17%/57%/24%	ATI	LB (n = 187)	0.49	≥ S1	0.59	0.92	96	80
					LB (n = 187)	0.49	≥ S2	0.72	0.79	69	80
					MRI-PDFF (n = 191)	0.51	≥ 6.4% (S1)	0.61	0.86	93	77
					MRI-PDFF (n = 191)	0.51	≥ 17.4% (S2)	0.72	0.71	74	58
Ferraioli et al. [36]	72	Adult potentially at risk of liver steatosis	S0/≥ S1: 35%/65%	ATI-Pen	MRI-PDFF	0.78	≥ 5.0% (S1)	0.69	0.90	79	96
Ferraioli et al. [36]	72	Adult potentially at risk of liver steatosis	S0/≥ S1: 35%/65%	ATI-Gen	MRI-PDFF	0.83	≥ 5.0% (S1)	0.62	0.92	81	96
Kwon et al. [72]	100	Mixed	S0/S1/≥ S2: 53%/32%/15%	ATI	MRI-PDFF	0.75	≥ 5.1% (S1)	0.615	0.91	92	80
Kwon et al. [72]	100	Mixed	S0/S1/≥ S2: 53%/32%/15%	ATI	MRI-PDFF	0.75	≥ 14.1% (S2)	0.715	0.94	93	87
Tada et al. [71]	119	Chronic liver disease (non-B, non-C) with hepatic steatosis	S0/S1/S2/S3: 53%/23%/13%/12%	ATI	MRI-PDFF	0.70	≥ 5.2% (S1)	0.63	0.81	68	86
							≥ 11.3% (S2)	0.73	0.87	79	91
							≥ 17.1% (S3)	0.75	0.94	93	89
Bulakci et al. [73]	140	NAFLD (pediatrics)	S0/S1/S2/S3: 50%/22%/21%/7%	ATI	MRI-PDFF	0.88	≥ 5.0% (S1)	0.65	0.94	84	93
							≥ 10.0% (S2)	0.74	0.98	97	93
							≥ 20.0% (S3)	0.91	0.97	90	95
Zhu et al. [94]	130	NAFLD (> 20 years old)	n/a	ATI	H-MRS	0.59	≥5.0%	0.634	0.88	87	77
Zhu et al. [94]	130	NAFLD (> 20 years old)	n/a	ATI	H-MRS	0.59	≥ 10.0%	0.718	0.86	86	71
Ogino et al. [84]	84	NAFLD	S0/S1/S2/S3: 11%/48%/25%/17%	UGAP	LB	0.81	≥ S1	0.60	0.94	87	89
							≥ S2	0.71	0.95	86	92
							≥ S3	0.72	0.88	86	80
Kuroda et al. [77]	105	NAFLD (18–80 years old): NAFL/NASH 21%/79%	S1/S2/S3: 49%/27%/25%	UGAP	LB	n/a	≥ S1	0.620	0.89	85	80
							≥ S2	0.720	0.91	82	85
							≥ S3	0.750	0.91	92	80
Kang et al. [64]	87	Patients who underwent cholecystectomy (≥ 20 years old): NAFLD 44%	n/a	UGAP	LB	0.70	≥ S1	0.59	0.82	87	67
Kang et al. [64]	87		n/a	UGAP	LB	0.70	≥ S2	0.69	0.80	80	84
Imajo et al. [86]	1010	Chronic liver disease: NAFLD 52%	n/a	UGAP	MRI-PDFF (n = 1,010)	0.80	≥ 5.2% (S1)	0.65	0.91	87	82
							≥ 11.3% (S2)	0.71	0.91	91	78
							≥ 17.1% (S3)	0.77	0.89	81	83
					LB (n = 119)	n/a	≥ S1	0.66	0.90	91	75
							≥ S2	0.74	0.88	91	77
							≥ S3	0.76	0.82	94	58
Yoon et al. [87]	118	Healthy or only fatty liver (< 19 years old): NAFLD 65%	n/a	UGAP	MRI-PDFF	0.50	≥ 6.0% (S1)	0.699	0.95	90	100
							≥ 17.6% (S2)	0.699	0.95	97	96
							> 23.3 (S3)	0.699	0.89	97	85
Pirmoazen et al. [95]	31	NAFLD	S0/≥ S1: 42%/58%	LFQ	MRI-PDFF	0.89	≥ 6.4% (S1)	0.63	0.98	94	100
							≥ 17.4% (S2)	0.7	0.96	91	95
							≥ 22.1% (S3)	0.84	0.95	88	100
D'Hondt et al. [96]	48	Children (< 18 years old)	S0/S1/S2/S3: 79%/10%/4%/6%	LFQ	MRI-PDFF	0.76	> 5% (S1)	0.54	0.86	80	82
D'Hondt et al. [96]	48	Children (< 18 years old)	S0/S1/S2/S3: 79%/10%/4%/6%	LFQ	MRI-PDFF	0.76	> 10% (S2)	0.60	0.91	80	82
Ogawa et al. [89]	427	Chronic liver disease (≥ 18 years old): MASLD/HBV 32%/23%	S0/S1/S2/S3: 51.8%/24.1%/12.4%/11.7%	Improved ATT	MRI-PDFF	0.80	≥ 5.2% (S1)	0.68	0.93	86	90
							≥ 11.3% (S2)	0.74	0.91	90	83
							≥ 17.1% (S3)	0.76	0.90	86	78
Jeon et al. [91]	120	NAFLD (≥ 18 years old)	S0/S1/≥ S2: 32%/19%/49%	TAI	MRI-PDFF	0.66	≥ 5.0% (S1)	0.884	0.86	78	79
				TAI		0.66	≥ 10% (S2)	0.980	0.84	64	93
				TSI		0.73	≥ 5.0% (S1)	91.2b	0.96	85	97
				TSI		0.73	≥ 10% (S2)	94.0b	0.94	88	87
Zhu et al. [94]	130	NAFLD (> 20 years old)	n/a	TAI	H-MRS	0.71	≥5.0%	0.910	0.86	74	82
				TAI		0.71	≥ 10.0%	0.955	0.70	77	72
				TSI		0.38	≥5.0%	91.8b	0.87	76	87
				TSI		0.38	≥ 10.0%	95.5b	0.82	90	66
Jeon et al. [101]	173	NAFLD or donor candidates for living LT (≥ 18 years old)	S0/≥ S1: 27%/73 %	USFF	MRI-PDFF	0.86	≥ 5.0% (S1)	5.7a	0.97	90	91
							≥ 15.0% (S2)	14.1a	0.96	89	91
							≥ 25.0% (S3)	16.7a	0.95	100	87
Jeon et al. [102]	173	Development set: MASLD or donor candidates for living LT (≥ 18 years old)	S0/S1/S2/S3: 27%/46%/21%/6%	USFF	MRI-PDFF	0.80	≥ 5% (S1)	8.7a	0.94	81	96
							≥ 15% (S2)	14.9a	0.93	81	92
							≥ 25% (S3)	16.0a	0.91	100	80
	452	Validation set: healthy screening (≥ 19 years old)	S0/S1/S2/S3: 48%/39%/10%/3%			0.82	≥ 5% (S1)	8.7a	0.92	69	94
							≥ 15% (S2)	14.9a	0.94	59	96
							≥ 25% (S3)	16.0a	0.96	79	94
Labyed and Milkowski [98]	101	NAFLD (adults)	S0/S1/S2/S3: 7%/43%/36%/13%	UDFF	LB (n = 90)	0.71	≥ S1	8.1a	0.94	84	100
							≥ S2	15.9a	0.88	77	89
							≥ S3	16.1a	0.83	100	65
					MRI-PDFF (n = 101)	0.87	≥ 5%	6.34	0.97	94	100
					MRI-PDFF (n = 101)	0.87	≥ 10%	11.7	0.95	83	92
Dillman et al. [99]	56	Overweight and obese patients (≥ 16 years old)	S0/≥ S1: 39%/61%	UDFF	MRI-PDFF	0.82	≥ 5.5% (S1)	5a	0.90	94	64
Kubale et al. [100]	187	Mixed (≥ 18 years old)	< 5%/5–10%/10–20%/≥ 20% steatosis: 21%/32%/24%/23%	UDFF	MRI-PDFF	0.79–0.82	≥ 6.5% (S1)	11.2a	0.90	76	90
							≥ 17.4% (S2)	13.6a	0.95	100	83
							≥ 22.1% (S3)	17.2a	0.95	97	85

The order of QUS studies: technique > reference standard > age and etiology of the participant.

The units of the cutoff value are as follows: % for a, no special unit for b, and the rest are dB/cm/MHz.

ATI, attenuation imaging (Cannon Medical Systems); ATT, attenuation measurement (Fujifilm); LFQ, liver fat quantification (Philips); TAI, tissue attenuation imaging (Samsung Medison); TSI, tissue scatter-distribution imaging (Samsung Medison); UDFF, ultrasound-derived fat fraction (Siemens Healthineer); UGAP, ultrasound-guided attenuation parameter (GE Healthcare); USFF, ultrasound fat fraction (Samsung Medison).

AC, attenuation coefficient; AUC, area under the receiver operating characteristic curve; Gen, general; HBV, hepatitis B virus; HCV, hepatitis C virus; H-MRS, proton magnetic resonance spectroscopy; LB, liver biopsy; LT, liver transplantation; MASLD, metabolic dysfunction-associated steatotic liver disease; MRI-PDFF, magnetic resonance imaging-proton density fat fraction; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; n/a, not available; No, number; Pen, penetration; QUS, quantitative ultrasound; r, correlation coefficient; Sn, sensitivity; Sp, specificity.

REFERENCES

1. Cheemerla S, Balakrishnan M. Global epidemiology of chronic liver disease. Clin Liver Dis (Hoboken) 2021;17:365–370.

2. Riazi K, Azhari H, Charette JH, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 2022;7:851–861.

3. Wong VW, Ekstedt M, Wong GL, Hagström H. Changing epidemiology, global trends and implications for outcomes of NAFLD. J Hepatol 2023;79:842–852.

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

5. Kang SH, Lee HW, Yoo JJ, et al. KASL clinical practice guidelines: Management of nonalcoholic fatty liver disease. Clin Mol Hepatol 2021;27:363–401.

6. Janiec DJ, Jacobson ER, Freeth A, Spaulding L, Blaszyk H. Histologic variation of grade and stage of non-alcoholic fatty liver disease in liver biopsies. Obes Surg 2005;15:497–501.

7. Merriman RB, Ferrell LD, Patti MG, et al. Correlation of paired liver biopsies in morbidly obese patients with suspected nonalcoholic fatty liver disease. Hepatology 2006;44:874–880.

8. Younossi ZM, Gramlich T, Liu YC, et al. Nonalcoholic fatty liver disease: assessment of variability in pathologic interpretations. Mod Pathol 1998;11:560–565.

9. 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.

10. 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.

11. Yen YH, Kuo FY, Lin CC, et al. Predicting hepatic steatosis in living liver donors via controlled attenuation parameter. Transplant Proc 2018;50:3533–3538.

12. Noureddin M, Lam J, Peterson MR, et al. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. Hepatology 2013;58:1930–1940.

13. Idilman IS, Keskin O, Elhan AH, Idilman R, Karcaaltincaba M. Impact of sequential proton density fat fraction for quantification of hepatic steatosis in nonalcoholic fatty liver disease. Scand J Gastroenterol 2014;49:617–624.

14. Ferraioli G, Berzigotti A, Barr RG, et al. Quantification of liver fat content with ultrasound: A WFUMB position paper. Ultrasound Med Biol 2021;47:2803–2820.

15. Barr RG. Ultrasound of diffuse liver disease including elastography. Radiol Clin North Am 2019;57:549–562.

16. Saadeh S, Younossi ZM, Remer EM, et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 2002;123:745–750.

17. Dasarathy S, Dasarathy J, Khiyami A, Joseph R, Lopez R, Mc-Cullough AJ. Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study. J Hepatol 2009;51:1061–1067.

18. Saverymuttu SH, Joseph AE, Maxwell JD. Ultrasound scanning in the detection of hepatic fibrosis and steatosis. Br Med J (Clin Res Ed) 1986;292:13–15.

19. Palmentieri B, de Sio I, La Mura V, et al. The role of bright liver echo pattern on ultrasound B-mode examination in the diagnosis of liver steatosis. Dig Liver Dis 2006;38:485–489.

20. Ferraioli G, Soares Monteiro LB. Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol 2019;25:6053–6062.

21. Webb M, Yeshua H, Zelber-Sagi S, et al. Diagnostic value of a computerized hepatorenal index for sonographic quantification of liver steatosis. AJR Am J Roentgenol 2009;192:909–914.

22. Shiralkar K, Johnson S, Bluth EI, Marshall RH, Dornelles A, Gulotta PM. Improved method for calculating hepatic steatosis using the hepatorenal index. J Ultrasound Med 2015;34:1051–1059.

23. Johnson SI, Fort D, Shortt KJ, et al. Ultrasound stratification of hepatic steatosis using hepatorenal index. Diagnostics (Basel) 2021;11:1443.

24. Chauhan A, Sultan LR, Furth EE, Jones LP, Khungar V, Sehgal CM. Diagnostic accuracy of hepatorenal index in the detection and grading of hepatic steatosis. J Clin Ultrasound 2016;44:580–586.

25. Mancini M, Prinster A, Annuzzi G, et al. Sonographic hepatic-renal ratio as indicator of hepatic steatosis: comparison with (1)H magnetic resonance spectroscopy. Metabolism 2009;58:1724–1730.

26. Hamaguchi M, Kojima T, Itoh Y, et al. The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation. Am J Gastroenterol 2007;102:2708–2715.

27. Ballestri S, Lonardo A, Romagnoli D, et al. Ultrasonographic fatty liver indicator, a novel score which rules out NASH and is correlated with metabolic parameters in NAFLD. Liver Int 2012;32:1242–1252.

28. Ballestri S, Nascimbeni F, Baldelli E, et al. Ultrasonographic fatty liver indicator detects mild steatosis and correlates with metabolic/histological parameters in various liver diseases. Metabolism 2017;72:57–65.

29. Bril F, Ortiz-Lopez C, Lomonaco R, et al. Clinical value of liver ultrasound for the diagnosis of nonalcoholic fatty liver disease in overweight and obese patients. Liver Int 2015;35:2139–2146.

30. Cloutier G, Destrempes F, Yu F, Tang A. Quantitative ultrasound imaging of soft biological tissues: a primer for radiologists and medical physicists. Insights Imaging 2021;12:127.

31. Ferraioli G, Kumar V, Ozturk A, Nam K, de Korte CL, Barr RG. US attenuation for liver fat quantification: An AIUM-RSNA QIBA pulse-echo quantitative ultrasound initiative. Radiology 2022;302:495–506.

32. Jang W, Song JS. Non-invasive imaging methods to evaluate non-alcoholic fatty liver disease with fat quantification: A review. Diagnostics (Basel) 2023;13:1852.

33. Paige JS, Bernstein GS, Heba E, et al. A pilot comparative study of quantitative ultrasound, conventional ultrasound, and MRI for predicting histology-determined steatosis grade in adult nonalcoholic fatty liver disease. AJR Am J Roentgenol 2017;208:W168–W177.

34. Fetzer DT, Pierce TT, Robbin ML, et al. US quantification of liver fat: Past, present, and future. Radiographics 2023;43:e220178.

35. Ferraioli G, Maiocchi L, Raciti MV, et al. Detection of liver steatosis with a novel ultrasound-based technique: A pilot study using MRI-derived proton density fat fraction as the gold standard. Clin Transl Gastroenterol 2019;10:e00081.

36. Ferraioli G, Maiocchi L, Savietto G, et al. Performance of the attenuation imaging technology in the detection of liver steatosis. J Ultrasound Med 2021;40:1325–1332.

37. Lin SC, Heba E, Wolfson T, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat using a new quantitative ultrasound technique. Clin Gastroenterol Hepatol 2015;13:1337–1345.e6.

38. Lu ZF, Zagzebski JA, Lee FT. Ultrasound backscatter and attenuation in human liver with diffuse disease. Ultrasound Med Biol 1999;25:1047–1054.

39. Zagzebski JA, Lu ZF, Yao LX. Quantitative ultrasound imaging: in vivo results in normal liver. Ultrason Imaging 1993;15:335–351.

40. Boote EJ, Zagzebski JA, Madsen EL. Backscatter coefficient imaging using a clinical scanner. Med Phys 1992;19:1145–1152.

41. Bamber JC, Hill CR, King JA. Acoustic properties of normal and cancerous human liver-II. Dependence of tissue structure. Ultrasound Med Biol 1981;7:135–144.

42. Han A, Andre MP, Deiranieh L, et al. Repeatability and reproducibility of the ultrasonic attenuation coefficient and backscatter coefficient measured in the right lobe of the liver in adults with known or suspected nonalcoholic fatty liver disease. J Ultrasound Med 2018;37:1913–1927.

43. Han A, Zhang YN, Boehringer AS, et al. Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD. Eur Radiol 2019;29:4699–4708.

44. Imbault M, Dioguardi Burgio M, Faccinetto A, et al. Ultrasonic fat fraction quantification using in vivo adaptive sound speed estimation. Phys Med Biol 2018;63:215013.

45. Imbault M, Faccinetto A, Osmanski BF, et al. Robust sound speed estimation for ultrasound-based hepatic steatosis assessment. Phys Med Biol 2017;62:3582–3598.

46. O'Brien WD Jr, Erdman JW Jr, Hebner TB. Ultrasonic propagation properties (@ 100 MHz) in excessively fatty rat liver. J Acoust Soc Am 1988;83:1159–1166.

47. Chen CF, Robinson DE, Wilson LS, Griffiths KA, Manoharan A, Doust BD. Clinical sound speed measurement in liver and spleen in vivo. Ultrason Imaging 1987;9:221–235.

48. Ozturk A, Grajo JR, Gee MS, et al. Quantitative hepatic fat quantification in non-alcoholic fatty liver disease using ultrasound-based techniques: A review of literature and their diagnostic performance. Ultrasound Med Biol 2018;44:2461–2475.

49. Wang X, Bamber JC, Esquivel-Sirvent R, et al. Ultrasonic sound speed estimation for liver fat quantification: A review by the AIUM-RSNA QIBA pulse-echo quantitative ultrasound initiative. Ultrasound Med Biol 2023;49:2327–2335.

50. Karlas T, Petroff D, Sasso M, et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol 2017;66:1022–1030.

51. Eddowes PJ, Sasso M, Allison M, et al. Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology 2019;156:1717–1730.

52. Petroff D, Blank V, Newsome PN, et al. Assessment of hepatic steatosis by controlled attenuation parameter using the M and XL probes: an individual patient data meta-analysis. Lancet Gastroenterol Hepatol 2021;6:185–198.

53. Sasso M, Audière S, Kemgang A, et al. Liver steatosis assessed by controlled attenuation parameter (CAP) measured with the XL probe of the FibroScan: A pilot study assessing diagnostic accuracy. Ultrasound Med Biol 2016;42:92–103.

54. de Lédinghen V, Vergniol J. Transient elastography (FibroScan). Gastroenterol Clin Biol 2008;32(6 Suppl 1):58–67.

55. Wong VW, Petta S, Hiriart JB, et al. Validity criteria for the diagnosis of fatty liver by M probe-based controlled attenuation parameter. J Hepatol 2017;67:577–584.

56. Sirli R, Sporea I. Controlled attenuation parameter for quantification of steatosis: Which cut-offs to use? Can J Gastroenterol Hepatol 2021;2021:6662760.

57. Ferraioli G. CAP for the detection of hepatic steatosis in clinical practice. Lancet Gastroenterol Hepatol 2021;6:151–152.

58. Runge JH, Smits LP, Verheij J, et al. MR Spectroscopy-derived proton density fat fraction is superior to controlled attenuation parameter for detecting and grading hepatic steatosis. Radiology 2018;286:547–556.

59. Imajo K, Kessoku T, Honda Y, et al. Magnetic resonance imaging more accurately classifies steatosis and fibrosis in patients with nonalcoholic fatty liver disease than transient elastography. Gastroenterology 2016;150:626–637.e7.

60. Nogami A, Yoneda M, Iwaki M, et al. Diagnostic comparison of vibration-controlled transient elastography and MRI techniques in overweight and obese patients with NAFLD. Sci Rep 2022;12:21925.

61. Bozic D, Podrug K, Mikolasevic I, Grgurevic I. Ultrasound methods for the assessment of liver steatosis: A critical appraisal. Diagnostics (Basel) 2022;12:2287.

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

63. Jeon SK, Lee JM, Joo I. Clinical feasibility of quantitative ultrasound imaging for suspected hepatic steatosis: Intra- and inter-examiner reliability and correlation with controlled attenuation parameter. Ultrasound Med Biol 2021;47:438–445.

64. Kang KA, Lee SR, Jun DW, Do IG, Kim MS. Diagnostic performance of a novel ultrasound-based quantitative method to assess liver steatosis in histologically identified nonalcoholic fatty liver disease. Med Ultrason 2023;25:7–13.

65. Jeon SK, Lee JM, Joo I, 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.

66. Dioguardi Burgio M, Ronot M, Reizine E, et al. Quantification of hepatic steatosis with ultrasound: promising role of attenuation imaging coefficient in a biopsy-proven cohort. Eur Radiol 2020;30:2293–2301.

67. Jesper D, Klett D, Schellhaas B, et al. Ultrasound-based attenuation imaging for the non-invasive quantification of liver fat - A pilot study on feasibility and inter-observer variability. IEEE J Transl Eng Health Med 2020;8:1800409.

68. Jang JK, Kim SY, Yoo IW, Cho YB, Kang HJ, Lee DH. Diagnostic performance of ultrasound attenuation imaging for assessing low-grade hepatic steatosis. Eur Radiol 2022;32:2070–2077.

69. Huang YL, Bian H, Zhu YL, et al. Quantitative diagnosis of nonalcoholic fatty liver disease with ultrasound attenuation imaging in a biopsy-proven cohort. Acad Radiol 2023;30 Suppl 1:S155–S163.

70. Dioguardi Burgio M, Castera L, Oufighou M, et al. Prospective comparison of attenuation imaging and controlled attenuation parameter for liver steatosis diagnosis in patients with nonalcoholic fatty liver disease and type 2 diabetes. Clin Gastroenterol Hepatol 2024;22:1005–1013.e27.

71. Tada T, Kumada T, Toyoda H, et al. Attenuation imaging based on ultrasound technology for assessment of hepatic steatosis: A comparison with magnetic resonance imaging-determined proton density fat fraction. Hepatol Res 2020;50:1319–1327.

72. Kwon EY, Kim YR, Kang DM, Yoon KH, Lee YH. Usefulness of US attenuation imaging for the detection and severity grading of hepatic steatosis in routine abdominal ultrasonography. Clin Imaging 2021;76:53–59.

73. Bulakci M, Ercan CC, Karapinar E, et al. Quantitative evaluation of hepatic steatosis using attenuation imaging in a pediatric population: a prospective study. Pediatr Radiol 2023;53:1629–1639.

74. Tada T, Iijima H, Kobayashi N, et al. Usefulness of attenuation imaging with an ultrasound scanner for the evaluation of hepatic steatosis. Ultrasound Med Biol 2019;45:2679–2687.

75. Lee DH, Cho EJ, Bae JS, et al. Accuracy of two-dimensional shear wave elastography and attenuation imaging for evaluation of patients with nonalcoholic steatohepatitis. Clin Gastroenterol Hepatol 2021;19:797–805.e7.

76. Sugimoto K, Moriyasu F, Oshiro H, et al. The role of multiparametric US of the liver for the evaluation of nonalcoholic steatohepatitis. Radiology 2020;296:532–540.

77. Kuroda H, Abe T, Fujiwara Y, Nagasawa T, Takikawa Y. Diagnostic accuracy of ultrasound-guided attenuation parameter as a noninvasive test for steatosis in non-alcoholic fatty liver disease. J Med Ultrason (2001) 2021;48:471–480.

78. Sugimoto K, Abe M, Oshiro H, et al. The most appropriate region-of-interest position for attenuation coefficient measurement in the evaluation of liver steatosis. J Med Ultrason (2001) 2021;48:615–621.

79. Jang JK, Lee ES, Seo JW, et al. Two-dimensional shear-wave elastography and US attenuation imaging for nonalcoholic steatohepatitis diagnosis: A cross-sectional, multicenter study. Radiology 2022;305:118–126.

80. Yuri M, Nishimura T, Tada T, et al. Diagnosis of hepatic steatosis based on ultrasound attenuation imaging is not influenced by liver fibrosis. Hepatol Res 2022;52:1009–1019.

81. Jang JK, Choi SH, Lee JS, Kim SY, Lee SS, Kim KW. Accuracy of the ultrasound attenuation coefficient for the evaluation of hepatic steatosis: a systematic review and meta-analysis of prospective studies. Ultrasonography 2022;41:83–92.

82. Bae JS, Lee DH, Suh KS, et al. Noninvasive assessment of hepatic steatosis using a pathologic reference standard: comparison of CT, MRI, and US-based techniques. Ultrasonography 2022;41:344–354.

83. Tada T, Kumada T, Toyoda H, et al. Utility of attenuation coefficient measurement using an ultrasound-guided attenuation parameter for evaluation of hepatic steatosis: Comparison with MRI-determined proton density fat fraction. AJR Am J Roentgenol 2019;212:332–341.

84. Ogino Y, Wakui N, Nagai H, Igarashi Y. The ultrasound-guided attenuation parameter is useful in quantification of hepatic steatosis in non-alcoholic fatty liver disease. JGH Open 2021;5:947–952.

85. Fujiwara Y, Kuroda H, Abe T, et al. The B-mode image-guided ultrasound attenuation parameter accurately detects hepatic steatosis in chronic liver disease. Ultrasound Med Biol 2018;44:2223–2232.

86. Imajo K, Toyoda H, Yasuda S, et al. Utility of ultrasound-guided attenuation parameter for grading steatosis with reference to MRI-PDFF in a large cohort. Clin Gastroenterol Hepatol 2022;20:2533–2541.e7.

87. Yoon H, Kim J, Lim HJ, et al. Attenuation coefficient measurement using a high-frequency (2-9 MHz) convex transducer for children including fatty liver. Ultrasound Med Biol 2022;48:1070–1077.

88. Tamaki N, Koizumi Y, Hirooka M, et al. Novel quantitative assessment system of liver steatosis using a newly developed attenuation measurement method. Hepatol Res 2018;48:821–828.

89. Ogawa S, Kumada T, Gotoh T, et al. A comparative study of hepatic steatosis using two different qualitative ultrasound techniques measured based on magnetic resonance imaging-derived proton density fat fraction. Hepatol Res 2024 Jan 31. doi: 10.1111/hepr.14019.

90. Koizumi Y, Hirooka M, Tamaki N, et al. New diagnostic technique to evaluate hepatic steatosis using the attenuation coefficient on ultrasound B mode. PLoS One 2019;14:e0221548.

91. Jeon SK, Lee JM, Joo I, Park SJ. Quantitative ultrasound radiofrequency data analysis for the assessment of hepatic steatosis in nonalcoholic fatty liver disease using magnetic resonance imaging proton density fat fraction as the reference standard. Korean J Radiol 2021;22:1077–1086.

92. Rocca A, Komici K, Brunese MC, et al. Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions. Radiol Med 2024;129:549–557.

93. Rónaszéki AD, Budai BK, Csongrády B, et al. Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis. Medicine (Baltimore) 2022;101:e29708.

94. Zhu Y, Yin H, Zhou D, et al. A prospective comparison of three ultrasound-based techniques in quantitative diagnosis of hepatic steatosis in NAFLD. Abdom Radiol (NY) 2024;49:81–92.

95. Pirmoazen AM, Khurana A, Loening AM, et al. Diagnostic performance of 9 quantitative ultrasound parameters for detection and classification of hepatic steatosis in nonalcoholic fatty liver disease. Invest Radiol 2022;57:23–32.

96. D'Hondt A, Rubesova E, Xie H, Shamdasani V, Barth RA. Liver fat quantification by ultrasound in children: A prospective study. AJR Am J Roentgenol 2021;217:996–1006.

97. Ormachea J, Parker KJ. A preliminary study of liver fat quantification using reported ultrasound speed of sound and attenuation parameters. Ultrasound Med Biol 2022;48:675–684.

98. Labyed Y, Milkowski A. Novel method for ultrasound-derived fat fraction using an integrated phantom. J Ultrasound Med 2020;39:2427–2438.

99. Dillman JR, Thapaliya S, Tkach JA, Trout AT. Quantification of hepatic steatosis by ultrasound: Prospective comparison with MRI proton density fat fraction as reference standard. AJR Am J Roentgenol 2022;219:784–791.

100. Kubale R, Schneider G, Lessenich CPN, et al. Ultrasound-derived fat fraction for hepatic steatosis assessment: Prospective study of agreement with MRI PDFF and sources of variability in a heterogeneous population. AJR Am J Roentgenol 2024 Mar 20. doi: 10.2214/AJR.23.30775.

101. Jeon SK, Lee JM, Joo I, Yoon JH, Lee G. Two-dimensional convolutional neural network using quantitative US for noninvasive assessment of hepatic steatosis in NAFLD. Radiology 2023;307:e221510.

102. Jeon SK, Lee JM, Cho SJ, Byun YH, Jee JH, Kang M. Development and validation of multivariable quantitative ultrasound for diagnosing hepatic steatosis. Sci Rep 2023;13:15235.

103. Jung J, Han A, Madamba E, et al. Direct comparison of quantitative US versus controlled attenuation parameter for liver fat assessment using MRI proton density fat fraction as the reference standard in patients suspected of having NAFLD. Radiology 2022;304:75–82.

104. Barr RG, Cestone A, De Silvestri A. A pre-release algorithm with a confidence map for estimating the attenuation coefficient for liver fat quantification. J Ultrasound Med 2022;41:1939–1948.

105. Jeon SK, Lee JM. Inter-platform reproducibility of ultrasound-based fat fraction for evaluating hepatic steatosis in nonalcoholic fatty liver disease. Insights Imaging 2024;15:46.

106. Jeon SK, Lee JM, Joo I, Yoon JH. Assessment of the inter-platform reproducibility of ultrasound attenuation examination in nonalcoholic fatty liver disease. Ultrasonography 2022;41:355–364.

107. Ferraioli G, Raimondi A, Maiocchi L, et al. Liver fat quantification with ultrasound: Depth dependence of attenuation coefficient. J Ultrasound Med 2023;42:2247–2255.

108. Seo DM, Lee SM, Park JW, et al. How many times should we repeat measurements of the ultrasound-guided attenuation parameter for evaluating hepatic steatosis? Ultrasonography 2023;42:227–237.

109. Byenfeldt M, Kihlberg J, Nasr P, et al. Altered probe pressure and body position increase diagnostic accuracy for men and women in detecting hepatic steatosis using quantitative ultrasound. Eur Radiol 2024 Mar 8. doi: 10.1007/s00330-024-10655-1.

110. Paverd C, Kupfer S, Kirchner IN, et al. Impact of breathing phase, liver segment, and prandial state on ultrasound shear wave speed, shear wave dispersion, and attenuation imaging of the liver in healthy volunteers. Diagnostics (Basel) 2023;13:989.

111. Tada T, Kumada T, Toyoda H, et al. Liver stiffness does not affect ultrasound-guided attenuation coefficient measurement in the evaluation of hepatic steatosis. Hepatol Res 2020;50:190–198.

112. Ferraioli G, Raimondi A, De Silvestri A, Filice C, Barr RG. Toward acquisition protocol standardization for estimating liver fat content using ultrasound attenuation coefficient imaging. Ultrasonography 2023;42:446–456.

113. Pirmoazen AM, Khurana A, El Kaffas A, Kamaya A. Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease. Theranostics 2020;10:4277–4289.

114. Kuroda H, Oguri T, Kamiyama N, et al. Multivariable quantitative US parameters for assessing hepatic steatosis. Radiology 2023;309:e230341.

115. Kuroda H, Fujiwara Y, Abe T, et al. Two-dimensional shear wave elastography and ultrasound-guided attenuation parameter for progressive non-alcoholic steatohepatitis. PLoS One 2021;16:e0249493.

116. Liu F, Bi M, Jing X, et al. Multiparametric US for Identifying metabolic dysfunction-associated steatohepatitis: A prospective multicenter study. Radiology 2024;310:e232416.

117. Zhao Y, Qiu C, Dong Y, et al. Technical acoustic measurements combined with clinical parameters for the differential diagnosis of nonalcoholic steatohepatitis. Diagnostics (Basel) 2023;13:1547.