Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis

Rónaszéki, A; Budai, Bettina Katalin; Csongrády, Barbara; Stollmayer, Róbert; Hagymási, Krisztina; Werling, Klára; Fodor, Tamás; Folhoffer, Anikó; Kalina, Ildikó; Gyõri, Gabriella; Maurovich-Horvat, Pál; Kaposi, Pál Novák

doi:10.1097/md.0000000000029708

Cited by 14 publications

(15 citation statements)

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“…Together with AI-HRI, during the same scan, we also measured two additional QUS parameters, the AC and the SC, as alternative biomarkers of hepatic steatosis. The detailed protocols of the AC and SC measurements have been published previously [ 14 ]. Briefly, the right lobe of the liver was visualized from an intercostal window, and from the QUS application of the scanner, either tissue attenuation imaging (TAI TM ) or tissue scatter distribution imaging (TSI TM ) mode was selected.…”

Section: Methodsmentioning

confidence: 99%

“…The MRI scans were completed within a month after the ultrasound scans and evaluated blinded from US results. Similar to previous publications, we classified patients into four severity grades (none: <5%, mild: 5–10%, moderate: 10–20%, severe: ≥20%) based on the amount of hepatic steatosis measured with MRI-PDFF [ 4 , 14 , 16 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, artificial intelligence-calculated HRI (AI-HRI) measurement has become available, where pixels of the liver and renal cortex are delineated by a deep convolutional neural network (DCNN), and positioning of the ROIs, and calculation of the HRI are fully-automated [ 11 ]. Furthermore, multiple quantitative ultrasound (QUS) parameters, which allow for simultaneous assessment of liver fibrosis, inflammation, and steatosis in chronic liver diseases, can be measured on advanced systems [ 12 , 13 , 14 ]. The performance of some of the QUS metrics, such as the ultrasound attenuation coefficient (AC) and ultrasound backscatter-distribution coefficient (SC), has been very good in the classification of all steatosis grades according to multiple studies [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease

et al. 2023

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Background and Objectives: This study aims to evaluate artificial intelligence-calculated hepatorenal index (AI-HRI) as a diagnostic method for hepatic steatosis. Materials and Methods: We prospectively enrolled 102 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD). All patients had a quantitative ultrasound (QUS), including AI-HRI, ultrasound attenuation coefficient (AC,) and ultrasound backscatter-distribution coefficient (SC) measurements. The ultrasonographic fatty liver indicator (US-FLI) score was also calculated. The magnetic resonance imaging fat fraction (MRI-PDFF) was the reference to classify patients into four grades of steatosis: none < 5%, mild 5–10%, moderate 10–20%, and severe ≥ 20%. We compared AI-HRI between steatosis grades and calculated Spearman’s correlation (rs) between the methods. We determined the agreement between AI-HRI by two examiners using the intraclass correlation coefficient (ICC) of 68 cases. We performed a receiver operating characteristics (ROC) analysis to estimate the area under the curve (AUC) for AI-HRI. Results: The mean AI-HRI was 2.27 (standard deviation, ±0.96) in the patient cohort. The AI-HRI was significantly different between groups without (1.480 ± 0.607, p < 0.003) and with mild steatosis (2.155 ± 0.776), as well as between mild and moderate steatosis (2.777 ± 0.923, p < 0.018). AI-HRI showed moderate correlation with AC (rs = 0.597), SC (rs = 0.473), US-FLI (rs = 0.5), and MRI-PDFF (rs = 0.528). The agreement in AI-HRI was good between the two examiners (ICC = 0.635, 95% confidence interval (CI) = 0.411–0.774, p < 0.001). The AI-HRI could detect mild steatosis (AUC = 0.758, 95% CI = 0.621–0.894) with fair and moderate/severe steatosis (AUC = 0.803, 95% CI = 0.721–0.885) with good accuracy. However, the performance of AI-HRI was not significantly different (p < 0.578) between the two diagnostic tasks. Conclusions: AI-HRI is an easy-to-use, reproducible, and accurate QUS method for diagnosing mild and moderate hepatic steatosis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease

et al. 2023

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“…The qualitative assessment of the morphology or the brightness of liver parenchyma has now been supported by quantitative values obtained from tissue microstructure characterization [ 33 ] through quantitative ultrasound (QUS) techniques.…”

Section: Introductionmentioning

confidence: 99%

Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions

Rocca,

Komici,

Brunese

et al. 2024

Radiol med

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Liver steatosis is the most common chronic liver disease and affects 10–24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate therapy. Innovative noninvasive radiological techniques have been developed through MRI and US. MRI-PDFF is the reference standard, but it is not so widely diffused due to its cost. For this reason, ultrasound tools have been validated to study liver parenchyma. The qualitative assessment of the brightness of liver parenchyma has now been supported by quantitative values of attenuation and scattering to make the analysis objective and reproducible. We aim to demonstrate the reliability of quantitative ultrasound in assessing liver fat and to confirm the inter-operator reliability in different respiratory phases. We enrolled 45 patients examined during normal breathing at rest, peak inspiration, peak expiration, and semi-sitting position. The highest inter-operator agreement in both attenuation and scattering parameters was achieved at peak inspiration and peak expiration, followed by semi-sitting position. In conclusion, this technology also allows to monitor uncompliant patients, as it grants high reliability and reproducibility in different body position and respiratory phases.

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“…However, the accuracy of ultrasound in quantifying the degree of hepatic steatosis is limited, as it relies on subjective visual interpretation by radiologists. This subjectivity can result in interobserver variability and make it di cult to precisely quantify the level of ecogenicity, or echogenicity, of the hepatic tissue (7).…”

Section: Introductionmentioning

confidence: 99%

Estimating Levels of Echogenicity in Non-Alcoholic Fatty Liver Disease - a Mathematical Model

Andrade

Oliveira

et al. 2023

Preprint

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Introduction Echogenicity is a measure used in ultrasound imaging to assess the ability of tissue to reflect sound waves. In the case of hepatic tissue, it is important to identify and classify different levels of echogenicity to assist in diagnosing possible alterations. As ultrasound imaging is operator-dependent, there is a gap in accurately defining the levels of non-alcoholic fatty liver disease (NAFLD). Objective To develop a mathematical model for estimating levels of echogenicity in hepatic tissue in NAFLD based on grayscale values. Method The common number of grayscale levels in an ultrasound is 256. We utilized a dataset containing experimental grayscale values (Ci) and corresponding echogenicity values (Ei). The method of least squares was applied to determine the equations describing the relationship between Ci and Ei. Results We assume that E is a linear function of the grayscale, given by: E = m * C + b. Where: E = is the unit of measurement of echogenicity, C = is the value of the grayscale (0 to 256), m = is the slope coefficient of the line relating E to C - b is the intercept coefficient of the line. The results derived from the least squares method provided the following regression equations: m = (Σ(Ci - Cm) * (Ei - Em)) / (Σ(Ci - Cm)^2) and b = Em - m * Cm. These equations allowed us to estimate the levels of echogenicity in hepatic tissue based on grayscale values. Thus, on a grayscale ranging from 0 to 256, hepatic tissue with normal echogenicity presented a value within the range of 120 to 150; mild NAFLD had a value between 150 and 180; moderate NAFLD had a value between 180 and 210; severe NAFLD had a value above 210. Conclusion This study developed a mathematical model for estimating levels of echogenicity in hepatic tissue using a grayscale ranging from 0 to 256. The regression equations obtained through the method of least squares can be used to calculate the degrees of NAFLD. This estimation can assist in the diagnosis and monitoring of hepatic tissue alterations during ultrasound examinations.

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Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis

Cited by 14 publications

References 29 publications

Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease

Evaluation of Artificial Intelligence-Calculated Hepatorenal Index for Diagnosing Mild and Moderate Hepatic Steatosis in Non-Alcoholic Fatty Liver Disease

Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions

Estimating Levels of Echogenicity in Non-Alcoholic Fatty Liver Disease - a Mathematical Model

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