Skip to main content
NCBI home page
Springer logoLink to Springer
. 2024 Mar 21;129(4):549–557. doi: 10.1007/s11547-024-01786-y

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

Aldo Rocca 1, Klara Komici 1, Maria Chiara Brunese 1,✉, Giulia Pacella 1, Pasquale Avella 2, Chiara Di Benedetto 1, Corrado Caiazzo 1, Marcello Zappia 1, Luca Brunese 1,#, Gianfranco Vallone 1,#
PMCID: PMC11021279  PMID: 38512608

Abstract

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.

KeyWord: Artificial intelligence, Formal Methods, Pancreatitis, Radiomics

Introduction

The most common chronic liver disease is liver steatosis or fatty liver, which affects 10–24% of the general population [1]. Non-alcoholic fatty liver disease (NAFLD) is a chronic disease related not only to alcohol consumption but also to diabetes, hyperlipidemia, toxins, drugs, or genetic diseases [2, 3].

NAFLD is diffusing in the general population in two steps, the first step consists of metabolic syndrome, where insulin resistance induces liver parenchyma to stock fat developing liver steatosis [4, 5].

The second step provides the progression of liver steatosis and steatohepatitis (NASH), characterized by inflammation and chronic damage that may evolve into liver fibrosis and end-stage liver disease [6]. Late or delayed diagnosis without any lifestyle change may increase the risk of liver fibrosis or cirrhosis in the general population with consequent costs on the healthcare system, but even in patients after liver transplantation [7–10].

Liver biopsy has the limit of being an invasive procedure and it allows the examination of only a selected parenchyma, but it has been abandoned in clinical practice [11–13].

To overcome those limits innovative noninvasive radiological techniques have been developed through MRI and US [14–17].

Magnetic resonance imaging proton density fat fraction (MRI-PDFF) enables accurate, repeatable, and reproducible quantitative assessment of liver fat over the entire liver parenchyma, achieving high accuracy and sensitivity [18]. The diagnostic power of MRI-PDFF allows the detection even of the 5% of microscopic fat, so it has a higher sensitivity to detect early, but fundamental changes in liver fat content than liver biopsies [19, 20].

The major concern about the wide diffusion of MRI-PDFF is represented by its costs, limited diffusion, and patient compliance [21–23]. Besides standard protocols, radiomics has already been proposed to be a useful tool in the management of several pathologies [24–26].

More in detail, most recent studies are also proposing machine learning-based models to analyze liver parenchyma, but there are still few studies validated in clinical practice [15, 27, 28].

Considering the huge diffusion and reproducibility of the liver US, ultrasound software has been enriched by tools dedicated to hepatic fat quantification [29].

Despite it is well known that a high percentage of fat determines the brightness of liver parenchyma on US images, we have to underline that this is related to the scatter and to the attenuation of the ultrasound wave due to the amount of fats [30, 31].

In particular, B-Mode ultrasound allows to assess the grade of liver steatosis through: the evaluation of echogenicity of the liver compared to the renal cortex. Furthermore, the right liver attenuation with diaphragm visualization and the visualization of intra-hepatic vessels are commonly used in clinical practice [32].

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.

There is only a few evidence about the inter-operator reliability of fat quantification tools in clinical practice [34]. There is also a lack of evidence of its reliability in selected categories of patients, in different body positions and respiratory phases [35].

So this study aims to demonstrate the reliability of quantitative ultrasound (QUS) in assessing liver fat volume measurements, to confirm the inter-operator reliability in the respiratory phases, but even in different body positions, in order to follow up uncompliant patients.

Tissue attenuation imaging (TAI, Samsung Medison)

Tissue attenuation is due to the energy loss of an ultrasound wave when it passes through a tissue. Attenuation depends on tissue features and wave frequency. When the percentage of liver fat is higher also the attenuation increases [36].

Attenuation coefficient (AC) has been calculated with several methods proposed by different vendors [37–39]. AC showed high reliability to detect liver fat, and to estimate the grade of liver steatosis, compared to liver biopsy and MRI-PDFF as reference standard [40].

In our study, AC is calculated by a parameter, the tissue attenuation imaging (TAI, Samsung Medison), that indicates the slope of the ultrasound central frequency downshift along the depth.

Tissue scatter distribution imaging (TSI, Samsung Medison)

Backscattering refers to the ultrasound energy reflected from a tissue, and it is represented by echogenicity or brightness. In particular, liver brightness means the backscattering has increased. The scattering of ultrasound also creates images with speckle patterns. The different patterns have been described by a statistical distribution. In particular, the Nakagami distribution correlates backscattering to the percentage of liver fat. In our work, Nakagami distribution is calculated by a parameter, tissue scatter distribution imaging (TSI, Samsung Medison), which calculates the concentration and the distribution of the ultrasound scatterers [41, 42].

TSI has been validated through a comparison with liver biopsy and MRI-PDFF as reference standards.

Materials and methods

This is a retrospective study conducted on a prospectively collected database study conducted at the University of Molise between November 2022 and April 2023. The patients were admitted to an abdominal ultrasound examination for other reasons, at the University of Molise, Campobasso, Italy.

All patients signed an informed consent to publish their anonymous clinical data.

Inclusion criteria:

  • > 18 years old.

  • No history of chronic liver disease.

  • No habitual alcohol consumption.

Exclusion criteria:

  • < 18 years old.

  • Chronic liver disease or alcohol addiction.

  • Lack of compliance.

We studied echogenicity and composition of liver parenchyma.

For each patient beyond the US exam, we provided a dataset of clinical data: gender, age, body mass index (BMI), complete blood count, bilirubin and alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels.

Patients were divided into three subgroups according to their body mass index: Subgroup 1 includes normal-weight patients with 18.5 < BMI < 24.99 kg/m2; subgroup 2 includes overweight patients with 25 < BMI < 29.99 kg/m2; and subgroup 3 includes obese patients with BMI > 30 kg/m2.

The ultrasound exam was performed on the right lobe with Samsung RS85 Prestige, with a single convex transducer 1–7 MHz convex transducer (CA1-7S) and completed with quantitative ultrasound (QUS) imaging: tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI).

TAI was recorded only after verification of an “R2 value” < 0.8 [43].

The examinations were performed by 2 expert radiologists. A total of 10 measurements were recorded in different liver segments, in particular V, VI, VIII, and VII segments.

We included in the study the highest value of TAI and TSI found by each physician.

For each patient, a mean of 10 measurements was recorded using four different methods:

  • Method 1: normal breathing at rest.

  • Method 2: peak inspiration.

  • Method 3: end-expiration.

  • Method 4: semi-sitting position.

Patients were asked to inhale, hold their breath, exhale, or breathe quietly. Then, patients were asked to move to a semi-seating position.

Operators 1 and 2 conducted the US with the same machine settings.

Patient subgroups during examination are reported in Figs. 1, 2, and 3.

Fig. 1.

Fig. 1

Open in a new tab

TAI and TSI measurement in a patient with BMI < 25 kg/m2. QUS shows no evidence of liver steatosis (Grade 0)

Fig. 2.

Fig. 2

Open in a new tab

TAI and TSI measurement in a patient with BMI 25–30 kg/m2. QUS shows evidence of mild liver steatosis (Grade 2)

Fig. 3.

Fig. 3

Open in a new tab

TAI and TSI measurement in a patient with BMI > 30 kg/m2. QUS shows evidence of severe steatosis (Grade 3)

Standard of reference

Table 1 reported the reference standard selected following Sendur et al. [41]

Table 1.

Reference standard for steatosis grade quantification

Steatosis MRI-PDFF value TAI TSI
Grade 1 MRI ≥ 5% 0.75 92.44
Grade 2 MRI ≥ 16.3% 0.86 96.64
Grade 3 MRI ≥ 21.7% 0.96 99.45
Open in a new tab

Statistical analysis

Cohen's Kappa values were calculated to identify rates of inter-rater agreement between two different radiologists. Data were expressed as agreement in percentage, Cohen's Kappa value, standard error, and Z. The measure of the agreement below 0.0 means poor agreement, 0.00–0.20 slight agreement, 0.21–0.40 fair agreement, 0.41–0.60 moderate agreement, 0.61–0.80 substantial agreement, and > 0.80 almost perfect agreement [44, 45]. Considering that measurements were performed in different methods as normal breathing, peak inspiration, end-expiration, and semi-seating position, one-way ANOVA with Bonferroni correction was performed separately for both experts. Statistical significance was at p ≤ 0.05. Statistical analyses were performed with STATA SE 16.1 StataCorp LLC software.

Results

We retrospectively collected 45 patients and divided them into three subgroups: 15 normal-weight patients, 15 overweight patients, and 15 obese patients. The mean age of the patients was 36.7 ± 1.89 [24–65 CI].

Most of the patients were male (27/45, 60%). In particular, most of the obese patients were male (10/15, 75%).

The mean values of TAI and TSI recorded by the operators during normal breath were 0.718 ± 0.026 (operator 1), 0.755 ± 0.236 (operator 2) and 92,654 ± 1465 (operator 1) 92579 ± 2549 (operator 2). TAI and TSI values are both expressions of a population with a mean grade of steatosis 1. Inter-operators agreement in this phase was low, 15.56% for TAI and 2.22% for TSI.

During forced inspiration, the mean values of TAI and TSI recorded by the operators were 0.728 ± 0.023 (operator 1), 0.741 ± 0.0219 (operator 2) and 93.67 ± 1.809 (operator 1) 94.33 ± 1.84 (operator 2). Both the TAI and TSI values are coherent and belong to steatosis grade 1. In this respiratory phase, the inter-operator agreement was higher both for TAI and TSI measurement, 48.89% and 37.78%, respectively.

During peak expiration, the mean values of TAI and TSI recorded by the operators were 0.736 ± 0.024 (operator 1), 0.732 ± 0.029 (operator 2), and 92,658 ± 1605 (operator 1), 92,571 ± 1608 (operator 2). Also during this respiratory phase, TAI and TSI values are coherent and belong to steatosis grade 1. Inter-operator agreement was the highest achieved, both for TAI and TSI measurement, 48.89% and 48.89%, respectively.

In the semi-sitting position, in uncompliant patients, the mean values of TAI and TSI recorded by the operators were 0.701 ± 0.02 (operator 1), 0.715 ± 0.021 (operator 2), and 92,098 ± 1316 (operator 1), 94,277 ± 1457 (operator 2). Inter-operator agreement as medium–high, 33.33% both for TAI and TSI values.

Inter-operator agreement calculated with K-Cohen test showed the lowest K values in the measurement of TAI and TSI during quiet breath (K = 0.137 and K = 0.0115, respectively).

ANOVA test showed a significant statistical difference among operators only in the TSI measurements; therefore, quiet breath strongly influenced TSI value rather than TAI.

Results of statistical analysis are summarized in Tables 2 and 3.

Table 2.

The means of TAI and TSI values taken by each operator and the agreement between the measurements (K Cohen test)

Total population
Age
36,7 ± 1,89
1 2 Agreement (%) Kappa Standard error Z
TAI (rt) 0.718 ± 0.026 0.741 ± 0.0219 15.56 0.137 0.0247 5.29
TSI (rt) 92.654 ± 1.465 92.579 ± 2.549 2.22 0.0115 0.0149 0.77
TAI (insp) 0.728 ± 0.023 0.755 ± 0.236 48.89 0.4687 0.0287 16.33
TSI (insp) 93.67 ± 1.809 94.33 ± 1.84 37.78 0.3585 0.0244 14.69
TAI (esp) 0.736 ± 0.024 0.732 ± 0.029 48.89 0.4626 0.0319 14.48
TSI (esp) 92.658 ± 1.605 92.571 ± 1.608 48.89 0.4736 0.0248 19.13
TAI (semi) 0.701 ± 0.02 0.715 ± 0.021 33.33 0.3056 0.0288 10.60
TSI (semi) 92.098 ± 1.316 94.277 ± 1.457 33.33 0.3151 0.0235 13.40
Open in a new tab

Table 3.

The TAI and TSI measurements in each method (ANOVA test)

Method 1
N = 45		 Method 2
N = 45		 Method 3
N = 45		 Method 4
N = 45		 P value
TAI1 0.7184 ± 0.1798 0.7288 ± 0.1597 0.736 ± 0.1668 0.7015 ± 0.1358 0.316
TSI1 92.6546 ± 9.8317 93.670 ± 12.1415 92.6586 ± 10.7699 92.0986 ± 8.8323 0.189
TAI2 0.7406 ± 0.1474 0.7551 ± 0.1584 0.7322 ± 0.1967 0.7155 ± 0.1446 0.136
TSI2 92.5797 ± 17.1057 94.3384 ± 12.3456 92.5717 ± 10.7921 94.2773 ± 9.7743 0.001
Open in a new tab

Discussion

This study aimed to evaluate the impact on TAI and TSI values of the breathing cycle, chest movement, and body position, to validate the reliability of QUS. This validation allows operators to monitor even hospitalized or uncompliant patients in the most appropriate position or respiratory phase to overcome the limitations due to several artifacts. As for all new technologies the reliability and reproducibility of QUS are not completely tested.

After a statistical analysis, the ultrasound quantification of liver fat is confirmed to be reliable even in normal-weight patients, even in overweight patients, and even obese patients. The evaluation of attenuation and scattering achieved a high agreement among the operators, especially during the peak inspiration and peak expiration, but also a satisfying agreement in the semi-sitting position. The study conducted by Sendur et al. reported that inspiration and expiration do not significantly influence the results in patients with BMI > 25 kg/m2, while a significant difference in the attenuation coefficient in the BMI < 25 kg/m2 subgroup was found. Our study confirmed the reliability among the respiratory phases in overweight patients (BMI > 25 kg/m2), but also among the operators. In addition, there was not a significant difference in patients with BMI < 25 kg/m2 among the operators, among different respiratory phases. Also, in the BMI < 25 kg/m2 subgroup, there was a stronger agreement at peak inspiration and expiration, in spite of quiet breath. Concerning the different methods evaluated in our study, TAI measurement did not show any statistically significant difference among the respiratory phases, while TSI did, due to the higher variability in the quiet breath. This was also probably due to a higher sensitivity of the method to the thorax movement.

Affordability, portability, and wide availability are some of the many advantages of ultrasound in clinical practice, in comparison with other imaging techniques. Therefore, US tools could be efficiently used to diagnose and follow-up liver steatosis.

Comparing our data with the reference standard reported by Sendur et al., in our dataset there was neither underestimation nor overestimation of steatosis grade attributed to the patients among different methods [34, 46].

Anyway in this study, we focused on demonstrating the reliability of TAI and TSI measurements inter-operators in a stratified population composed of normal, overweight, and obese patients.

The main limitation of our study is the unavailability of an MRI-PDFF to compare the results. To overcome this limit we introduced a control group of normal-weight patients not affected by liver steatosis. TAI and TSI measurements, in fact, gradually increased from the normal-weight group to obese group.

The importance of detecting liver steatosis is already assessed as it affects 90% of obese patients [47].

Because the grade of injury can range from fat infiltration to cirrhosis, early therapy must be set [48, 49] and monitored to lose weight, rather than muscle mass [50–52].

Nowadays, the most effective treatments are represented by bariatric surgery in young patients [53], to avoid the development of metabolic syndrome and liver failure. It should be underlined, the high risk of liver steatosis also after liver transplants [54, 55], so QUS may represent a safe and efficient tool to monitor the results of bariatric surgery or the health of the liver graft.

Even if bariatric surgery might be a challenging surgical procedure and it might be considered an invasive treatment, especially for young people, the advent of minimally invasive surgery has changed surgical scenarios allowing faster recovery, lower blood loss, and lower risk of major complications [56–64].

Thanks to the low risk of complications, several studies are now introducing a combined treatment with both bariatric surgery before liver transplantation, as NAFLD is a metabolic condition that may persist in damaging the graft [65–67].

The combined treatment can be helpful in adult or elderly patients, even if a consensual physical performance is needed and further studies are needed to standardize the procedure [68, 69].

In these groups of patients who have undergone bariatric surgery or liver transplantation, the importance of follow-up to monitor liver fat is outstanding and there is the need to quantify the percentage of liver fat to avoid sub-optimal treatments [70].

In addition, as liver fibrosis can benefit weight loss, bariatric surgery is starting to be considered also in compensated patients [71].

Future investigation will focus on the implementation and validation of the shear wave parameter to evaluate and monitor liver fibrosis.

Future studies will concern the evaluation of attenuation and scattering in a prospective cohort of patients undergoing bariatric surgery and weight loss.

Acknowledgements

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the University of Molise (Prot.n.19/2023).

Funding

Open access funding provided by Università degli Studi del Molise within the CRUI-CARE Agreement. The authors have not disclosed any funding.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

All patients enrolled signed the informed consent.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Luca Brunese and Gianfranco Vallone contributed equally to this work.

References

  • 1.An L, Wirth U, Koch D, et al. The role of gut-derived lipopolysaccharides and the intestinal barrier in fatty liver diseases. J Gastrointest Surg. 2022;26(3):671–683. doi: 10.1007/s11605-021-05188-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Huang XJ, Yin M, Zhou BQ, Tan XY, Xia YQ, Qin CX. Impact renaming non-alcoholic fatty liver disease to metabolic associated fatty liver disease in prevalence, characteristics and risk factors. World J Hepatol. 2023;15(8):985–1000. doi: 10.4254/wjh.v15.i8.985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Song Y, Zhao F. Bibliometric analysis of metabolic surgery for type 2 diabetes: current status and future prospects. Updates Surg. 2022;74(2):697–707. doi: 10.1007/s13304-021-01201-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bellini MI, Urciuoli I, Del Gaudio G, Polti G, Iannetti G, Gangitano E, Lori E, Lubrano C, Cantisani V, Sorrenti S, D'Andrea V. Nonalcoholic fatty liver disease and diabetes. World J Diabetes. 2022;13(9):668–682. doi: 10.4239/wjd.v13.i9.668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Faenza M, Benincasa G, Docimo L, Nicoletti GF, Napoli C. Clinical epigenetics and restoring of metabolic health in severely obese patients undergoing batriatric and metabolic surgery. Updates Surg. 2022;74(2):431–438. doi: 10.1007/s13304-021-01162-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Csermely A, Mantovani A, Morieri ML, et al. Association between different modalities of insulin administration and metabolic dysfunction-associated fatty liver disease in adults with type 1 diabetes mellitus. Diabetes Metab. 2023 doi: 10.1016/j.diabet.2023.101477. [DOI] [PubMed] [Google Scholar]
  • 7.Ibrahim MK, Wilechansky RM, Challa PK, et al. The empirical dietary inflammatory pattern score and the risk of nonalcoholic fatty liver disease and cirrhosis. Hepatol Commun. 2023;7(10):e0263. doi: 10.1097/HC9.0000000000000263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Fernandes ESM, de Mello FPT, Pimentel LS, et al. Combined liver transplantation with sleeve gastrectomy: a pioneer case series from Brazil. Updates Surg. 2022;74(1):81–86. doi: 10.1007/s13304-021-01230-0. [DOI] [PubMed] [Google Scholar]
  • 9.Fukushima M, Miyaaki H, Sasaki R, et al. Most cases of cryptogenic cirrhosis may be nonobese nonalcoholic steatohepatitis—risk factors of liver steatosis after liver transplantation for cryptogenic cirrhosis: a retrospective study. Intern Med. 2023;62(10):1415–1423. doi: 10.2169/internalmedicine.0514-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lai Q, Ghinolfi D, Avolio AW, Manzia TM, Mennini G, Melandro F, Frongillo F, Pellicciaro M, Larghi Laureiro Z, Aglietti R, Franco A, Quaranta C, Tisone G, Agnes S, Rossi M, de Simone P. Proposal and validation of a liver graft discard score for liver transplantation from deceased donors: a multicenter Italian study. Updates Surg. 2022;74(2):491–500. doi: 10.1007/s13304-022-01262-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Adani GL, Baccarani U, Sainz-Barriga M, et al. The role of hepatic biopsy to detect macrovacuolar steatosis during liver procurement. Transplant Proc. 2006;38(5):1404–1406. doi: 10.1016/j.transproceed.2006.02.111. [DOI] [PubMed] [Google Scholar]
  • 12.Choudhuri G, Shah S, Kulkarni A, et al. Non-alcoholic steatohepatitis in Asians: current perspectives and future directions. Cureus. 2023;15(8):e42852. doi: 10.7759/cureus.42852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Renzulli M, Brandi N, Argalia G, et al. Morphological, dynamic and functional characteristics of liver pseudolesions and benign lesions. Radiol Med. 2022;127(2):129–144. doi: 10.1007/s11547-022-01449-w. [DOI] [PubMed] [Google Scholar]
  • 14.Zalcman M, Barth RA, Rubesova E. Real-time ultrasound-derived fat fraction in pediatric population: feasibility validation with MR-PDFF. Pediatr Radiol. 2023 doi: 10.1007/s00247-023-05752-0. [DOI] [PubMed] [Google Scholar]
  • 15.Qi YM, Xiao EH. Advances in application of novel magnetic resonance imaging technologies in liver disease diagnosis. World J Gastroenterol. 2023;29(28):4384–4396. doi: 10.3748/wjg.v29.i28.4384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Hyodo R, Takehara Y, Naganawa S. 4D flow MRI in the portal venous system: imaging and analysis methods, and clinical applications. Radiol Med. 2022;127(11):1181–1198. doi: 10.1007/s11547-022-01553-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.D'Onofrio M, de Sio I, Mirk P, et al. SIUMB recommendations for focal pancreatic lesions. J Ultrasound. 2020;23(4):599–606. doi: 10.1007/s40477-020-00522-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Dichtel LE, Tabari A, Mercaldo ND, et al. CT texture analysis in nonalcoholic fatty liver disease (NAFLD) J Clin Exp Hepatol. 2023;13(5):760–766. doi: 10.1016/j.jceh.2023.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Jang W, Song JS. Non-invasive imaging methods to evaluate non-alcoholic fatty liver disease with fat quantification: a review. Diagnostics (Basel) 2023;13(11):1852. doi: 10.3390/diagnostics13111852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gu J, Liu S, Du S, et al. Diagnostic value of MRI-PDFF for hepatic steatosis in patients with non-alcoholic fatty liver disease: a meta-analysis. Eur Radiol. 2019;29(7):3564–3573. doi: 10.1007/s00330-019-06072-4. [DOI] [PubMed] [Google Scholar]
  • 21.Permutt Z, Le TA, Peterson MR, et al. Correlation between liver histology and novel magnetic resonance imaging in adult patients with non-alcoholic fatty liver disease—MRI accurately quantifies hepatic steatosis in NAFLD. Aliment Pharmacol Ther. 2012;36(1):22–29. doi: 10.1111/j.1365-2036.2012.05121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Huang DQ, Loomba R. Editorial: will MRI-PDFF become the new standard for steatosis assessment in NAFLD? Aliment Pharmacol Ther. 2023;58(3):366–367. doi: 10.1111/apt.17619. [DOI] [PubMed] [Google Scholar]
  • 23.Faccioli N, Santi E, Foti G, D'Onofrio M. Cost-effectiveness analysis of including contrast-enhanced ultrasound in management of pancreatic cystic neoplasms. Radiol Med. 2022;127(4):349–359. doi: 10.1007/s11547-022-01459-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med. 2022;127(1):39–56. doi: 10.1007/s11547-021-01423-y. [DOI] [PubMed] [Google Scholar]
  • 25.Rocca A, Brunese MC, Santone A, et al. Early diagnosis of liver metastases from colorectal cancer through CT radiomics and formal methods: a pilot study. J Clin Med. 2021;11(1):31. doi: 10.3390/jcm11010031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zerunian M, Pucciarelli F, Caruso D, et al. Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. Radiol Med. 2022;127(10):1098–1105. doi: 10.1007/s11547-022-01539-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sim KC, Kim MJ, Cho Y, et al. Radiomics analysis of magnetic resonance proton density fat fraction for the diagnosis of hepatic steatosis in patients with suspected non-alcoholic fatty liver disease. J Korean Med Sci. 2022;37(49):e339. doi: 10.3346/jkms.2022.37.e339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wang FH, Zheng HL, Li JT, et al. Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. Radiol Med. 2022;127(10):1085–1097. doi: 10.1007/s11547-022-01549-7. [DOI] [PubMed] [Google Scholar]
  • 29.Kim BK, Bernstein N, Huang DQ, et al. Clinical and histologic factors associated with discordance between steatosis grade derived from histology vs. MRI-PDFF in NAFLD. Aliment Pharmacol Ther. 2023;58(2):229–237. doi: 10.1111/apt.17564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Argalia G, Ventura C, Tosi N, et al. Comparison of point shear wave elastography and transient elastography in the evaluation of patients with NAFLD. Radiol Med. 2022;127(5):571–576. doi: 10.1007/s11547-022-01475-8. [DOI] [PubMed] [Google Scholar]
  • 31.Ferraioli G, Soares Monteiro LB. Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol. 2019;25(40):6053–6062. doi: 10.3748/wjg.v25.i40.6053). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.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(1):13–29. doi: 10.3348/kjr.2021.0112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.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(33):e29708. doi: 10.1097/MD.0000000000029708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zeng KY, Bao WY, Wang YH, Liao M, Yang J, Huang JY, Lu Q. Non-invasive evaluation of liver steatosis with imaging modalities: new techniques and applications. World J Gastroenterol. 2023;29(17):2534–2550. doi: 10.3748/wjg.v29.i17.2534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.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. doi: 10.1016/j.ultrasmedbio.2020.11.009. [DOI] [PubMed] [Google Scholar]
  • 36.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(5):W168–W177. doi: 10.2214/AJR.16.16726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.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(12):6499–6507. doi: 10.1007/s00330-019-06272-y. [DOI] [PubMed] [Google Scholar]
  • 38.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(11):2223–2232. doi: 10.1016/j.ultrasmedbio.2018.06.017. [DOI] [PubMed] [Google Scholar]
  • 39.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(10):821–828. doi: 10.1111/hepr.13179. [DOI] [PubMed] [Google Scholar]
  • 40.Bozic D, Podrug K, Mikolasevic I, Grgurevic I. Ultrasound methods for the assessment of liver steatosis: a critical appraisal. Diagnostics (Basel) 2022;12:2287. doi: 10.3390/diagnostics12102287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Şendur HN, Cerit MN, Ibrahimkhanli N, Şendur AB, Özhan Oktar S. Interobserver variability in ultrasound-based liver fat quantification. J Ultrasound Med. 2022 doi: 10.1002/jum.16048. [DOI] [PubMed] [Google Scholar]
  • 42.Yun MH, Seo YS, Kang HS, Lee KG, Kim JH, An H, Yim HJ, Keum B, Jeen YT, Lee HS, Chun HJ, Um SH, Kim CD, Ryu HS. The effect of the respiratory cycle on liver stiffness values as measured by transient elastography. J Viral Hepat. 2011;18(9):631–636. doi: 10.1111/j.1365-2893.2010.01376.x. [DOI] [PubMed] [Google Scholar]
  • 43.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(3):495–506. doi: 10.1148/radiol.210736. [DOI] [PubMed] [Google Scholar]
  • 44.Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46. doi: 10.1177/001316446002000104. [DOI] [Google Scholar]
  • 45.Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi: 10.2307/2529310. [DOI] [PubMed] [Google Scholar]
  • 46.Şendur HN, Özdemir Kalkan D, Cerit MN, Kalkan G, Şendur AB, Özhan OS. Hepatic fat quantification with novel ultrasound based techniques: a diagnostic performance study using magnetic resonance imaging proton density fat fraction as reference standard. Can Assoc Radiol J. 2023;74:362–369. doi: 10.1177/08465371221123696. [DOI] [PubMed] [Google Scholar]
  • 47.Milić S, Lulić D, Štimac D. Non-alcoholic fatty liver disease and obesity: biochemical, metabolic and clinical presentations. World J Gastroenterol. 2014;20(28):9330–9337. doi: 10.3748/wjg.v20.i28.9330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Voglino C, Badalucco S, Tirone A, et al. Follow-up after bariatric surgery: is it time to tailor it? Analysis of early predictive factors of 3-year weight loss predictors of unsuccess in bariatric patients. Updates Surg. 2022;74(4):1389–1398. doi: 10.1007/s13304-022-01314-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Agyapong G, Dashti F, Banini BA. Nonalcoholic liver disease: epidemiology, risk factors, natural history, and management strategies. Ann N Y Acad Sci. 2023;1526(1):16–29. doi: 10.1111/nyas.15012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Salaffi F, Carotti M, Di Matteo A, et al. Ultrasound and magnetic resonance imaging as diagnostic tools for sarcopenia in immune-mediated rheumatic diseases (IMRDs) Radiol Med. 2022;127(11):1277–1291. doi: 10.1007/s11547-022-01560-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Costa MSD, Pontes KSDS, Guedes MR, Barreto Silva MI, Klein MRST. Association of habitual coffee consumption with obesity, sarcopenia, bone mineral density and cardiovascular risk factors: a two-year follow-up study in kidney transplant recipients. Clin Nutr. 2023;42(10):1889–1900. doi: 10.1016/j.clnu.2023.08.004. [DOI] [PubMed] [Google Scholar]
  • 52.Tagliafico AS, Bignotti B, Torri L, Rossi F. Sarcopenia: how to measure, when and why. Radiol Med. 2022;127(3):228–237. doi: 10.1007/s11547-022-01450-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.de Angelis F, Boru CE, Iossa A, Perotta N, Campanile FC, Silecchia G. Long-term outcomes of sleeve gastrectomy as a revisional procedure after failed gastric band: a multicenter cross-matched cohort study. Updates Surg. 2022;74(2):709–713. doi: 10.1007/s13304-021-01182-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Elhelw O, Ragavan S, Majeed W, et al. The impact of bariatric surgery on liver enzymes in people with obesity: a 5-year observational study. Surgeon. 2023 doi: 10.1016/j.surge.2023.07.006. [DOI] [PubMed] [Google Scholar]
  • 55.Mak LY, Chan AC, Wong TC, et al. High prevalence of de novo metabolic dysfunction-associated fatty liver disease after liver transplantation and the role of controlled attenuation parameter. BMC Gastroenterol. 2023;23(1):307. doi: 10.1186/s12876-023-02940-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Thaher O, Driouch J, Hukauf M, Glatz T, Croner RS, Stroh C. Is development in bariatric surgery in Germany compatible with international standards? A review of 16 years of data. Updates Surg. 2022;74(5):1571–1579. doi: 10.1007/s13304-022-01349-8. [DOI] [PubMed] [Google Scholar]
  • 57.Si Y, Lin S, Li C, Shen J, Guan W, Liang H. Comparison of single versus double anastomosis bariatric metabolic surgery in obesity: a systematic review and meta-analysis. Obes Surg. 2023 doi: 10.1007/s11695-023-06805-x. [DOI] [PubMed] [Google Scholar]
  • 58.Polese L, Prevedello L, Belluzzi A, Giugliano E, Albanese A, Foletto M. Endoscopic sleeve gastroplasty: results from a single surgical bariatric centre. Updates Surg. 2022;74(6):1971–1975. doi: 10.1007/s13304-022-01385-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Dupont L, Alves A, Bui E, et al. Long-term weight loss outcomes after bariatric surgery: a propensity score study among patients with psychiatric disorders. Surg Endosc. 2023 doi: 10.1007/s00464-023-10343-z. [DOI] [PubMed] [Google Scholar]
  • 60.Sakran N, Raziel A, Hod K, Azaria B, Goitein D, Kaplan U. Early outcomes of one-anastomosis gastric bypass in the elderly population at high-volume bariatric centers. Updates Surg. 2023 doi: 10.1007/s13304-023-01697-z. [DOI] [PubMed] [Google Scholar]
  • 61.Kabir A, Izadi S, Mashayekhi F, Shokraee K, Rimaz S, Ansar H, Farsi F, Pazouki A. Effect of different bariatric surgery methods on metabolic syndrome in patients with severe obesity. Updates Surg. 2023 doi: 10.1007/s13304-023-01699-x. [DOI] [PubMed] [Google Scholar]
  • 62.Ceccarelli G, Rocca A, De Rosa M, et al. Minimally invasive robotic-assisted combined colorectal and liver excision surgery: feasibility, safety and surgical technique in a pilot series. Updates Surg. 2021;73(3):1015–1022. doi: 10.1007/s13304-021-01009-3. [DOI] [PubMed] [Google Scholar]
  • 63.Ceccarelli G, Costa G, De Rosa M, et al. Minimally invasive approach to gastric GISTs: analysis of a multicenter robotic and laparoscopic experience with literature review. Cancers (Basel) 2021;13(17):4351. doi: 10.3390/cancers13174351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Ceccarelli G, Pasculli A, Bugiantella W, et al. Minimally invasive laparoscopic and robot-assisted emergency treatment of strangulated giant hiatal hernias: report of five cases and literature review. World J Emerg Surg. 2020;15(1):37. doi: 10.1186/s13017-020-00316-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.de Barros F, Cardoso Faleiro Uba PH. Liver transplantation and bariatric surgery: a new surgical reality: a systematic review of the best time for bariatric surgery. Updates Surg. 2021;73(5):1615–1622. doi: 10.1007/s13304-021-01106-3. [DOI] [PubMed] [Google Scholar]
  • 66.López-López V, Martínez-Serrano MÁ, Ruiz-Manzanera JJ, Eshmuminov D, Ramirez P. Minimally invasive surgery and liver transplantation: is it a safe, feasible, and effective approach? Updates Surg. 2023;75(4):807–816. doi: 10.1007/s13304-023-01506-7. [DOI] [PubMed] [Google Scholar]
  • 67.Andrisani MC, Vespro V, Fusco S, et al. Interobserver variability in the evaluation of primary graft dysfunction after lung transplantation: impact of radiological training and analysis of discordant cases. Radiol Med. 2022;127(2):145–153. doi: 10.1007/s11547-021-01438-5. [DOI] [PubMed] [Google Scholar]
  • 68.Rocca A, Brunese MC, Cappuccio M, et al. Impact of physical activity on disability risk in elderly patients hospitalized for mild acute diverticulitis and diverticular bleeding undergone conservative management. Medicina (Kaunas) 2021;57(4):360. doi: 10.3390/medicina57040360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Komici K, Dello Iacono A, De Luca A, Perrotta F, Bencivenga L, Rengo G, Rocca A, Guerra G. Adiponectin and sarcopenia: a systematic review with meta-analysis. Front Endocrinol (Lausanne) 2021;15(12):576619. doi: 10.3389/fendo.2021.576619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Tavaglione F, De Vincentis A, Bruni V, et al. Accuracy of controlled attenuation parameter for assessing liver steatosis in individuals with morbid obesity before bariatric surgery. Liver Int. 2022;42(2):374–383. doi: 10.1111/liv.15127. [DOI] [PubMed] [Google Scholar]
  • 71.Miller A, Jalil S, Hussan H, et al. Apprising the choice of bariatric surgery in patients with compensated cirrhosis with morbid obesity: results of a national analysis. Updates Surg. 2021;73(5):1899–1908. doi: 10.1007/s13304-021-01142-z. [DOI] [PubMed] [Google Scholar]

Articles from La Radiologia Medica are provided here courtesy of Springer

RESOURCES